Tipsomaly (ICASSP'26) learned prompt checkpoints

Learned prompt weights for the ICASSP'26 paper, TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection (Tipsomaly), trained using the MVTec AD and VisA datasets. These are auxiliary prompt checkpoints for the official Tipsomaly PyTorch implementation; they are not standalone model weights and do not include the TIPS vision-language backbone.

Paper: Hugging Face paper page · arXiv · IEEE

Demo: Tipsomaly on Hugging Face Spaces

Files

Each dataset directory contains training arguments and two learned-prompt checkpoints:

Dataset	Files
MVTec AD	args.txt, learnable_params_1.pth, learnable_params_2.pth
VisA	args.txt, learnable_params_1.pth, learnable_params_2.pth

learnable_params_1.pth and learnable_params_2.pth are saved at the end of training epochs 1 and 2, respectively. Each file stores the learned prompt tensor under the learnable_prompts key. The recorded training settings use 518-pixel images, the concat prompt-learning method, and segmentation loss.

Download and use

Install and authenticate the Hugging Face CLI. From the Tipsomaly project directory, download the desired checkpoint files (replace mvtec with visa for VisA):

hf download AlirezaSalehi99/Tipsomaly \
  checkpoints/mvtec/args.txt \
  checkpoints/mvtec/learnable_params_2.pth \
  --local-dir .

For instructions on preparing the environment and datasets and running either checkpoint, follow the official GitHub repository's setup and inference instructions. The checkpoints contain learned prompts only; the TIPS backbone and evaluation datasets must be obtained separately.

Training provenance

The checkpoint args.txt files record training with prompt_learn_method=concat, cls_seg_los=seg, seed 111, and image size 518. The MVTec and VisA weights were trained separately using their respective training datasets. See the paper and project repository for the method, full implementation, and experiment details.

Citation

@article{salehi2026tips,
  title={TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection},
  author={Salehi, Alireza and Karami, Ehsan and Noey, Sepehr and Noey, Sahand and Yamada, Makoto and Hosseini, Reshad and Sabokrou, Mohammad},
  journal={arXiv preprint arXiv:2602.03594},
  year={2026}
}

License

The Tipsomaly project and these prompt checkpoints are released under the MIT License.