initial push

backend/database/database.js

@@ -0,0 +1,10 @@
+// database.js
+const { Sequelize } = require('sequelize');
+
+const sequelize = new Sequelize('myapp', 'root', 'root', {
+  host: 'localhost',
+  dialect: 'mysql',
+  logging: false,
+});
+
+module.exports = sequelize;

backend/models/Annotation.js

@@ -0,0 +1,40 @@
+const { DataTypes } = require('sequelize');
+const sequelize = require('../database/database.js');
+
+
+const Annotation = sequelize.define('Annotation', {
+  annotation_id: {
+    type: DataTypes.INTEGER,
+    primaryKey: true,
+    autoIncrement: true,
+  },
+  image_id: {
+    type: DataTypes.INTEGER,
+    allowNull: false,
+  },
+  x: {
+    type: DataTypes.FLOAT,
+    allowNull: false,
+  },
+  y: {
+    type: DataTypes.FLOAT,
+    allowNull: false,
+  },
+  height: {
+    type: DataTypes.FLOAT,
+    allowNull: false,
+  },
+  width: {
+    type: DataTypes.FLOAT,
+    allowNull: false,
+  },
+  Label: {
+    type: DataTypes.STRING,
+    allowNull: false,
+  },
+}, {
+  tableName: 'annotation',
+  timestamps: false,
+});
+
+module.exports = Annotation;

backend/models/Images.js

@@ -0,0 +1,35 @@
+
+const { DataTypes } = require('sequelize');
+const sequelize = require('../database/database.js');
+
+const Image = sequelize.define('Image', {
+  image_id: {
+    type: DataTypes.INTEGER,
+    primaryKey: true,
+    autoIncrement: true,
+  },
+  image_path: {
+    type: DataTypes.STRING,
+    allowNull: false,
+  },
+  project_id: {
+    type: DataTypes.INTEGER,
+    allowNull: false,
+  },
+  width: {
+    type: DataTypes.FLOAT,
+    allowNull: true,
+  },
+  height: {
+    type: DataTypes.FLOAT,
+    allowNull: true,
+  },
+
+}, {
+  tableName: 'image',
+  timestamps: false,
+});
+
+module.exports = Image;
+
+

backend/models/LabelStudioProject.js

@@ -0,0 +1,24 @@
+
+const { DataTypes } = require('sequelize');
+const sequelize = require('../database/database.js');
+
+const Label_studio_project = sequelize.define('LabelStudioProject', {
+  project_id: {
+    type: DataTypes.INTEGER,
+    primaryKey: true,
+    unique: true,
+    allowNull: false,
+  },
+  title:{
+    type: DataTypes.STRING,
+    allowNull: false,
+  }
+
+}, {
+  tableName: 'label_studio_project',
+  timestamps: false,
+});
+
+module.exports = Label_studio_project;
+
+

backend/models/TrainingProject.js

@@ -0,0 +1,38 @@
+const { DataTypes } = require('sequelize');
+const sequelize = require('../database/database.js');
+
+const Training_Project = sequelize.define('LabelStudioProject', {
+  project_id: {
+    type: DataTypes.INTEGER,
+    primaryKey: true,
+    unique: true,
+    allowNull: false,
+    autoIncrement: true,
+  },
+  title:{
+    type: DataTypes.STRING,
+    allowNull: false,
+  },
+  description: {
+    type: DataTypes.STRING,
+  },
+  classes: {
+    type: DataTypes.JSON,
+    allowNull: false,
+  },
+  project_image: {
+    type: DataTypes.BLOB,
+  },
+  project_image_type: {
+    type: DataTypes.STRING,
+    allowNull: true,
+  }
+
+}, {
+  tableName: 'training_project',
+  timestamps: false,
+});
+
+module.exports = Training_Project;
+
+

backend/models/TrainingProjectDetails.js

@@ -0,0 +1,33 @@
+const { DataTypes } = require('sequelize');
+const sequelize = require('../database/database.js');
+
+const TrainingProjectDetails = sequelize.define('TrainingProjectDetails', {
+  id: {
+    type: DataTypes.INTEGER,
+    primaryKey: true,
+    autoIncrement: true,
+    unique: true,
+  },
+  project_id: {
+    type: DataTypes.INTEGER,
+    allowNull: false,
+    unique: true,
+  },
+  annotation_projects: {
+    type: DataTypes.JSON,
+    allowNull: false,
+  },
+  class_map: {
+    type: DataTypes.JSON,
+    allowNull: true,
+  },
+  description: {
+    type: DataTypes.JSON,
+    allowNull: true,
+  }
+}, {
+  tableName: 'training_project_details',
+  timestamps: false,
+});
+
+module.exports = TrainingProjectDetails;

backend/models/index.js

@@ -0,0 +1,30 @@
+const LabelStudioProject = require('./LabelStudioProject.js');
+const Annotation = require('./Annotation.js');
+const Image = require('./Images.js');
+const sequelize = require('../database/database.js');
+const TrainingProjectDetails = require('./TrainingProjectDetails.js');
+const TrainingProject = require('./TrainingProject.js');
+const Training = require('./training.js');
+
+
+
+const Project = LabelStudioProject;
+const Img = Image;
+const Ann = Annotation;
+
+// Associations
+Project.hasMany(Img, { foreignKey: 'project_id' });
+Img.belongsTo(Project, { foreignKey: 'project_id' });
+
+Img.hasMany(Ann, { foreignKey: 'image_id' });
+Ann.belongsTo(Img, { foreignKey: 'image_id' });
+
+// TrainingProjectDetails <-> TrainingProject
+TrainingProjectDetails.belongsTo(TrainingProject, { foreignKey: 'project_id' });
+TrainingProject.hasOne(TrainingProjectDetails, { foreignKey: 'project_id' });
+
+// Training <-> TrainingProjectDetails
+Training.belongsTo(TrainingProjectDetails, { foreignKey: 'project_details_id' });
+TrainingProjectDetails.hasMany(Training, { foreignKey: 'project_details_id' });
+
+module.exports = { Project, Img, Ann, TrainingProjectDetails, TrainingProject, Training };

backend/models/training.js

@@ -0,0 +1,140 @@
+const { DataTypes } = require('sequelize');
+const sequelize = require('../database/database.js');
+
+const Training = sequelize.define('training', {
+    id: {
+        type: DataTypes.INTEGER,
+        autoIncrement: true,
+        unique: true,
+        primaryKey: true
+    },
+    exp_name: {
+        type: DataTypes.STRING(255)
+    },
+    max_epoch: {
+        type: DataTypes.INTEGER
+    },
+    depth: {
+        type: DataTypes.FLOAT
+    },
+    width: {
+        type: DataTypes.FLOAT
+    },
+    activation: {
+        type: DataTypes.STRING(255)
+    },
+    warmup_epochs: {
+        type: DataTypes.INTEGER
+    },
+    warmup_lr: {
+        type: DataTypes.FLOAT
+    },
+    basic_lr_per_img: {
+        type: DataTypes.FLOAT
+    },
+    scheduler: {
+        type: DataTypes.STRING(255)
+    },
+    no_aug_epochs: {
+        type: DataTypes.INTEGER
+    },
+    min_lr_ratio: {
+        type: DataTypes.FLOAT
+    },
+    ema: {
+        type: DataTypes.BOOLEAN
+    },
+    weight_decay: {
+        type: DataTypes.FLOAT
+    },
+    momentum: {
+        type: DataTypes.FLOAT
+    },
+    input_size: {
+        type: DataTypes.JSON
+    },
+    print_interval: {
+        type: DataTypes.INTEGER
+    },
+    eval_interval: {
+        type: DataTypes.INTEGER
+    },
+    save_history_ckpt: {
+        type: DataTypes.BOOLEAN
+    },
+    test_size: {
+        type: DataTypes.JSON
+    },
+    test_conf: {
+        type: DataTypes.FLOAT
+    },
+    nms_thre: {
+        type: DataTypes.FLOAT
+    },
+    multiscale_range: {
+        type: DataTypes.INTEGER
+    },
+    enable_mixup: {
+        type: DataTypes.BOOLEAN
+    },
+    mosaic_prob: {
+        type: DataTypes.FLOAT
+    },
+    mixup_prob: {
+        type: DataTypes.FLOAT
+    },
+    hsv_prob: {
+        type: DataTypes.FLOAT
+    },
+    flip_prob: {
+        type: DataTypes.FLOAT
+    },
+    degrees: {
+        type: DataTypes.FLOAT
+    },
+    mosaic_scale: {
+        type: DataTypes.JSON
+    },
+    mixup_scale: {
+        type: DataTypes.JSON
+    },
+    translate: {
+        type: DataTypes.FLOAT
+    },
+    shear: {
+        type: DataTypes.FLOAT
+    },
+    training_name: {
+        type: DataTypes.STRING(255)
+    },
+    project_details_id: {
+        type: DataTypes.INTEGER,
+        allowNull: false
+    },
+    seed: {
+        type: DataTypes.INTEGER
+    },
+    train: {
+        type: DataTypes.INTEGER
+    },
+    valid: {
+        type: DataTypes.INTEGER
+    },
+    test: {
+        type: DataTypes.INTEGER
+    },
+    selected_model: {
+        type: DataTypes.STRING(255)
+    },
+    transfer_learning: {
+        type: DataTypes.STRING(255)
+    },
+    model_upload: {
+        type: DataTypes.BLOB
+    }
+}, {
+    tableName: 'training',
+    timestamps: false
+});
+
+module.exports = Training;

backend/package.json

@@ -0,0 +1,20 @@
+{
+  "name": "backend",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "dependencies": {
+    "cors": "^2.8.5",
+    "express": "^5.1.0",
+    "multer": "^2.0.1",
+    "mysql": "^2.18.1",
+    "mysql2": "^3.14.1",
+    "sequelize": "^6.37.7"
+  }
+}

backend/project_23/40/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.33
+        self.width = 1.25
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_23/41/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.33
+        self.width = 1.25
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_23/42/exp.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.375
+        self.input_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.test_size = (416, 416)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_23/43/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.33
+        self.width = 1.25
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_23/46/exp.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.375
+        self.input_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.test_size = (416, 416)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_23/47/exp.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.375
+        self.input_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.test_size = (416, 416)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_23/48/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_23/49/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_23/50/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_23/54/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_35/37/exp_infer.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_37_train.json"
+        self.val_ann = "coco_project_37_valid.json"
+        self.test_ann = "coco_project_37_test.json"
+        self.num_classes = 1
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-Tiny.pth'
+        self.depth = 1.0
+        self.width = 1.0
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_35/37/testy.py

