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README.md

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-license: mit
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+# Multi-Label Object Classification using ResNet
+
+## Model Description
+ResNet18 and ResNet50 models fine-tuned for multi-label object classification, 
+capable of detecting 12 objects simultaneously in a single image.
+
+## Classes
+`backpack`, `book`, `bottle`, `calculator`, `chair`, `clock`, 
+`desk`, `keychain`, `laptop`, `paper`, `pen`, `phone`
+
+## Usage
+Download all 10 `.pth` files and use with the inference script from the 
+[GitHub repository](https://github.com/pranav1233/multi-label-object-classification).
+
+## Model Files
+- `resnet18_fold1.pth` through `resnet18_fold5.pth` — ResNet18 ensemble
+- `resnet50_fold1.pth` through `resnet50_fold5.pth` — ResNet50 ensemble
+
+## Performance
+
+| Model    | Exact Match | Micro F1 | Mean IOU |
+|----------|-------------|----------|----------|
+| ResNet18 | 52.29%      | 76.92%   | 0.7200   |
+| ResNet50 | 68.78%      | 86.18%   | 0.8301   |
+
+## Training
+- 5-Fold Stratified Cross Validation
+- Test Time Augmentation (TTA) with 10 augmented views
+- Optimal prediction threshold: 0.40