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multilabel_freeze_metadata.json

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+{
+  "model_type": "multilabel_resnet50",
+  "training_mode": "freeze",
+  "num_classes": 90,
+  "best_map": 0.3746700931482063,
+  "epoch": 29,
+  "total_parameters": 24656463,
+  "config": {
+    "experiment_name": "multilabel_freeze_encoder_optimized",
+    "training_mode": "freeze",
+    "model": {
+      "pretrained": true,
+      "checkpoint_path": "/content/drive/MyDrive/Colab Notebooks/ai_course_wrok/resnet50_best.pth",
+      "dropout": 0.5
+    },
+    "data": {
+      "dataset_name": "ibrahimdaud/multi-label-food-recognition",
+      "batch_size": 128,
+      "num_workers": 8,
+      "image_size": 224,
+      "split_ratio": 0.8
+    },
+    "training": {
+      "epochs": 50,
+      "learning_rate": 0.001,
+      "weight_decay": 0.0001,
+      "scheduler": "step",
+      "step_size": 10,
+      "gamma": 0.1
+    },
+    "use_amp": true,
+    "checkpoint_dir": "/content/drive/MyDrive/Colab Notebooks/ai_course_wrok",
+    "resume_checkpoint": null,
+    "use_wandb": true,
+    "wandb": {
+      "project": "multilabel-food-recognition",
+      "run_name": "multilabel_freeze_encoder_optimized"
+    },
+    "debug_mode": false,
+    "debug_max_samples": 10,
+    "seed": 42
+  }
+}