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README.md

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+---
+license: mit
+datasets:
+  - zobeir/GoldNet
+tags:
+  - image-classification
+  - pytorch
+  - vision-transformer
+  - counterfeit-detection
+  - gold
+  - fine-grained-recognition
+language:
+  - en
+---
+
+# GoldNet Model Weights
+
+Trained checkpoints for **GoldFormer** and baseline models from the paper:
+
+> **GoldFormer: A Texture-Aware Vision Transformer-based Algorithm for Detecting Near-Identical Images**  
+> Z. Raisi, *Algorithms* (MDPI), under review.  
+> Code & dataset: [github.com/zobeirraisi/GoldNet](https://github.com/zobeirraisi/GoldNet)
+
+## Task
+
+Binary image classification — **authentic vs. counterfeit gold items** — from ordinary smartphone photographs. The two classes are near-identical to the eye; trained experts reached 89.80% accuracy on a blind subset.
+
+## Available Checkpoints (`weights/`)
+
+| File | Model | Accuracy (5-fold CV) |
+|---|---|---|
+| `GoldFormer_best.pth` | GoldFormer (CNN + Swin-T + TAAG) | 94.69 ± 0.79% |
+| `Swin_T_best.pth` | Swin Transformer-Tiny | 94.31 ± 0.78% |
+| `ViT_B16_best.pth` | ViT-B/16 | 94.31 ± 0.94% |
+| `ResNet101_best.pth` | ResNet-101 | 92.29 ± 1.01% |
+| `ResNet50_best.pth` | ResNet-50 | — |
+| `ResNet18_best.pth` | ResNet-18 | — |
+| `DenseNet121_best.pth` | DenseNet-121 | — |
+| `EfficientNet_B3_best.pth` | EfficientNet-B3 | — |
+| `EfficientNet_B0_best.pth` | EfficientNet-B0 | — |
+| `MobileNet_V2_best.pth` | MobileNet-V2 | — |
+
+All models trained with 5-fold stratified cross-validation, AdamW, AMP (bfloat16), freeze-then-unfreeze fine-tuning on the GoldNet dataset (2,127 images, 1,044 authentic / 1,083 counterfeit).
+
+## Usage
+
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+
+# Download weights
+# bash fetch_weights.sh   (from the GitHub repo)
+
+# Load a checkpoint
+model = torch.load("weights/GoldFormer_best.pth", weights_only=True)
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((299, 299)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225]),
+])
+
+img = Image.open("your_image.jpg").convert("RGB")
+x = transform(img).unsqueeze(0)
+
+with torch.no_grad():
+    logits = model(x)
+    prob_authentic = torch.softmax(logits, dim=1)[0, 0].item()
+    print(f"P(authentic) = {prob_authentic:.3f}")
+```
+
+> **Note:** All baseline models use 224×224 input. GoldFormer uses 299×299.  
+> The `models.py` class definitions are in the [GitHub repo](https://github.com/zobeirraisi/GoldNet).
+
+## Citation
+
+```bibtex
+@article{raisi2026goldformer,
+  title   = {GoldFormer: A Texture-Aware Vision Transformer-based Algorithm
+             for Detecting Near-Identical Images},
+  author  = {Raisi, Zobeir},
+  journal = {Algorithms},
+  year    = {2026},
+  note    = {Under review}
+}
+```
+
+## License
+
+Model weights: [MIT License](https://github.com/zobeirraisi/GoldNet/blob/main/LICENSE)  
+Dataset: [CC BY 4.0](https://github.com/zobeirraisi/GoldNet/blob/main/LICENSE-DATA)