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Saumya Saksena

dronefreak
hugging-science
138 2 4
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https://saumyasaksena.com

AI & ML interests

Computer Vision, Deep Learning, Image Restoration, Image De-noising, LLMs, RAG, Image Classification, Image Segmentation, EEG Classification, Signal Processing, PPG, BCI

Recent Activity

liked a Space about 2 hours ago
cinderholm/wan-i2v
repliedto their post about 15 hours ago
🌧️❄️ Free demo: remove rain, raindrops, or snow from a photo with a single model I put together an unofficial demo for **Histoformer** (ECCV 2024, arXiv: 2407.10172), a 16.6M-parameter transformer that handles three different weather degradations, rain streaks, adherent raindrops, and snow, in one unified model. It uses a "histogram self-attention" mechanism that groups pixels by degradation intensity instead of spatial position, which is a neat way to sidestep the usual spatial-window tradeoffs in restoration transformers. Try it here, free on ZeroGPU: https://huggingface.co/spaces/dronefreak/histoformer-weather-restoration Upload a photo and get a before/after slider. Two checkpoints available: one tuned for real-world photos, one for the paper's synthetic benchmarks. Also put together a cleaner, easy-to-use model card with a copy-pasteable Quickstart if you'd rather run it yourself: https://huggingface.co/dronefreak/Histoformer This is an unofficial demo/mirror, not affiliated with the original authors. All credit for the actual research goes to Shangquan Sun, Wenqi Ren, Xinwei Gao, Rui Wang, and Xiaochun Cao (@sunsean). Official repo: https://github.com/sunshangquan/Histoformer. Weights are MIT-licensed. Reported numbers from the paper: 32.1 PSNR on rain+fog (Outdoor-Rain), 33.1 on raindrops, 37.4 / 32.2 on light/heavy snow (Snow100K-S/L).
posted an update about 17 hours ago
🌧️❄️ Free demo: remove rain, raindrops, or snow from a photo with a single model I put together an unofficial demo for **Histoformer** (ECCV 2024, arXiv: 2407.10172), a 16.6M-parameter transformer that handles three different weather degradations, rain streaks, adherent raindrops, and snow, in one unified model. It uses a "histogram self-attention" mechanism that groups pixels by degradation intensity instead of spatial position, which is a neat way to sidestep the usual spatial-window tradeoffs in restoration transformers. Try it here, free on ZeroGPU: https://huggingface.co/spaces/dronefreak/histoformer-weather-restoration Upload a photo and get a before/after slider. Two checkpoints available: one tuned for real-world photos, one for the paper's synthetic benchmarks. Also put together a cleaner, easy-to-use model card with a copy-pasteable Quickstart if you'd rather run it yourself: https://huggingface.co/dronefreak/Histoformer This is an unofficial demo/mirror, not affiliated with the original authors. All credit for the actual research goes to Shangquan Sun, Wenqi Ren, Xinwei Gao, Rui Wang, and Xiaochun Cao (@sunsean). Official repo: https://github.com/sunshangquan/Histoformer. Weights are MIT-licensed. Reported numbers from the paper: 32.1 PSNR on rain+fog (Outdoor-Rain), 33.1 on raindrops, 37.4 / 32.2 on light/heavy snow (Snow100K-S/L).
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