---
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), 2026, 19(7), 530.  
> DOI: [10.3390/a19070530](https://doi.org/10.3390/a19070530). Open access (CC BY 4.0).  
> 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/`)

Results below are 5-fold stratified cross-validation at matched 224×224 resolution (the paper's primary setting).

| File | Model | Accuracy (%) | F1 |
|---|---|---|---|
| `GoldFormer_best.pth` | GoldFormer (CNN + Swin-T + TAAG) | **95.02 ± 0.75** | **0.9502** |
| `ViT_B16_best.pth` | ViT-B/16 | 94.31 ± 0.94 | 0.9431 |
| `Swin_T_best.pth` | Swin Transformer-Tiny (GoldFormer's backbone) | 93.65 ± 0.67 | 0.9365 |
| `ResNet101_best.pth` | ResNet-101 | 92.29 ± 1.01 | 0.9228 |
| `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 | — | — |

GoldFormer is the best single model and beats a soft-voting ensemble (94.92%); it is
statistically tied with the strongest individual backbone, ViT-B/16 (paired McNemar
p = 0.228), and significantly beats its own Swin-T backbone (p = 0.014), while using
about half ViT-B/16's FLOPs (8.6 vs 16.9 GFLOPs) and fewer parameters (54.3M vs 86.6M).

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 models import build_model   # models.py from the GitHub repo

# Download weights
# bash fetch_weights.sh   (from the GitHub repo)

model = build_model("goldformer")
state = torch.load("weights/GoldFormer_best.pth", map_location="cpu", weights_only=True)
model.load_state_dict(state)   # strict — exact match with the released checkpoint
model.eval()

from torchvision import transforms
from PIL import Image

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    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, gamma = model(x)   # gamma = TAAG gate activations, for interpretability
    prob_authentic = torch.softmax(logits, dim=1)[0, 0].item()
    print(f"P(authentic) = {prob_authentic:.3f}")
```

> **Note:** All checkpoints, including GoldFormer, use 224×224 input in the published
> configuration. The `models.py` class definitions (`TextureAwareAttentionGate` +
> `GoldFormer`) 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},
  volume  = {19},
  number  = {7},
  pages   = {530},
  year    = {2026},
  doi     = {10.3390/a19070530}
}
```

## 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)