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backend/project_53/53/exp.py

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+#!/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