@@ -0,0 +1,292 @@
+import os
+import random
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+
+# Dynamically import BaseExp from fixed path
+import importlib.util
+import sys
+base_exp_path = '/home/kitraining/Yolox/YOLOX-main/yolox/exp/base_exp.py'
+spec = importlib.util.spec_from_file_location('base_exp', base_exp_path)
+base_exp = importlib.util.module_from_spec(spec)
+sys.modules['base_exp'] = base_exp
+spec.loader.exec_module(base_exp)
+BaseExp = base_exp.BaseExp
+
+__all__ = ["Exp", "check_exp_value"]
+
+class Exp(BaseExp):
+    def __init__(self):
+        super().__init__()
+        self.seed = None
+        self.data_dir = r'/home/kitraining/To_Annotate/'
+        self.train_ann = 'coco_project_37_train.json'
+        self.val_ann = 'coco_project_37_valid.json'
+        self.test_ann = 'coco_project_37_test.json'
+        self.num_classes = 80
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-l.pth'
+        self.depth = 1.00
+        self.width = 1.00
+        self.act = 'silu'
+        self.data_num_workers = 4
+        self.input_size = (640, 640)
+        self.multiscale_range = 5
+        self.mosaic_prob = 1.0
+        self.mixup_prob = 1.0
+        self.hsv_prob = 1.0
+        self.flip_prob = 0.5
+        self.degrees = (10.0, 10.0)
+        self.translate = (0.1, 0.1)
+        self.mosaic_scale = (0.1, 2)
+        self.enable_mixup = True
+        self.mixup_scale = (0.5, 1.5)
+        self.shear = (2.0, 2.0)
+        self.warmup_epochs = 5
+        self.max_epoch = 300
+        self.warmup_lr = 0
+        self.min_lr_ratio = 0.05
+        self.basic_lr_per_img = 0.01 / 64.0
+        self.scheduler = 'yoloxwarmcos'
+        self.no_aug_epochs = 15
+        self.ema = True
+        self.weight_decay = 5e-4
+        self.momentum = 0.9
+        self.print_interval = 10
+        self.eval_interval = 10
+        self.save_history_ckpt = True
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split('.')[0]
+        self.test_size = (640, 640)
+        self.test_conf = 0.01
+        self.nmsthre = 0.65
+        self.exp_name = 'custom_exp123'
+        self.max_epoch = 300
+        self.depth = 1
+        self.width = 1
+        self.activation = 'silu'
+        self.warmup_epochs = 5
+        self.warmup_lr = 0
+        self.scheduler = 'yoloxwarmcos'
+        self.no_aug_epochs = 15
+        self.min_lr_ratio = 0.05
+        self.ema = True
+        self.weight_decay = 0.0005
+        self.momentum = 0.9
+        self.input_size = (640, 640)
+        self.print_interval = 10
+        self.eval_interval = 10
+        self.save_history_ckpt = True
+        self.test_size = (640, 640)
+        self.test_conf = 0.01
+        self.multiscale_range = 5
+        self.enable_mixup = True
+        self.mosaic_prob = 1
+        self.mixup_prob = 1
+        self.hsv_prob = 1
+        self.flip_prob = 0.5
+        self.degrees = (10, 10)
+        self.mosaic_scale = (0.1, 2)
+        self.mixup_scale = (0.5, 1.5)
+        self.translate = (0.1, 0.1)
+        self.shear = (2, 2)
+        self.project_details_id = 37
+        self.selected_model = 'YOLOX-l'
+        self.transfer_learning = 'coco'
+
+    def get_model(self):
+        from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead
+        def init_yolo(M):
+            for m in M.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    m.eps = 1e-3
+                    m.momentum = 0.03
+        if getattr(self, 'model', None) is None:
+            in_channels = [256, 512, 1024]
+            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels, act=self.act)
+            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels, act=self.act)
+            self.model = YOLOX(backbone, head)
+        self.model.apply(init_yolo)
+        self.model.head.initialize_biases(1e-2)
+        self.model.train()
+        return self.model
+
+    def get_dataset(self, cache=False, cache_type='ram'):
+        from yolox.data import COCODataset, TrainTransform
+        return COCODataset(
+            data_dir=self.data_dir,
+            json_file=self.train_ann,
+            img_size=self.input_size,
+            preproc=TrainTransform(
+                max_labels=50,
+                flip_prob=self.flip_prob,
+                hsv_prob=self.hsv_prob
+            ),
+            cache=cache,
+            cache_type=cache_type,
+        )
+
+    def get_data_loader(self, batch_size, is_distributed, no_aug=False, cache_img=None):
+        from yolox.data import (
+            TrainTransform,
+            YoloBatchSampler,
+            DataLoader,
+            InfiniteSampler,
+            MosaicDetection,
+            worker_init_reset_seed,
+        )
+        from yolox.utils import wait_for_the_master
+        if self.dataset is None:
+            with wait_for_the_master():
+                assert cache_img is None, 'cache_img must be None if you did not create self.dataset before launch'
+                self.dataset = self.get_dataset(cache=False, cache_type=cache_img)
+        self.dataset = MosaicDetection(
+            dataset=self.dataset,
+            mosaic=not no_aug,
+            img_size=self.input_size,
+            preproc=TrainTransform(
+                max_labels=120,
+                flip_prob=self.flip_prob,
+                hsv_prob=self.hsv_prob),
+            degrees=self.degrees,
+            translate=self.translate,
+            mosaic_scale=self.mosaic_scale,
+            mixup_scale=self.mixup_scale,
+            shear=self.shear,
+            enable_mixup=self.enable_mixup,
+            mosaic_prob=self.mosaic_prob,
+            mixup_prob=self.mixup_prob,
+        )
+        if is_distributed:
+            batch_size = batch_size // dist.get_world_size()
+        sampler = InfiniteSampler(len(self.dataset), seed=self.seed if self.seed else 0)
+        batch_sampler = YoloBatchSampler(
+            sampler=sampler,
+            batch_size=batch_size,
+            drop_last=False,
+            mosaic=not no_aug,
+        )
+        dataloader_kwargs = {'num_workers': self.data_num_workers, 'pin_memory': True}
+        dataloader_kwargs['batch_sampler'] = batch_sampler
+        dataloader_kwargs['worker_init_fn'] = worker_init_reset_seed
+        train_loader = DataLoader(self.dataset, **dataloader_kwargs)
+        return train_loader
+
+    def random_resize(self, data_loader, epoch, rank, is_distributed):
+        tensor = torch.LongTensor(2).cuda()
+        if rank == 0:
+            size_factor = self.input_size[1] * 1.0 / self.input_size[0]
+            if not hasattr(self, 'random_size'):
+                min_size = int(self.input_size[0] / 32) - self.multiscale_range
+                max_size = int(self.input_size[0] / 32) + self.multiscale_range
+                self.random_size = (min_size, max_size)
+            size = random.randint(*self.random_size)
+            size = (int(32 * size), 32 * int(size * size_factor))
+            tensor[0] = size[0]
+            tensor[1] = size[1]
+        if is_distributed:
+            dist.barrier()
+            dist.broadcast(tensor, 0)
+        input_size = (tensor[0].item(), tensor[1].item())
+        return input_size
+
+    def preprocess(self, inputs, targets, tsize):
+        scale_y = tsize[0] / self.input_size[0]
+        scale_x = tsize[1] / self.input_size[1]
+        if scale_x != 1 or scale_y != 1:
+            inputs = nn.functional.interpolate(
+                inputs, size=tsize, mode='bilinear', align_corners=False
+            )
+            targets[..., 1::2] = targets[..., 1::2] * scale_x
+            targets[..., 2::2] = targets[..., 2::2] * scale_y
+        return inputs, targets
+
+    def get_optimizer(self, batch_size):
+        if 'optimizer' not in self.__dict__:
+            if self.warmup_epochs > 0:
+                lr = self.warmup_lr
+            else:
+                lr = self.basic_lr_per_img * batch_size
+            pg0, pg1, pg2 = [], [], []
+            for k, v in self.model.named_modules():
+                if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
+                    pg2.append(v.bias)
+                if isinstance(v, nn.BatchNorm2d) or 'bn' in k:
+                    pg0.append(v.weight)
+                elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
+                    pg1.append(v.weight)
+            optimizer = torch.optim.SGD(
+                pg0, lr=lr, momentum=self.momentum, nesterov=True
+            )
+            optimizer.add_param_group({'params': pg1, 'weight_decay': self.weight_decay})
+            optimizer.add_param_group({'params': pg2})
+            self.optimizer = optimizer
+        return self.optimizer
+
+    def get_lr_scheduler(self, lr, iters_per_epoch):
+        from yolox.utils import LRScheduler
+        scheduler = LRScheduler(
+            self.scheduler,
+            lr,
+            iters_per_epoch,
+            self.max_epoch,
+            warmup_epochs=self.warmup_epochs,
+            warmup_lr_start=self.warmup_lr,
+            no_aug_epochs=self.no_aug_epochs,
+            min_lr_ratio=self.min_lr_ratio,
+        )
+        return scheduler
+
+    def get_eval_dataset(self, **kwargs):
+        from yolox.data import COCODataset, ValTransform
+        testdev = kwargs.get('testdev', False)
+        legacy = kwargs.get('legacy', False)
+        return COCODataset(
+            data_dir=self.data_dir,
+            json_file=self.val_ann if not testdev else self.test_ann,
+            name='' if not testdev else 'test2017',
+            img_size=self.test_size,
+            preproc=ValTransform(legacy=legacy),
+        )
+
+    def get_eval_loader(self, batch_size, is_distributed, **kwargs):
+        valdataset = self.get_eval_dataset(**kwargs)
+        if is_distributed:
+            batch_size = batch_size // dist.get_world_size()
+            sampler = torch.utils.data.distributed.DistributedSampler(
+                valdataset, shuffle=False
+            )
+        else:
+            sampler = torch.utils.data.SequentialSampler(valdataset)
+        dataloader_kwargs = {
+            'num_workers': self.data_num_workers,
+            'pin_memory': True,
+            'sampler': sampler,
+        }
+        dataloader_kwargs['batch_size'] = batch_size
+        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)
+        return val_loader
+
+    def get_evaluator(self, batch_size, is_distributed, testdev=False, legacy=False):
+        from yolox.evaluators import COCOEvaluator
+        return COCOEvaluator(
+            dataloader=self.get_eval_loader(batch_size, is_distributed,
+                                            testdev=testdev, legacy=legacy),
+            img_size=self.test_size,
+            confthre=self.test_conf,
+            nmsthre=self.nmsthre,
+            num_classes=self.num_classes,
+            testdev=testdev,
+        )
+
+    def get_trainer(self, args):
+        from yolox.core import Trainer
+        trainer = Trainer(self, args)
+        return trainer
+
+    def eval(self, model, evaluator, is_distributed, half=False, return_outputs=False):
+        return evaluator.evaluate(model, is_distributed, half, return_outputs=return_outputs)
+
+def check_exp_value(exp):
+    h, w = exp.input_size
+    assert h % 32 == 0 and w % 32 == 0, 'input size must be multiples of 32'

backend/project_36/38/exp_infer.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_38_train.json"
+        self.val_ann = "coco_project_38_valid.json"
+        self.test_ann = "coco_project_38_test.json"
+        self.depth = 0.33
+        self.width = 0.50
+        self.num_classes = 1
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_36/38/testy.py

