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

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

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.

Citation

@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
Dataset: CC BY 4.0