chenshuang-zhang/imagenet_d
[CVPR 2024 Highlight] ImageNet-D
ImageNet-D provides a specialized dataset to evaluate how well computer vision models, from classic classifiers like ResNet to advanced foundation models like CLIP and MiniGPT-4, can recognize objects in images generated by AI diffusion models. It takes a trained vision model and synthetic images as input, and outputs its object recognition accuracy under various challenging conditions like altered backgrounds, textures, or materials. This is for researchers and engineers developing and testing robust computer vision systems.
No commits in the last 6 months.
Use this if you need to rigorously test the real-world robustness of your image classification, vision-language, or visual question answering models against highly varied and realistic synthetic data.
Not ideal if you are looking for a standard image classification dataset or if your primary focus is on training models from scratch rather than benchmarking their robustness.
Stars
47
Forks
5
Language
Python
License
MIT
Category
Last pushed
Oct 15, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/chenshuang-zhang/imagenet_d"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
OPTML-Group/Unlearn-Saliency
[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in...
Shilin-LU/VINE
[ICLR 2025] "Robust Watermarking Using Generative Priors Against Image Editing: From...
WindVChen/DiffAttack
An unrestricted attack based on diffusion models that can achieve both good transferability and...
koninik/DiffusionPen
Official PyTorch Implementation of "DiffusionPen: Towards Controlling the Style of Handwritten...
Wuyxin/DISC
(ICML 2023) Discover and Cure: Concept-aware Mitigation of Spurious Correlation