@@ -0,0 +1,292 @@
+import os
+import random
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+
+# Dynamically import BaseExp from fixed path
+import importlib.util
+import sys
+base_exp_path = '/home/kitraining/Yolox/YOLOX-main/yolox/exp/base_exp.py'
+spec = importlib.util.spec_from_file_location('base_exp', base_exp_path)
+base_exp = importlib.util.module_from_spec(spec)
+sys.modules['base_exp'] = base_exp
+spec.loader.exec_module(base_exp)
+BaseExp = base_exp.BaseExp
+
+__all__ = ["Exp", "check_exp_value"]
+
+class Exp(BaseExp):
+    def __init__(self):
+        super().__init__()
+        self.seed = None
+        self.data_dir = r'/home/kitraining/To_Annotate/'
+        self.train_ann = 'coco_project_38_train.json'
+        self.val_ann = 'coco_project_38_valid.json'
+        self.test_ann = 'coco_project_38_test.json'
+        self.num_classes = 80
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-s.pth'
+        self.depth = 1.00
+        self.width = 1.00
+        self.act = 'silu'
+        self.data_num_workers = 4
+        self.input_size = (640, 640)
+        self.multiscale_range = 5
+        self.mosaic_prob = 1.0
+        self.mixup_prob = 1.0
+        self.hsv_prob = 1.0
+        self.flip_prob = 0.5
+        self.degrees = (10.0, 10.0)
+        self.translate = (0.1, 0.1)
+        self.mosaic_scale = (0.1, 2)
+        self.enable_mixup = True
+        self.mixup_scale = (0.5, 1.5)
+        self.shear = (2.0, 2.0)
+        self.warmup_epochs = 5
+        self.max_epoch = 300
+        self.warmup_lr = 0
+        self.min_lr_ratio = 0.05
+        self.basic_lr_per_img = 0.01 / 64.0
+        self.scheduler = 'yoloxwarmcos'
+        self.no_aug_epochs = 15
+        self.ema = True
+        self.weight_decay = 5e-4
+        self.momentum = 0.9
+        self.print_interval = 10
+        self.eval_interval = 10
+        self.save_history_ckpt = True
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split('.')[0]
+        self.test_size = (640, 640)
+        self.test_conf = 0.01
+        self.nmsthre = 0.65
+        self.exp_name = 'lalalalal'
+        self.max_epoch = 300
+        self.depth = 1
+        self.width = 1
+        self.activation = 'silu'
+        self.warmup_epochs = 5
+        self.warmup_lr = 0
+        self.scheduler = 'yoloxwarmcos'
+        self.no_aug_epochs = 15
+        self.min_lr_ratio = 0.05
+        self.ema = True
+        self.weight_decay = 0.0005
+        self.momentum = 0.9
+        self.input_size = (640, 640)
+        self.print_interval = 10
+        self.eval_interval = 10
+        self.save_history_ckpt = True
+        self.test_size = (640, 640)
+        self.test_conf = 0.01
+        self.multiscale_range = 5
+        self.enable_mixup = True
+        self.mosaic_prob = 1
+        self.mixup_prob = 1
+        self.hsv_prob = 1
+        self.flip_prob = 0.5
+        self.degrees = (10, 10)
+        self.mosaic_scale = (0.1, 2)
+        self.mixup_scale = (0.5, 1.5)
+        self.translate = (0.1, 0.1)
+        self.shear = (2, 2)
+        self.project_details_id = 38
+        self.selected_model = 'YOLOX-s'
+        self.transfer_learning = 'coco'
+
+    def get_model(self):
+        from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead
+        def init_yolo(M):
+            for m in M.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    m.eps = 1e-3
+                    m.momentum = 0.03
+        if getattr(self, 'model', None) is None:
+            in_channels = [256, 512, 1024]
+            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels, act=self.act)
+            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels, act=self.act)
+            self.model = YOLOX(backbone, head)
+        self.model.apply(init_yolo)
+        self.model.head.initialize_biases(1e-2)
+        self.model.train()
+        return self.model
+
+    def get_dataset(self, cache=False, cache_type='ram'):
+        from yolox.data import COCODataset, TrainTransform
+        return COCODataset(
+            data_dir=self.data_dir,
+            json_file=self.train_ann,
+            img_size=self.input_size,
+            preproc=TrainTransform(
+                max_labels=50,
+                flip_prob=self.flip_prob,
+                hsv_prob=self.hsv_prob
+            ),
+            cache=cache,
+            cache_type=cache_type,
+        )
+
+    def get_data_loader(self, batch_size, is_distributed, no_aug=False, cache_img=None):
+        from yolox.data import (
+            TrainTransform,
+            YoloBatchSampler,
+            DataLoader,
+            InfiniteSampler,
+            MosaicDetection,
+            worker_init_reset_seed,
+        )
+        from yolox.utils import wait_for_the_master
+        if self.dataset is None:
+            with wait_for_the_master():
+                assert cache_img is None, 'cache_img must be None if you did not create self.dataset before launch'
+                self.dataset = self.get_dataset(cache=False, cache_type=cache_img)
+        self.dataset = MosaicDetection(
+            dataset=self.dataset,
+            mosaic=not no_aug,
+            img_size=self.input_size,
+            preproc=TrainTransform(
+                max_labels=120,
+                flip_prob=self.flip_prob,
+                hsv_prob=self.hsv_prob),
+            degrees=self.degrees,
+            translate=self.translate,
+            mosaic_scale=self.mosaic_scale,
+            mixup_scale=self.mixup_scale,
+            shear=self.shear,
+            enable_mixup=self.enable_mixup,
+            mosaic_prob=self.mosaic_prob,
+            mixup_prob=self.mixup_prob,
+        )
+        if is_distributed:
+            batch_size = batch_size // dist.get_world_size()
+        sampler = InfiniteSampler(len(self.dataset), seed=self.seed if self.seed else 0)
+        batch_sampler = YoloBatchSampler(
+            sampler=sampler,
+            batch_size=batch_size,
+            drop_last=False,
+            mosaic=not no_aug,
+        )
+        dataloader_kwargs = {'num_workers': self.data_num_workers, 'pin_memory': True}
+        dataloader_kwargs['batch_sampler'] = batch_sampler
+        dataloader_kwargs['worker_init_fn'] = worker_init_reset_seed
+        train_loader = DataLoader(self.dataset, **dataloader_kwargs)
+        return train_loader
+
+    def random_resize(self, data_loader, epoch, rank, is_distributed):
+        tensor = torch.LongTensor(2).cuda()
+        if rank == 0:
+            size_factor = self.input_size[1] * 1.0 / self.input_size[0]
+            if not hasattr(self, 'random_size'):
+                min_size = int(self.input_size[0] / 32) - self.multiscale_range
+                max_size = int(self.input_size[0] / 32) + self.multiscale_range
+                self.random_size = (min_size, max_size)
+            size = random.randint(*self.random_size)
+            size = (int(32 * size), 32 * int(size * size_factor))
+            tensor[0] = size[0]
+            tensor[1] = size[1]
+        if is_distributed:
+            dist.barrier()
+            dist.broadcast(tensor, 0)
+        input_size = (tensor[0].item(), tensor[1].item())
+        return input_size
+
+    def preprocess(self, inputs, targets, tsize):
+        scale_y = tsize[0] / self.input_size[0]
+        scale_x = tsize[1] / self.input_size[1]
+        if scale_x != 1 or scale_y != 1:
+            inputs = nn.functional.interpolate(
+                inputs, size=tsize, mode='bilinear', align_corners=False
+            )
+            targets[..., 1::2] = targets[..., 1::2] * scale_x
+            targets[..., 2::2] = targets[..., 2::2] * scale_y
+        return inputs, targets
+
+    def get_optimizer(self, batch_size):
+        if 'optimizer' not in self.__dict__:
+            if self.warmup_epochs > 0:
+                lr = self.warmup_lr
+            else:
+                lr = self.basic_lr_per_img * batch_size
+            pg0, pg1, pg2 = [], [], []
+            for k, v in self.model.named_modules():
+                if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
+                    pg2.append(v.bias)
+                if isinstance(v, nn.BatchNorm2d) or 'bn' in k:
+                    pg0.append(v.weight)
+                elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
+                    pg1.append(v.weight)
+            optimizer = torch.optim.SGD(
+                pg0, lr=lr, momentum=self.momentum, nesterov=True
+            )
+            optimizer.add_param_group({'params': pg1, 'weight_decay': self.weight_decay})
+            optimizer.add_param_group({'params': pg2})
+            self.optimizer = optimizer
+        return self.optimizer
+
+    def get_lr_scheduler(self, lr, iters_per_epoch):
+        from yolox.utils import LRScheduler
+        scheduler = LRScheduler(
+            self.scheduler,
+            lr,
+            iters_per_epoch,
+            self.max_epoch,
+            warmup_epochs=self.warmup_epochs,
+            warmup_lr_start=self.warmup_lr,
+            no_aug_epochs=self.no_aug_epochs,
+            min_lr_ratio=self.min_lr_ratio,
+        )
+        return scheduler
+
+    def get_eval_dataset(self, **kwargs):
+        from yolox.data import COCODataset, ValTransform
+        testdev = kwargs.get('testdev', False)
+        legacy = kwargs.get('legacy', False)
+        return COCODataset(
+            data_dir=self.data_dir,
+            json_file=self.val_ann if not testdev else self.test_ann,
+            name='' if not testdev else 'test2017',
+            img_size=self.test_size,
+            preproc=ValTransform(legacy=legacy),
+        )
+
+    def get_eval_loader(self, batch_size, is_distributed, **kwargs):
+        valdataset = self.get_eval_dataset(**kwargs)
+        if is_distributed:
+            batch_size = batch_size // dist.get_world_size()
+            sampler = torch.utils.data.distributed.DistributedSampler(
+                valdataset, shuffle=False
+            )
+        else:
+            sampler = torch.utils.data.SequentialSampler(valdataset)
+        dataloader_kwargs = {
+            'num_workers': self.data_num_workers,
+            'pin_memory': True,
+            'sampler': sampler,
+        }
+        dataloader_kwargs['batch_size'] = batch_size
+        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)
+        return val_loader
+
+    def get_evaluator(self, batch_size, is_distributed, testdev=False, legacy=False):
+        from yolox.evaluators import COCOEvaluator
+        return COCOEvaluator(
+            dataloader=self.get_eval_loader(batch_size, is_distributed,
+                                            testdev=testdev, legacy=legacy),
+            img_size=self.test_size,
+            confthre=self.test_conf,
+            nmsthre=self.nmsthre,
+            num_classes=self.num_classes,
+            testdev=testdev,
+        )
+
+    def get_trainer(self, args):
+        from yolox.core import Trainer
+        trainer = Trainer(self, args)
+        return trainer
+
+    def eval(self, model, evaluator, is_distributed, half=False, return_outputs=False):
+        return evaluator.evaluate(model, is_distributed, half, return_outputs=return_outputs)
+
+def check_exp_value(exp):
+    h, w = exp.input_size
+    assert h % 32 == 0 and w % 32 == 0, 'input size must be multiples of 32'

backend/project_37/39/exp_infer.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_39_train.json"
+        self.val_ann = "coco_project_39_valid.json"
+        self.test_ann = "coco_project_39_test.json"
+        self.num_classes = 80
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-Tiny.pth'
+        self.depth = 0.33
+        self.width = 0.375
+        self.input_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.test_size = (416, 416)
+        self.enable_mixup = False
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+

backend/project_38/40/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.33
+        self.width = 1.25
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_38/40/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_40_train.json"
+        self.val_ann = "coco_project_40_valid.json"
+        self.test_ann = "coco_project_40_test.json"
+        self.num_classes = 80
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-x.pth'
+        self.depth = 1.33
+        self.width = 1.25
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_38/40/testy.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.33
+        self.width = 1.25
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_40/41/exp.py

@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.33
+        self.width = 1.25
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_40/41/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_41_train.json"
+        self.val_ann = "coco_project_41_valid.json"
+        self.test_ann = "coco_project_41_test.json"
+        self.num_classes = 1
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-l.pth'
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_41/42/exp.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_42_train.json"
+        self.val_ann = "coco_project_42_valid.json"
+        self.test_ann = "coco_project_42_test.json"
+        self.depth = 0.33
+        self.width = 0.375
+        self.input_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.test_size = (416, 416)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_41/42/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_42_train.json"
+        self.val_ann = "coco_project_42_valid.json"
+        self.test_ann = "coco_project_42_test.json"
+        self.num_classes = 4
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-s.pth'
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_43/43/exp.py

@@ -0,0 +1,19 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_43_train.json"
+        self.val_ann = "coco_project_43_valid.json"
+        self.test_ann = "coco_project_43_test.json"
+        self.depth = 1.33
+        self.width = 1.25
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_43/43/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_43_train.json"
+        self.val_ann = "coco_project_43_valid.json"
+        self.test_ann = "coco_project_43_test.json"
+        self.num_classes = 1
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-Tiny.pth'
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_44/44/exp.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_48_train.json"
+        self.val_ann = "coco_project_48_valid.json"
+        self.test_ann = "coco_project_48_test.json"
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/tiny_persondetector/best_ckpt.pth'
+        self.num_classes = 4
+        self.depth = 0.33
+        self.width = 0.375
+        self.ingput_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_44/44/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_44_train.json"
+        self.val_ann = "coco_project_44_valid.json"
+        self.test_ann = "coco_project_44_test.json"
+        self.num_classes = 2
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-x.pth'
+        self.depth = 1.33
+        self.width = 1.25
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_46/46/exp.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_46_train.json"
+        self.val_ann = "coco_project_46_valid.json"
+        self.test_ann = "coco_project_46_test.json"
+        self.num_classes = 4
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-Tiny.pth'
+        self.depth = 0.33
+        self.width = 0.375
+        self.input_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.test_size = (416, 416)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = True

backend/project_46/46/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_46_train.json"
+        self.val_ann = "coco_project_46_valid.json"
+        self.test_ann = "coco_project_46_test.json"
+        self.num_classes = 80
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-Tiny.pth'
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_47/47/exp.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_47_train.json"
+        self.val_ann = "coco_project_47_valid.json"
+        self.test_ann = "coco_project_47_test.json"
+        self.num_classes = 2
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-Tiny.pth'
+        self.depth = 0.33
+        self.width = 0.375
+        self.input_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.test_size = (416, 416)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = True

backend/project_47/47/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_47_train.json"
+        self.val_ann = "coco_project_47_valid.json"
+        self.test_ann = "coco_project_47_test.json"
+        self.num_classes = 4
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-x.pth'
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_48/48/exp.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_48_train.json"
+        self.val_ann = "coco_project_48_valid.json"
+        self.test_ann = "coco_project_48_test.json"
+        self.num_classes = 2
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-s.pth'
+        self.depth = 0.33
+        self.width = 0.50
+        self.act = "relu"
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

backend/project_48/48/exp_infer.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_48_train.json"
+        self.val_ann = "coco_project_48_valid.json"
+        self.test_ann = "coco_project_48_test.json"
+        self.num_classes = 4
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_49/49/exp.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_49_train.json"
+        self.val_ann = "coco_project_49_valid.json"
+        self.test_ann = "coco_project_49_test.json"
+        self.num_classes = 2
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX_s.pth'
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = True
+
+
+# --------------  training config --------------------- #
+        self.warmup_epochs = 5
+        self.max_epoch = 100
+        self.act = "silu"

backend/project_49/49/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_49_train.json"
+        self.val_ann = "coco_project_49_valid.json"
+        self.test_ann = "coco_project_49_test.json"
+        self.num_classes = 4
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-Tiny.pth'
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_50/50/camera_inference.py

@@ -0,0 +1 @@
+python tools/demo.py video -f /home/kitraining/coco_tool/backend/project_50/50/exp.py -c ./YOLOX_outputs/exp/best_ckpt.pth --path /home/kitraining/Videos/test_1.mkv --conf 0.25 --nms 0.45 --tsize 640 --save_result --device gpu

backend/project_50/50/exp.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_50_train.json"
+        self.val_ann = "coco_project_50_valid.json"
+        self.test_ann = "coco_project_50_test.json"
+        self.num_classes = 2
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX_s.pth'
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+
+        # --------------  training config --------------------- #
+        self.warmup_epochs = 15           # More warmup
+        self.max_epoch = 250             # more epochs
+        self.act = "silu"                #Activation function
+                
+        # Thresholds 
+        self.test_conf = 0.01            # Low to catch more the second class
+        self.nmsthre = 0.7               
+        
+        # Data Augmentation intens to improve generalization
+        self.enable_mixup = True
+        self.mixup_prob = 0.9            # mixup
+        self.mosaic_prob = 0.9           # mosaico
+        self.degrees = 30.0              # Rotation
+        self.translate = 0.4             # Translation
+        self.scale = (0.2, 2.0)          # Scaling
+        self.shear = 10.0                # Shear
+        self.flip_prob = 0.8
+        self.hsv_prob = 1.0
+        
+        # Learning rate 
+        self.basic_lr_per_img = 0.001 / 64.0  # Lower LR to avoid divergence
+        self.scheduler = "yoloxwarmcos" 
+        
+        # Loss weights
+        self.cls_loss_weight = 8.0       # More weight to the classification loss
+        self.obj_loss_weight = 1.0
+        self.reg_loss_weight = 0.5
+        
+        # Input size bigger for better detection of small objects like babys 
+        self.input_size = (832, 832)  
+        self.test_size = (832, 832)
+        
+        # Batch size
+        self.batch_size = 5              # Reduce if you have memory issues

backend/project_50/50/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_50_train.json"
+        self.val_ann = "coco_project_50_valid.json"
+        self.test_ann = "coco_project_50_test.json"
+        self.num_classes = 2
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-s.pth'
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_53/53/exp.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_53_train.json"
+        self.val_ann = "coco_project_53_valid.json"
+        self.test_ann = "coco_project_53_test.json"
+        self.num_classes = 3
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/YOLOX_outputs/exp_Topview_4/best_ckpt.pth'
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+    
+
+        # --------------  training config --------------------- #
+        self.warmup_epochs = 15           # More warmup
+        self.max_epoch = 250             # more epochs
+        self.act = "silu"                #Activation function
+                
+        # Thresholds 
+        self.test_conf = 0.01            # Low to catch more the second class
+        self.nmsthre = 0.7               
+        
+        # Data Augmentation intens to improve generalization
+        self.enable_mixup = True
+        self.mixup_prob = 0.9            # mixup
+        self.mosaic_prob = 0.9           # mosaico
+        self.degrees = 30.0              # Rotation
+        self.translate = 0.4             # Translation
+        self.scale = (0.2, 2.0)          # Scaling
+        self.shear = 10.0                # Shear
+        self.flip_prob = 0.8
+        self.hsv_prob = 1.0
+        
+        # Learning rate 
+        self.basic_lr_per_img = 0.001 / 64.0  # Lower LR to avoid divergence
+        self.scheduler = "yoloxwarmcos" 
+        
+        # Loss weights
+        self.cls_loss_weight = 8.0       # More weight to the classification loss
+        self.obj_loss_weight = 1.0
+        self.reg_loss_weight = 0.5
+        
+        # Input size bigger for better detection of small objects like babys 
+        self.input_size = (832, 832)  
+        self.test_size = (832, 832)
+        
+        # Batch size
+        self.batch_size = 5              # Reduce if you have memory issues

backend/project_53/53/exp_infer.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_53_train.json"
+        self.val_ann = "coco_project_53_valid.json"
+        self.test_ann = "coco_project_53_test.json"
+        self.num_classes = 3
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/project_54/54/exp.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_54_train.json"
+        self.val_ann = "coco_project_54_valid.json"
+        self.test_ann = "coco_project_54_test.json"
+        self.num_classes = 3
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX_s.pth'
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+
+
+        # --------------  training config --------------------- #
+        
+        self.use_focal_loss = True         # Focal Loss for better class imbalance handling
+        self.focal_loss_alpha = 0.25
+        self.focal_loss_gamma = 1.5
+
+        self.warmup_epochs = 20           # More warmup    
+        self.max_epoch = 150              # More epochs for better convergence 
+        self.act = "silu"                 # Activation function
+        self.no_aug_epochs = 30           # No augmentation for last epochs to stabilize training
+
+        self.class_weights = [1.0, 1.0, 1.0] # Weights for each class to handle imbalance
+                
+        # Thresholds 
+        self.test_conf = 0.15            # Low to catch more the second class
+        self.nmsthre = 0.5               # IoU threshold for NMS         
+
+        # Data Augmentation intens to improve generalization
+        self.enable_mixup = True
+        self.mixup_prob = 0.7            # mixup
+        self.mosaic_prob = 0.8           # mosaico
+        self.degrees = 20.0              # Rotation
+        self.translate = 0.2             # Translation
+        self.scale = (0.5, 1.5)          # Scaling
+        self.shear = 5.0                 # Shear
+        self.flip_prob = 0.8
+        self.hsv_prob = 1.0
+        
+        # Learning rate 
+        self.basic_lr_per_img = 0.001 / 64.0  # Lower LR to avoid divergence
+        self.scheduler = "yoloxwarmcos" 
+        self.min_lr_ratio = 0.01
+        
+        # Loss weights
+        self.cls_loss_weight = 8.0       # More weight to the classification loss
+        self.obj_loss_weight = 1.0
+        self.reg_loss_weight = 1.0
+        
+        # Input size bigger for better detection of small objects like babys 
+        self.input_size = (832, 832)  
+        self.test_size = (832, 832)
+        
+        # Batch size
+        self.batch_size = 5              # Reduce if you have memory issues

backend/project_54/54/exp_infer.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.data_dir = "/home/kitraining/To_Annotate/"
+        self.train_ann = "coco_project_54_train.json"
+        self.val_ann = "coco_project_54_valid.json"
+        self.test_ann = "coco_project_54_test.json"
+        self.num_classes = 2
+        self.pretrained_ckpt = r'/home/kitraining/Yolox/YOLOX-main/pretrained/YOLOX-s.pth'
+        self.depth = 1
+        self.width = 1
+        self.input_size = (640, 640)
+        self.mosaic_scale = (0.1, 2)
+        self.random_size = (10, 20)
+        self.test_size = (640, 640)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

backend/routes/api.js

@@ -0,0 +1,496 @@
+const express = require('express');
+const multer = require('multer');
+const upload = multer();
+const TrainingProject = require('../models/TrainingProject.js');
+const LabelStudioProject = require('../models/LabelStudioProject.js')
+const { seedLabelStudio, updateStatus } = require('../services/seed-label-studio.js');
+const fs = require('fs');
+const path = require('path');
+const {generateTrainingJson} = require('../services/generate-json-yolox.js')
+
+
+const router = express.Router();
+
+// Ensure JSON bodies are parsed for all routes
+router.use(express.json());
+
+router.get('/seed', async (req, res) => {
+  const result = await seedLabelStudio();
+  res.json(result);
+});
+
+
+
+// Trigger generate-json-yolox.js
+
+router.post('/generate-yolox-json', async (req, res) => {
+  const { project_id } = req.body;
+  if (!project_id) {
+    return res.status(400).json({ message: 'Missing project_id in request body' });
+  }
+  try {
+    // Generate COCO JSONs
+    // Find all TrainingProjectDetails for this project
+    const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+    const detailsRows = await TrainingProjectDetails.findAll({ where: { project_id } });
+    if (!detailsRows || detailsRows.length === 0) {
+      return res.status(404).json({ message: 'No TrainingProjectDetails found for project ' + project_id });
+    }
+    // For each details row, generate coco.jsons and exp.py in projectfolder/project_details_id
+    const Training = require('../models/training.js');
+    const { saveYoloxExp } = require('../services/generate-yolox-exp.js');
+    const TrainingProject = require('../models/TrainingProject.js');
+    const trainingProject = await TrainingProject.findByPk(project_id);
+    const projectName = trainingProject.name ? trainingProject.name.replace(/\s+/g, '_') : `project_${project_id}`;
+    for (const details of detailsRows) {
+      const detailsId = details.id;
+      await generateTrainingJson(detailsId);
+      const trainings = await Training.findAll({ where: { project_details_id: detailsId } });
+      if (trainings.length === 0) continue;
+      // For each training, save exp.py in projectfolder/project_details_id
+      const outDir = path.join(__dirname, '..', projectName, String(detailsId));
+      if (!fs.existsSync(outDir)) fs.mkdirSync(outDir, { recursive: true });
+      for (const training of trainings) {
+        const expFilePath = path.join(outDir, 'exp.py');
+        await saveYoloxExp(training.id, expFilePath);
+      }
+    }
+
+    // Find all trainings for this project
+    // ...existing code...
+    res.json({ message: 'YOLOX JSON and exp.py generated for project ' + project_id });
+  } catch (err) {
+    console.error('Error generating YOLOX JSON:', err);
+    res.status(500).json({ message: 'Failed to generate YOLOX JSON', error: err.message });
+  }
+});
+
+// Start YOLOX training
+const { spawn } = require('child_process');
+router.post('/start-yolox-training', async (req, res) => {
+  try {
+    const { project_id, training_id } = req.body;
+    // Get project name
+    const trainingProject = await TrainingProject.findByPk(project_id);
+    const projectName = trainingProject.name ? trainingProject.name.replace(/\s+/g, '_') : `project_${project_id}`;
+    // Look up training row by id or project_details_id
+    const Training = require('../models/training.js');
+    let trainingRow = await Training.findByPk(training_id);
+    if (!trainingRow) {
+      trainingRow = await Training.findOne({ where: { project_details_id: training_id } });
+    }
+    if (!trainingRow) {
+      return res.status(404).json({ error: `Training row not found for id or project_details_id ${training_id}` });
+    }
+    const project_details_id = trainingRow.project_details_id;
+    // Use the generated exp.py from the correct project folder
+    const outDir = path.join(__dirname, '..', projectName, String(project_details_id));
+    const yoloxMainDir = '/home/kitraining/Yolox/YOLOX-main';
+    const expSrc = path.join(outDir, 'exp.py');
+    if (!fs.existsSync(expSrc)) {
+      return res.status(500).json({ error: `exp.py not found at ${expSrc}` });
+    }
+    // Source venv and run YOLOX training in YOLOX-main folder
+    const yoloxVenv = '/home/kitraining/Yolox/yolox_venv/bin/activate';
+    // Determine model argument based on selected_model and transfer_learning
+    let modelArg = '';
+    let cmd = '';
+    if (
+      trainingRow.transfer_learning &&
+      typeof trainingRow.transfer_learning === 'string' &&
+      trainingRow.transfer_learning.toLowerCase() === 'coco'
+    ) {
+      // If transfer_learning is 'coco', add -o and modelArg
+      modelArg = ` -c /home/kitraining/Yolox/YOLOX-main/pretrained/${trainingRow.selected_model}`;
+      cmd = `bash -c 'source ${yoloxVenv} && python tools/train.py -f ${expSrc} -d 1 -b 8 --fp16 -o ${modelArg}.pth --cache'`;
+    } else if (
+      trainingRow.selected_model &&
+      trainingRow.selected_model.toLowerCase() === 'coco' &&
+      (!trainingRow.transfer_learning || trainingRow.transfer_learning === false)
+    ) {
+      // If selected_model is 'coco' and not transfer_learning, add modelArg only
+      modelArg = ` -c /pretrained/${trainingRow.selected_model}`;
+      cmd = `bash -c 'source ${yoloxVenv} && python tools/train.py -f ${expSrc} -d 1 -b 8 --fp16 -o ${modelArg}.pth --cache'`;
+    } else {
+      // Default: no modelArg
+      cmd = `bash -c 'source ${yoloxVenv} && python tools/train.py -f ${expSrc} -d 1 -b 8 --fp16' --cache`;
+    }
+    console.log(cmd)
+    const child = spawn(cmd, { shell: true, cwd: yoloxMainDir });
+    child.stdout.pipe(process.stdout);
+    child.stderr.pipe(process.stderr);
+
+    res.json({ message: 'Training started' });
+  } catch (err) {
+    res.status(500).json({ error: 'Failed to start training', details: err.message });
+  }
+});
+
+// Get YOLOX training log
+router.get('/training-log', async (req, res) => {
+  try {
+    const { project_id, training_id } = req.query;
+    const trainingProject = await TrainingProject.findByPk(project_id);
+    const projectName = trainingProject.name ? trainingProject.name.replace(/\s+/g, '_') : `project_${project_id}`;
+    const outDir = path.join(__dirname, '..', projectName, String(training_id));
+    const logPath = path.join(outDir, 'training.log');
+    if (!fs.existsSync(logPath)) {
+      return res.status(404).json({ error: 'Log not found' });
+    }
+    const logData = fs.readFileSync(logPath, 'utf8');
+    res.json({ log: logData });
+  } catch (err) {
+    res.status(500).json({ error: 'Failed to fetch log', details: err.message });
+  }
+});
+
+router.post('/training-projects', upload.single('project_image'), async (req, res) => {
+  try {
+    const { title, description } = req.body;
+    const classes = JSON.parse(req.body.classes);
+    const project_image = req.file ? req.file.buffer : null;
+    const project_image_type = req.file ? req.file.mimetype : null;
+    await TrainingProject.create({
+      title,
+      description,
+      classes,
+      project_image,
+      project_image_type
+    });
+    res.json({ message: 'Project created!' });
+  } catch (error) {
+    console.error('Error creating project:', error);
+    res.status(500).json({ message: 'Failed to create project', error: error.message });
+  }
+});
+
+router.get('/training-projects', async (req, res) => {
+  try {
+    const projects = await TrainingProject.findAll();
+    // Convert BLOB to base64 data URL for each project
+    const serialized = projects.map(project => {
+      const plain = project.get({ plain: true });
+      if (plain.project_image) {
+        const base64 = Buffer.from(plain.project_image).toString('base64');
+        const mimeType = plain.project_image_type || 'image/png';
+        plain.project_image = `data:${mimeType};base64,${base64}`;
+      }
+      return plain;
+    });
+    res.json(serialized);
+  } catch (error) {
+    res.status(500).json({ message: 'Failed to fetch projects', error: error.message });
+  }
+});
+
+router.get('/update-status', async (req, res) => {
+  res.json(updateStatus)
+})
+
+router.get('/label-studio-projects', async (req, res) => {
+  try {
+    const LabelStudioProject = require('../models/LabelStudioProject.js');
+    const Image = require('../models/Images.js');
+    const Annotation = require('../models/Annotation.js');
+    const labelStudioProjects = await LabelStudioProject.findAll();
+    const projectsWithCounts = await Promise.all(labelStudioProjects.map(async project => {
+      const plain = project.get({ plain: true });
+      // Get all images for this project
+      const images = await Image.findAll({ where: { project_id: plain.project_id } });
+      let annotationCounts = {};
+      if (images.length > 0) {
+        const imageIds = images.map(img => img.image_id);
+        // Get all annotations for these images
+        const annotations = await Annotation.findAll({ where: { image_id: imageIds } });
+        // Count by label
+        for (const ann of annotations) {
+          const label = ann.Label;
+          annotationCounts[label] = (annotationCounts[label] || 0) + 1;
+        }
+      }
+      plain.annotationCounts = annotationCounts;
+      return plain;
+    }));
+    res.json(projectsWithCounts);
+  } catch (error) {
+    res.status(500).json({ message: 'Failed to fetch projects', error: error.message });
+  }
+})
+
+
+// POST endpoint to create TrainingProjectDetails with all fields
+router.post('/training-project-details', async (req, res) => {
+  try {
+    const {
+      project_id,
+      annotation_projects,
+      class_map,
+      description
+    } = req.body;
+    if (!project_id || !annotation_projects) {
+      return res.status(400).json({ message: 'Missing required fields' });
+    }
+    const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+    const created = await TrainingProjectDetails.create({
+      project_id,
+      annotation_projects,
+      class_map: class_map || null,
+      description: description || null
+    });
+    res.json({ message: 'TrainingProjectDetails created', details: created });
+  } catch (error) {
+    res.status(500).json({ message: 'Failed to create TrainingProjectDetails', error: error.message });
+  }
+});
+
+// GET endpoint to fetch all TrainingProjectDetails
+router.get('/training-project-details', async (req, res) => {
+  try {
+    const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+    const details = await TrainingProjectDetails.findAll();
+    res.json(details);
+  } catch (error) {
+    res.status(500).json({ message: 'Failed to fetch TrainingProjectDetails', error: error.message });
+  }
+});
+
+// PUT endpoint to update class_map and description in TrainingProjectDetails
+router.put('/training-project-details', async (req, res) => {
+  try {
+    const { project_id, class_map, description } = req.body;
+    if (!project_id || !class_map || !description) {
+      return res.status(400).json({ message: 'Missing required fields' });
+    }
+    const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+    const details = await TrainingProjectDetails.findOne({ where: { project_id } });
+    if (!details) {
+      return res.status(404).json({ message: 'TrainingProjectDetails not found' });
+    }
+    details.class_map = class_map;
+    details.description = description;
+    await details.save();
+    res.json({ message: 'Class map and description updated', details });
+  } catch (error) {
+    res.status(500).json({ message: 'Failed to update class map or description', error: error.message });
+  }
+});
+
+// POST endpoint to receive YOLOX settings and save to DB (handles multipart/form-data)
+router.post('/yolox-settings', upload.any(), async (req, res) => {
+  try {
+    const settings = req.body;
+    // Debug: Log all received fields and types
+    console.log('--- YOLOX settings received ---');
+    console.log('settings:', settings);
+    if (req.files && req.files.length > 0) {
+      console.log('Files received:', req.files.map(f => ({ fieldname: f.fieldname, originalname: f.originalname, size: f.size })));
+    }
+    // Declare requiredFields once
+    const requiredFields = ['project_details_id', 'exp_name', 'max_epoch', 'depth', 'width', 'activation', 'train', 'valid', 'test', 'selected_model', 'transfer_learning'];
+    // Log types of required fields
+    requiredFields.forEach(field => {
+      console.log(`Field '${field}': value='${settings[field]}', type='${typeof settings[field]}'`);
+    });
+    // Map select_model to selected_model if present
+    if (settings && settings.select_model && !settings.selected_model) {
+      settings.selected_model = settings.select_model;
+      delete settings.select_model;
+    }
+    // Lookup project_details_id from project_id
+    if (!settings.project_id || isNaN(Number(settings.project_id))) {
+      throw new Error('Missing or invalid project_id in request. Cannot assign training to a project.');
+    }
+    const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+    let details = await TrainingProjectDetails.findOne({ where: { project_id: settings.project_id } });
+    if (!details) {
+      details = await TrainingProjectDetails.create({
+        project_id: settings.project_id,
+        annotation_projects: [],
+        class_map: null,
+        description: null
+      });
+    }
+    settings.project_details_id = details.id;
+    // Map 'act' from frontend to 'activation' for DB
+    if (settings.act !== undefined) {
+      settings.activation = settings.act;
+      delete settings.act;
+    }
+    // Type conversion for DB compatibility
+    [
+      'max_epoch', 'depth', 'width', 'warmup_epochs', 'warmup_lr', 'no_aug_epochs', 'min_lr_ratio', 'weight_decay', 'momentum', 'print_interval', 'eval_interval', 'test_conf', 'nmsthre', 'multiscale_range', 'degrees', 'translate', 'shear', 'train', 'valid', 'test'
+    ].forEach(f => {
+      if (settings[f] !== undefined) settings[f] = Number(settings[f]);
+    });
+    // Improved boolean conversion
+    ['ema', 'enable_mixup', 'save_history_ckpt'].forEach(f => {
+      if (settings[f] !== undefined) {
+        if (typeof settings[f] === 'string') {
+          settings[f] = settings[f].toLowerCase() === 'true';
+        } else {
+          settings[f] = Boolean(settings[f]);
+        }
+      }
+    });
+    // Improved array conversion
+    ['mosaic_scale', 'mixup_scale', 'scale'].forEach(f => {
+      if (settings[f] && typeof settings[f] === 'string') {
+        settings[f] = settings[f]
+          .split(',')
+          .map(s => Number(s.trim()))
+          .filter(n => !isNaN(n));
+      }
+    });
+    // Trim all string fields
+    Object.keys(settings).forEach(f => {
+      if (typeof settings[f] === 'string') settings[f] = settings[f].trim();
+    });
+    // Set default for transfer_learning if missing
+    if (settings.transfer_learning === undefined) settings.transfer_learning = false;
+    // Convert empty string seed to null
+    if ('seed' in settings && (settings.seed === '' || settings.seed === undefined)) {
+      settings.seed = null;
+    }
+    // Validate required fields for training table
+    for (const field of requiredFields) {
+      if (settings[field] === undefined || settings[field] === null || settings[field] === '') {
+        console.error('Missing required field:', field, 'Value:', settings[field]);
+        throw new Error('Missing required field: ' + field);
+      }
+    }
+    console.log('Received YOLOX settings:', settings);
+    // Handle uploaded model file (ckpt_upload)
+    if (req.files && req.files.length > 0) {
+      const ckptFile = req.files.find(f => f.fieldname === 'ckpt_upload');
+      if (ckptFile) {
+        const uploadDir = path.join(__dirname, '..', 'uploads');
+        if (!fs.existsSync(uploadDir)) fs.mkdirSync(uploadDir);
+        const filename = ckptFile.originalname || `uploaded_model_${settings.project_id}.pth`;
+        const filePath = path.join(uploadDir, filename);
+        fs.writeFileSync(filePath, ckptFile.buffer);
+        settings.model_upload = filePath;
+      }
+    }
+    // Save settings to DB only (no file)
+    const { pushYoloxExpToDb } = require('../services/push-yolox-exp.js');
+    const training = await pushYoloxExpToDb(settings);
+    res.json({ message: 'YOLOX settings saved to DB', training });
+  } catch (error) {
+    console.error('Error in /api/yolox-settings:', error.stack || error);
+    res.status(500).json({ message: 'Failed to save YOLOX settings', error: error.message });
+  }
+});
+
+// POST endpoint to receive binary model file and save to disk (not DB)
+router.post('/yolox-settings/upload', async (req, res) => {
+  try {
+    const projectId = req.query.project_id;
+    if (!projectId) return res.status(400).json({ message: 'Missing project_id in query' });
+    // Save file to disk
+    const uploadDir = path.join(__dirname, '..', 'uploads');
+    if (!fs.existsSync(uploadDir)) fs.mkdirSync(uploadDir);
+    const filename = req.headers['x-upload-filename'] || `uploaded_model_${projectId}.pth`;
+    const filePath = path.join(uploadDir, filename);
+    const chunks = [];
+    req.on('data', chunk => chunks.push(chunk));
+    req.on('end', async () => {
+      const buffer = Buffer.concat(chunks);
+      fs.writeFile(filePath, buffer, async err => {
+        if (err) {
+          console.error('Error saving file:', err);
+          return res.status(500).json({ message: 'Failed to save model file', error: err.message });
+        }
+        // Update latest training row for this project with file path
+        try {
+          const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+          const Training = require('../models/training.js');
+          // Find details row for this project
+          const details = await TrainingProjectDetails.findOne({ where: { project_id: projectId } });
+          if (!details) return res.status(404).json({ message: 'No TrainingProjectDetails found for project_id' });
+          // Find latest training for this details row
+          const training = await Training.findOne({ where: { project_details_id: details.id }, order: [['createdAt', 'DESC']] });
+          if (!training) return res.status(404).json({ message: 'No training found for project_id' });
+          // Save file path to model_upload field
+          training.model_upload = filePath;
+          await training.save();
+          res.json({ message: 'Model file uploaded and saved to disk', filename, trainingId: training.id });
+        } catch (dbErr) {
+          console.error('Error updating training with file path:', dbErr);
+          res.status(500).json({ message: 'File saved but failed to update training row', error: dbErr.message });
+        }
+      });
+    });
+  } catch (error) {
+    console.error('Error in /api/yolox-settings/upload:', error.stack || error);
+    res.status(500).json({ message: 'Failed to upload model file', error: error.message });
+  }
+});
+
+// GET endpoint to fetch all trainings (optionally filtered by project_id)
+router.get('/trainings', async (req, res) => {
+  try {
+    const project_id = req.query.project_id;
+    const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+    const Training = require('../models/training.js');
+    if (project_id) {
+      // Find all details rows for this project
+      const detailsRows = await TrainingProjectDetails.findAll({ where: { project_id } });
+      if (!detailsRows || detailsRows.length === 0) return res.json([]);
+      // Get all trainings linked to any details row for this project
+      const detailsIds = detailsRows.map(d => d.id);
+      const trainings = await Training.findAll({ where: { project_details_id: detailsIds } });
+      return res.json(trainings);
+    } else {
+      // Return all trainings if no project_id is specified
+      const trainings = await Training.findAll();
+      return res.json(trainings);
+    }
+  } catch (error) {
+    res.status(500).json({ message: 'Failed to fetch trainings', error: error.message });
+  }
+});
+
+// DELETE endpoint to remove a training by id
+router.delete('/trainings/:id', async (req, res) => {
+  try {
+    const Training = require('../models/training.js');
+    const id = req.params.id;
+    const deleted = await Training.destroy({ where: { id } });
+    if (deleted) {
+      res.json({ message: 'Training deleted' });
+    } else {
+      res.status(404).json({ message: 'Training not found' });
+    }
+  } catch (error) {
+    res.status(500).json({ message: 'Failed to delete training', error: error.message });
+  }
+});
+
+// DELETE endpoint to remove a training project and all related entries
+router.delete('/training-projects/:id', async (req, res) => {
+  try {
+    const projectId = req.params.id;
+    const TrainingProject = require('../models/TrainingProject.js');
+    const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+    const Training = require('../models/training.js');
+    // Find details row(s) for this project
+    const detailsRows = await TrainingProjectDetails.findAll({ where: { project_id: projectId } });
+    const detailsIds = detailsRows.map(d => d.id);
+    // Delete all trainings linked to these details
+    if (detailsIds.length > 0) {
+      await Training.destroy({ where: { project_details_id: detailsIds } });
+      await TrainingProjectDetails.destroy({ where: { project_id: projectId } });
+    }
+    // Delete the project itself
+    const deleted = await TrainingProject.destroy({ where: { project_id: projectId } });
+    if (deleted) {
+      res.json({ message: 'Training project and all related entries deleted' });
+    } else {
+      res.status(404).json({ message: 'Training project not found' });
+    }
+  } catch (error) {
+    res.status(500).json({ message: 'Failed to delete training project', error: error.message });
+  }
+});
+
+module.exports = router;

backend/server.js

@@ -0,0 +1,34 @@
+const express = require('express');
+const cors = require('cors');
+const path = require('path');
+const sequelize = require('./database/database');
+
+
+const app = express();
+app.use(express.json());
+const port = 3000;
+
+const apiRouter = require('./routes/api.js');
+app.use('/api', apiRouter);
+
+
+app.use(cors());
+app.use(express.json());
+app.use(express.static(path.join(__dirname, '..')));
+
+
+
+// Initialize DB and start server
+(async () => {
+  try {
+    await sequelize.authenticate();
+    console.log('DB connection established.');
+    await sequelize.sync(); // Only if you want Sequelize to ensure schema matches
+
+    app.listen(port, '0.0.0.0', () =>
+      console.log(`Server running at http://0.0.0.0:${port}`)
+    );
+  } catch (err) {
+    console.error('Failed to start:', err);
+  }
+})();

backend/services/fetch-labelstudio.js

@@ -0,0 +1,92 @@
+const API_URL = 'http://192.168.1.19:8080/api';
+const API_TOKEN = 'c1cef980b7c73004f4ee880a42839313b863869f';
+
+
+
+
+
+
+const fetch = require('node-fetch');
+
+async function fetchLableStudioProject(projectid) {
+    // 1. Trigger export
+    const exportUrl = `${API_URL}/projects/${projectid}/export?exportType=JSON_MIN`;
+    const headers = { Authorization: `Token ${API_TOKEN}` };
+    let res = await fetch(exportUrl, { headers });
+    if (!res.ok) {
+        let errorText = await res.text().catch(() => '');
+        console.error(`Failed to trigger export: ${res.status} ${res.statusText} - ${errorText}`);
+        throw new Error(`Failed to trigger export: ${res.status} ${res.statusText}`);
+    }
+    let data = await res.json();
+    // If data is an array, it's ready
+    if (Array.isArray(data)) return data;
+    // If not, poll for the export file
+    let fileUrl = data.download_url || data.url || null;
+    let tries = 0;
+    while (!fileUrl && tries < 20) {
+        await new Promise(r => setTimeout(r, 2000));
+        res = await fetch(exportUrl, { headers });
+        if (!res.ok) {
+            let errorText = await res.text().catch(() => '');
+            console.error(`Failed to poll export: ${res.status} ${res.statusText} - ${errorText}`);
+            throw new Error(`Failed to poll export: ${res.status} ${res.statusText}`);
+        }
+        data = await res.json();
+        fileUrl = data.download_url || data.url || null;
+        tries++;
+    }
+    if (!fileUrl) throw new Error('Label Studio export did not become ready');
+    // 2. Download the export file
+    res = await fetch(fileUrl.startsWith('http') ? fileUrl : `${API_URL.replace('/api','')}${fileUrl}`, { headers });
+    if (!res.ok) {
+        let errorText = await res.text().catch(() => '');
+        console.error(`Failed to download export: ${res.status} ${res.statusText} - ${errorText}`);
+        throw new Error(`Failed to download export: ${res.status} ${res.statusText}`);
+    }
+    return await res.json();
+}
+
+
+
+async function fetchProjectIdsAndTitles() {
+  try {
+    const response = await fetch(`${API_URL}/projects/`, {
+      headers: {
+        'Authorization': `Token ${API_TOKEN}`,
+        'Content-Type': 'application/json'
+      }
+    });
+
+    if (!response.ok) {
+      let errorText = await response.text().catch(() => '');
+      console.error(`Failed to fetch projects: ${response.status} ${response.statusText} - ${errorText}`);
+      throw new Error(`HTTP error! status: ${response.status}`);
+    }
+
+    const data = await response.json();
+
+    if (!data.results || !Array.isArray(data.results)) {
+      throw new Error('API response does not contain results array');
+    }
+
+    // Extract id and title from each project
+    const projects = data.results.map(project => ({
+      id: project.id,
+      title: project.title
+    }));
+    console.log(projects)
+    return projects;
+
+  } catch (error) {
+    console.error('Failed to fetch projects:', error);
+    return [];
+  }
+}
+
+module.exports = { fetchLableStudioProject, fetchProjectIdsAndTitles };
+
+
+
+//getLableStudioProject(20)
+//fetchProjectIdsAndTitles()

backend/services/generate-json-yolox.js

@@ -0,0 +1,176 @@
+const TrainingProject = require('../models/TrainingProject.js');
+const TrainingProjectDetails = require('../models/TrainingProjectDetails.js')
+const LabelStudioProject = require('../models/LabelStudioProject.js')
+const Annotation = require('../models/Annotation.js')
+const Images = require('../models/Images.js')
+const fs = require('fs');
+
+
+async function generateTrainingJson(trainingId){
+    // trainingId is now project_details_id
+    const trainingProjectDetails = await TrainingProjectDetails.findByPk(trainingId);
+    if (!trainingProjectDetails) throw new Error('No TrainingProjectDetails found for project_details_id ' + trainingId);
+    const detailsObj = trainingProjectDetails.get({ plain: true });
+    // Get parent project for name
+    const trainingProject = await TrainingProject.findByPk(detailsObj.project_id);
+    // Get split percentages (assume they are stored as train_percent, valid_percent, test_percent)
+    const trainPercent = detailsObj.train_percent || 85;
+    const validPercent = detailsObj.valid_percent || 10;
+    const testPercent = detailsObj.test_percent || 5;
+
+    let cocoImages = [];
+    let cocoAnnotations = [];
+    let cocoCategories = [];
+    let categoryMap = {};
+    let categoryId = 0;
+    let imageid = 0;
+    let annotationid = 0;
+
+    for (const cls of detailsObj.class_map) {
+        const asgMap = [];
+        const listAsg = cls[1];
+        for(const asg of listAsg){
+            asgMap.push ({ original: asg[0], mapped: asg[1] });
+            // Build category list and mapping
+            if (asg[1] && !(asg[1] in categoryMap)) {
+                categoryMap[asg[1]] = categoryId;
+                cocoCategories.push({ id: categoryId, name: asg[1], supercategory: '' });
+                categoryId++;
+            }
+        }
+        const images = await Images.findAll({ where: { project_id: cls[0] } });
+        for(const image of images){
+            imageid += 1;
+            let fileName = image.image_path;
+            if (fileName.includes('%20')) {
+                fileName = fileName.replace(/%20/g, ' ');
+            }
+            if (fileName && fileName.startsWith('/data/local-files/?d=')) {
+                fileName = fileName.replace('/data/local-files/?d=', '');
+                fileName = fileName.replace('/home/kitraining/home/kitraining/', '');
+            }
+            if (fileName && fileName.startsWith('home/kitraining/To_Annotate/')) {
+                fileName = fileName.replace('home/kitraining/To_Annotate/','');
+            }
+            // Get annotations for this image
+            const annotations = await Annotation.findAll({ where: { image_id: image.image_id } });
+            // Use image.width and image.height from DB (populated from original_width/original_height)
+            cocoImages.push({
+                id: imageid,
+                file_name: fileName,
+                width: image.width || 0,
+                height: image.height || 0
+            });
+            for (const annotation of annotations) {
+                // Translate class name using asgMap
+                let mappedClass = annotation.Label;
+                for (const mapEntry of asgMap) {
+                    if (annotation.Label === mapEntry.original) {
+                        mappedClass = mapEntry.mapped;
+                        break;
+                    }
+                }
+                // Only add annotation if mappedClass is valid
+                if (mappedClass && mappedClass in categoryMap) {
+                    annotationid += 1;
+                    let area = 0;
+                    if (annotation.width && annotation.height) {
+                        area = annotation.width * annotation.height;
+                    }
+                    cocoAnnotations.push({
+                        id: annotationid,
+                        image_id: imageid,
+                        category_id: categoryMap[mappedClass],
+                        bbox: [annotation.x, annotation.y, annotation.width, annotation.height],
+                        area: area,
+                        iscrowd: annotation.iscrowd || 0
+                    });
+                }
+            }
+        }
+    }
+
+    // Shuffle images for random split using seed
+    function seededRandom(seed) {
+        let x = Math.sin(seed++) * 10000;
+        return x - Math.floor(x);
+    }
+    function shuffle(array, seed) {
+        for (let i = array.length - 1; i > 0; i--) {
+            const j = Math.floor(seededRandom(seed + i) * (i + 1));
+            [array[i], array[j]] = [array[j], array[i]];
+        }
+    }
+    // Use seed from detailsObj if present, else default to 42
+    const splitSeed = detailsObj.seed !== undefined && detailsObj.seed !== null ? Number(detailsObj.seed) : 42;
+    shuffle(cocoImages, splitSeed);
+
+    // Split images
+    const totalImages = cocoImages.length;
+    const trainCount = Math.floor(totalImages * trainPercent / 100);
+    const validCount = Math.floor(totalImages * validPercent / 100);
+    const testCount = totalImages - trainCount - validCount;
+
+    const trainImages = cocoImages.slice(0, trainCount);
+    const validImages = cocoImages.slice(trainCount, trainCount + validCount);
+    const testImages = cocoImages.slice(trainCount + validCount);
+
+    // Helper to get image ids for each split
+    const trainImageIds = new Set(trainImages.map(img => img.id));
+    const validImageIds = new Set(validImages.map(img => img.id));
+    const testImageIds = new Set(testImages.map(img => img.id));
+
+    // Split annotations
+    const trainAnnotations = cocoAnnotations.filter(ann => trainImageIds.has(ann.image_id));
+    const validAnnotations = cocoAnnotations.filter(ann => validImageIds.has(ann.image_id));
+    const testAnnotations = cocoAnnotations.filter(ann => testImageIds.has(ann.image_id));
+
+    // Build final COCO JSONs with info section
+    const buildCocoJson = (images, annotations, categories) => ({
+        images,
+        annotations,
+        categories
+    });
+
+    // Build COCO JSONs with info section
+    const trainJson = buildCocoJson(trainImages, trainAnnotations, cocoCategories);
+    const validJson = buildCocoJson(validImages, validAnnotations, cocoCategories);
+    const testJson = buildCocoJson(testImages, testAnnotations, cocoCategories);
+
+    // Create output directory: projectname/trainingid/annotations
+    const projectName = trainingProject && trainingProject.name ? trainingProject.name.replace(/\s+/g, '_') : `project_${detailsObj.project_id}`;
+    const outDir = `${projectName}/${trainingId}`;
+    const annotationsDir = `/home/kitraining/To_Annotate/annotations`;
+    if (!fs.existsSync(annotationsDir)) {
+        fs.mkdirSync(annotationsDir, { recursive: true });
+    }
+
+    // Write to files in the annotations directory
+    const trainPath = `${annotationsDir}/coco_project_${trainingId}_train.json`;
+    const validPath = `${annotationsDir}/coco_project_${trainingId}_valid.json`;
+    const testPath = `${annotationsDir}/coco_project_${trainingId}_test.json`;
+    fs.writeFileSync(trainPath, JSON.stringify(trainJson, null, 2));
+    fs.writeFileSync(validPath, JSON.stringify(validJson, null, 2));
+    fs.writeFileSync(testPath, JSON.stringify(testJson, null, 2));
+    console.log(`COCO JSON splits written to ${annotationsDir} for trainingId ${trainingId}`);
+
+
+
+    // Also generate inference exp.py in the same output directory as exp.py (project folder in workspace)
+    const { generateYoloxInferenceExp } = require('./generate-yolox-exp');
+    const path = require('path');
+    const projectFolder = path.join(__dirname, '..', projectName, String(trainingId));
+    if (!fs.existsSync(projectFolder)) {
+        fs.mkdirSync(projectFolder, { recursive: true });
+    }
+    const inferenceExpPath = path.join(projectFolder, 'exp_infer.py');
+    generateYoloxInferenceExp(trainingId).then(expContent => {
+        fs.writeFileSync(inferenceExpPath, expContent);
+        console.log(`Inference exp.py written to ${inferenceExpPath}`);
+    }).catch(err => {
+        console.error('Failed to generate inference exp.py:', err);
+    });
+}
+
+
+module.exports = {generateTrainingJson};

backend/services/generate-yolox-exp.js

@@ -0,0 +1,135 @@
+const fs = require('fs');
+const path = require('path');
+const Training = require('../models/training.js');
+const TrainingProject = require('../models/TrainingProject.js');
+
+// Remove Python comments and legacy code
+const exp_names = [
+    'YOLOX-s',
+    'YOLOX-m',
+    'YOLOX-l',
+    'YOLOX-x', 
+    'YOLOX-Darknet53', //todo
+    'YOLOX-Nano',
+    'YOLOX-Tiny'
+]
+
+//TODO: Clean up generation of exp_names.py and remove second exp creation!!!
+
+
+// Refactored: Accept trainingId, fetch info from DB
+async function generateYoloxExp(trainingId) {
+    // Fetch training row from DB by project_details_id if not found by PK
+    let training = await Training.findByPk(trainingId);
+    if (!training) {
+        training = await Training.findOne({ where: { project_details_id: trainingId } });
+    }
+    if (!training) throw new Error('Training not found for trainingId or project_details_id: ' + trainingId);
+
+    // If transfer_learning is 'coco', just return the path to the default exp.py
+    if (training.transfer_learning === 'coco') {
+        const selectedModel = training.selected_model.toLowerCase().replace('-', '_');
+        const expSourcePath = `/home/kitraining/Yolox/YOLOX-main/exps/default/${selectedModel}.py`;
+        if (!fs.existsSync(expSourcePath)) {
+            throw new Error(`Default exp.py not found for model: ${selectedModel} at ${expSourcePath}`);
+        }
+        // Copy to project folder (e.g., /home/kitraining/coco_tool/backend/project_XX/YY/exp.py)
+        const projectDetailsId = training.project_details_id;
+        const projectFolder = path.resolve(__dirname, `../project_23/${projectDetailsId}`);
+        if (!fs.existsSync(projectFolder)) {
+            fs.mkdirSync(projectFolder, { recursive: true });
+        }
+        const expDestPath = path.join(projectFolder, 'exp.py');
+        fs.copyFileSync(expSourcePath, expDestPath);
+        return { type: 'default', expPath: expDestPath };
+    }
+
+    // If transfer_learning is 'sketch', generate a custom exp.py as before
+    if (training.transfer_learning === 'sketch') {
+        // ...existing custom exp.py generation logic here (copy from previous implementation)...
+        // For brevity, you can call generateYoloxInferenceExp or similar here, or inline the logic.
+        // Example:
+        const expContent = await generateYoloxInferenceExp(trainingId);
+        return { type: 'custom', expContent };
+    }
+
+    throw new Error('Unknown transfer_learning type: ' + training.transfer_learning);
+}
+
+async function saveYoloxExp(trainingId, outPath) {
+    const expResult = await generateYoloxExp(trainingId);
+    if (expResult.type === 'custom' && expResult.expContent) {
+        fs.writeFileSync(outPath, expResult.expContent);
+        return outPath;
+    } else if (expResult.type === 'default' && expResult.expPath) {
+        // Optionally copy the file if outPath is different
+        if (expResult.expPath !== outPath) {
+            fs.copyFileSync(expResult.expPath, outPath);
+        }
+        return outPath;
+    } else {
+        throw new Error('Unknown expResult type or missing content');
+    }
+}
+
+// Generate a second exp.py for inference, using the provided template and DB values
+async function generateYoloxInferenceExp(trainingId, options = {}) {
+    let training = await Training.findByPk(trainingId);
+    if (!training) {
+        training = await Training.findOne({ where: { project_details_id: trainingId } });
+    }
+    if (!training) throw new Error('Training not found for trainingId or project_details_id: ' + trainingId);
+    // Always use the trainingId (project_details_id) for annotation file names
+    const projectDetailsId = training.project_details_id;
+    const dataDir = options.data_dir || '/home/kitraining/To_Annotate/';
+    const trainAnn = options.train_ann || `coco_project_${trainingId}_train.json`;
+    const valAnn = options.val_ann || `coco_project_${trainingId}_valid.json`;
+    const testAnn = options.test_ann || `coco_project_${trainingId}_test.json`;
+    // Get num_classes from TrainingProject.classes JSON
+    let numClasses = 80;
+    try {
+        const trainingProject = await TrainingProject.findByPk(projectDetailsId);
+        if (trainingProject && trainingProject.classes) {
+            let classesArr = trainingProject.classes;
+            if (typeof classesArr === 'string') {
+                classesArr = JSON.parse(classesArr);
+            }
+            if (Array.isArray(classesArr)) {
+                numClasses = classesArr.filter(c => c !== null && c !== undefined && c !== '').length;
+            } else if (typeof classesArr === 'object' && classesArr !== null) {
+                numClasses = Object.keys(classesArr).filter(k => classesArr[k] !== null && classesArr[k] !== undefined && classesArr[k] !== '').length;
+            }
+        }
+    } catch (e) {
+        console.warn('Could not determine num_classes from TrainingProject.classes:', e);
+    }
+    const depth = options.depth || training.depth || 1.00;
+    const width = options.width || training.width || 1.00;
+    const inputSize = options.input_size || training.input_size || [640, 640];
+    const mosaicScale = options.mosaic_scale || training.mosaic_scale || [0.1, 2];
+    const randomSize = options.random_size || training.random_size || [10, 20];
+    const testSize = options.test_size || training.test_size || [640, 640];
+    const expName = options.exp_name || 'inference_exp';
+    const enableMixup = options.enable_mixup !== undefined ? options.enable_mixup : false;
+    let expContent = '';
+    expContent += `#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n    def __init__(self):\n        super(Exp, self).__init__()\n        self.data_dir = "${dataDir}"\n        self.train_ann = "${trainAnn}"\n        self.val_ann = "${valAnn}"\n        self.test_ann = "coco_project_${trainingId}_test.json"\n        self.num_classes = ${numClasses}\n`;
+    // Set pretrained_ckpt if transfer_learning is 'coco'
+    if (training.transfer_learning && typeof training.transfer_learning === 'string' && training.transfer_learning.toLowerCase() === 'coco') {
+        const yoloxBaseDir = '/home/kitraining/Yolox/YOLOX-main';
+        const selectedModel = training.selected_model ? training.selected_model.replace(/\.pth$/i, '') : '';
+        if (selectedModel) {
+            expContent += `        self.pretrained_ckpt = r'${yoloxBaseDir}/pretrained/${selectedModel}.pth'\n`;
+        }
+    }
+    expContent += `        self.depth = ${depth}\n        self.width = ${width}\n        self.input_size = (${Array.isArray(inputSize) ? inputSize.join(', ') : inputSize})\n        self.mosaic_scale = (${Array.isArray(mosaicScale) ? mosaicScale.join(', ') : mosaicScale})\n        self.random_size = (${Array.isArray(randomSize) ? randomSize.join(', ') : randomSize})\n        self.test_size = (${Array.isArray(testSize) ? testSize.join(', ') : testSize})\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]\n        self.enable_mixup = ${enableMixup ? 'True' : 'False'}\n`;
+    return expContent;
+}
+
+// Save inference exp.py to a custom path
+async function saveYoloxInferenceExp(trainingId, outPath, options = {}) {
+    const expContent = await generateYoloxInferenceExp(trainingId, options);
+    fs.writeFileSync(outPath, expContent);
+    return outPath;
+}
+
+module.exports = { generateYoloxExp, saveYoloxExp, generateYoloxInferenceExp, saveYoloxInferenceExp };

backend/services/push-yolox-exp.js

@@ -0,0 +1,48 @@
+const Training = require('../models/training.js');
+const fs = require('fs');
+const path = require('path');
+
+async function pushYoloxExpToDb(settings) {
+    // Normalize boolean and array fields for DB
+    const normalized = { ...settings };
+    // Map 'act' from frontend to 'activation' for DB
+    if (normalized.act !== undefined) {
+        normalized.activation = normalized.act;
+        delete normalized.act;
+    }
+    // Convert 'on'/'off' to boolean for save_history_ckpt
+    if (typeof normalized.save_history_ckpt === 'string') {
+        normalized.save_history_ckpt = normalized.save_history_ckpt === 'on' ? true : false;
+    }
+    // Convert comma-separated strings to arrays for input_size, test_size, mosaic_scale, mixup_scale
+    ['input_size', 'test_size', 'mosaic_scale', 'mixup_scale'].forEach(key => {
+        if (typeof normalized[key] === 'string') {
+            const arr = normalized[key].split(',').map(v => parseFloat(v.trim()));
+            normalized[key] = arr.length === 1 ? arr[0] : arr;
+        }
+    });
+    // Find TrainingProjectDetails for this project
+    const TrainingProjectDetails = require('../models/TrainingProjectDetails.js');
+    const details = await TrainingProjectDetails.findOne({ where: { project_id: normalized.project_id } });
+    if (!details) throw new Error('TrainingProjectDetails not found for project_id ' + normalized.project_id);
+    normalized.project_details_id = details.id;
+    // Create DB row
+    const training = await Training.create(normalized);
+    return training;
+}
+
+async function generateYoloxExpFromDb(trainingId) {
+    // Fetch training row from DB
+    const training = await Training.findByPk(trainingId);
+    if (!training) throw new Error('Training not found');
+    // Template for exp.py
+    const expTemplate = `#!/usr/bin/env python3\n# Copyright (c) Megvii Inc. All rights reserved.\n\nimport os\nimport random\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\n\nfrom .base_exp import BaseExp\n\n__all__ = [\"Exp\", \"check_exp_value\"]\n\nclass Exp(BaseExp):\n    def __init__(self):\n        super().__init__()\n\n        # ---------------- model config ---------------- #\n        self.num_classes = ${training.num_classes || 80}\n        self.depth = ${training.depth || 1.00}\n        self.width = ${training.width || 1.00}\n        self.act = \"${training.activation || training.act || 'silu'}\"\n\n        # ---------------- dataloader config ---------------- #\n        self.data_num_workers = ${training.data_num_workers || 4}\n        self.input_size = (${Array.isArray(training.input_size) ? training.input_size.join(', ') : '640, 640'})\n        self.multiscale_range = ${training.multiscale_range || 5}\n        self.data_dir = ${training.data_dir ? `\"${training.data_dir}\"` : 'None'}\n        self.train_ann = \"${training.train_ann || 'instances_train2017.json'}\"\n        self.val_ann = \"${training.val_ann || 'instances_val2017.json'}\"\n        self.test_ann = \"${training.test_ann || 'instances_test2017.json'}\"\n\n        # --------------- transform config ----------------- #\n        self.mosaic_prob = ${training.mosaic_prob !== undefined ? training.mosaic_prob : 1.0}\n        self.mixup_prob = ${training.mixup_prob !== undefined ? training.mixup_prob : 1.0}\n        self.hsv_prob = ${training.hsv_prob !== undefined ? training.hsv_prob : 1.0}\n        self.flip_prob = ${training.flip_prob !== undefined ? training.flip_prob : 0.5}\n        self.degrees = ${training.degrees !== undefined ? training.degrees : 10.0}\n        self.translate = ${training.translate !== undefined ? training.translate : 0.1}\n        self.mosaic_scale = (${Array.isArray(training.mosaic_scale) ? training.mosaic_scale.join(', ') : '0.1, 2'})\n        self.enable_mixup = ${training.enable_mixup !== undefined ? training.enable_mixup : true}\n        self.mixup_scale = (${Array.isArray(training.mixup_scale) ? training.mixup_scale.join(', ') : '0.5, 1.5'})\n        self.shear = ${training.shear !== undefined ? training.shear : 2.0}\n\n        # --------------  training config --------------------- #\n        self.warmup_epochs = ${training.warmup_epochs !== undefined ? training.warmup_epochs : 5}\n        self.max_epoch = ${training.max_epoch !== undefined ? training.max_epoch : 300}\n        self.warmup_lr = ${training.warmup_lr !== undefined ? training.warmup_lr : 0}\n        self.min_lr_ratio = ${training.min_lr_ratio !== undefined ? training.min_lr_ratio : 0.05}\n        self.basic_lr_per_img = ${training.basic_lr_per_img !== undefined ? training.basic_lr_per_img : 0.01 / 64.0}\n        self.scheduler = \"${training.scheduler || 'yoloxwarmcos'}\"\n        self.no_aug_epochs = ${training.no_aug_epochs !== undefined ? training.no_aug_epochs : 15}\n        self.ema = ${training.ema !== undefined ? training.ema : true}\n        self.weight_decay = ${training.weight_decay !== undefined ? training.weight_decay : 5e-4}\n        self.momentum = ${training.momentum !== undefined ? training.momentum : 0.9}\n        self.print_interval = ${training.print_interval !== undefined ? training.print_interval : 10}\n        self.eval_interval = ${training.eval_interval !== undefined ? training.eval_interval : 10}\n        self.save_history_ckpt = ${training.save_history_ckpt !== undefined ? training.save_history_ckpt : true}\n        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n\n        # -----------------  testing config ------------------ #\n        self.test_size = (${Array.isArray(training.test_size) ? training.test_size.join(', ') : '640, 640'})\n        self.test_conf = ${training.test_conf !== undefined ? training.test_conf : 0.01}\n        self.nmsthre = ${training.nmsthre !== undefined ? training.nmsthre : 0.65}\n\n    # ... rest of the template ...\n\ndef check_exp_value(exp: Exp):\n    h, w = exp.input_size\n    assert h % 32 == 0 and w % 32 == 0, \"input size must be multiples of 32\"\n`;
+    // Save to file in output directory
+    const outDir = path.join(__dirname, '../../', training.project_id ? `project_${training.project_id}/${trainingId}` : 'exp_files');
+    if (!fs.existsSync(outDir)) fs.mkdirSync(outDir, { recursive: true });
+    const filePath = path.join(outDir, 'exp.py');
+    fs.writeFileSync(filePath, expTemplate);
+    return filePath;
+}
+
+module.exports = { pushYoloxExpToDb, generateYoloxExpFromDb };

backend/services/seed-label-studio.js

@@ -0,0 +1,120 @@
+const sequelize = require('../database/database.js');
+const { Project, Img, Ann } = require('../models');
+const { fetchLableStudioProject, fetchProjectIdsAndTitles } = require('./fetch-labelstudio.js');
+
+const updateStatus = { running: false };
+
+async function seedLabelStudio() {
+  updateStatus.running = true;
+  console.log('Seeding started');
+  try {
+    await sequelize.sync();
+    const projects = await fetchProjectIdsAndTitles();
+
+    for (const project of projects) {
+      console.log(`Processing project ${project.id} (${project.title})`);
+
+      // Upsert project in DB
+      await Project.upsert({ project_id: project.id, title: project.title });
+
+      // Fetch project data (annotations array)
+      const data = await fetchLableStudioProject(project.id);
+      if (!Array.isArray(data) || data.length === 0) {
+        console.log(`No annotation data for project ${project.id}`);
+        continue;
+      }
+
+      // Remove old images and annotations for this project
+      const oldImages = await Img.findAll({ where: { project_id: project.id } });
+      const oldImageIds = oldImages.map(img => img.image_id);
+      if (oldImageIds.length > 0) {
+        await Ann.destroy({ where: { image_id: oldImageIds } });
+        await Img.destroy({ where: { project_id: project.id } });
+        console.log(`Deleted ${oldImageIds.length} old images and their annotations for project ${project.id}`);
+      }
+
+      // Prepare arrays
+      const imagesBulk = [];
+      const annsBulk = [];
+
+      for (const ann of data) {
+        // Extract width/height
+        let width = null;
+        let height = null;
+        if (Array.isArray(ann.label_rectangles) && ann.label_rectangles.length > 0) {
+          width = ann.label_rectangles[0].original_width;
+          height = ann.label_rectangles[0].original_height;
+        } else if (Array.isArray(ann.label) && ann.label.length > 0 && ann.label[0].original_width && ann.label[0].original_height) {
+          width = ann.label[0].original_width;
+          height = ann.label[0].original_height;
+        }
+
+        // Only push image and annotations if width and height are valid
+        if (width && height) {
+          imagesBulk.push({
+            project_id: project.id,
+            image_path: ann.image,
+            width,
+            height
+          });
+
+          // Handle multiple annotations per image
+          if (Array.isArray(ann.label_rectangles)) {
+            for (const ann_detail of ann.label_rectangles) {
+              annsBulk.push({
+                image_path: ann.image,
+                x: (ann_detail.x * width) / 100,
+                y: (ann_detail.y * height) / 100,
+                width: (ann_detail.width * width) / 100,
+                height: (ann_detail.height * height) / 100,
+                Label: Array.isArray(ann_detail.rectanglelabels) ? (ann_detail.rectanglelabels[0] || 'unknown') : (ann_detail.rectanglelabels || 'unknown')
+              });
+            }
+          } else if (Array.isArray(ann.label)) {
+            for (const ann_detail of ann.label) {
+              annsBulk.push({
+                image_path: ann.image,
+                x: (ann_detail.x * width) / 100,
+                y: (ann_detail.y * height) / 100,
+                width: (ann_detail.width * width) / 100,
+                height: (ann_detail.height * height) / 100,
+                Label: Array.isArray(ann_detail.rectanglelabels) ? (ann_detail.rectanglelabels[0] || 'unknown') : (ann_detail.rectanglelabels || 'unknown')
+              });
+            }
+          }
+        }
+      }
+
+      // 1) Insert images and get generated IDs
+      const insertedImages = await Img.bulkCreate(imagesBulk, { returning: true });
+
+      // 2) Map image_path -> image_id
+      const imageMap = {};
+      for (const img of insertedImages) {
+        imageMap[img.image_path] = img.image_id;
+      }
+
+      // 3) Assign correct image_id to each annotation
+      for (const ann of annsBulk) {
+        ann.image_id = imageMap[ann.image_path];
+        delete ann.image_path; // cleanup
+      }
+
+      // 4) Insert annotations
+      await Ann.bulkCreate(annsBulk);
+
+      console.log(`Inserted ${imagesBulk.length} images and ${annsBulk.length} annotations for project ${project.id}`);
+    }
+
+    console.log('Seeding done');
+    return { success: true, message: 'Data inserted successfully!' };
+  } catch (error) {
+    console.error('Error inserting data:', error);
+    return { success: false, message: error.message };
+  } finally {
+    updateStatus.running = false;
+    console.log('updateStatus.running set to false');
+  }
+}
+
+module.exports = { seedLabelStudio, updateStatus };