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selects-onnx
bihanikeshav/selects-onnx
selects-onnx is a machine learning model from bihanikeshav. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for onnx. The card lists the license as apache-2.0.
ONNX Runtime weights for the selects desktop app (photo culling / curation). Replaces the PyTorch + CUDA runtime with ONNX Runtime (DirectML on Windows).
Access
Public
Updated Jul 8, 2026
Repo size
3.2 GB
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.onnx1.9 GB · 59%
At a glance
- Library
- onnx
- License
- apache-2.0
- Access
- Public
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026
- SHA
- 658de7d9
Model card
From the Hugging Face model README
selects-onnx
ONNX Runtime weights for the selects desktop app (photo culling / curation). Replaces the PyTorch + CUDA runtime with ONNX Runtime (DirectML on Windows).
| File | Model | Precision | Task |
|---|---|---|---|
siglip_vision.onnx | SigLIP SO400M vision tower | fp16 | image embeddings |
siglip_text.onnx | SigLIP SO400M text tower | fp16 | text embeddings |
nafnet.onnx | NAFNet GoPro width32 | fp16 | deblur |
ram_plus.onnx (+ .data) | RAM++ (Recognize Anything Plus) | fp32 | tagging |
csrnet.onnx (+ .data) | CSRNet FiveK | fp32 | retouch |
zero_dce.onnx (+ .data) | Zero-DCE++ | fp32 | low-light |
ram_meta.npz, ram_tags.json | RAM++ thresholds / tag vocab | - | post-processing |
All exports are parity-checked against the original PyTorch models. RAM++ is kept fp32 (fp16 conversion trips a Swin shifted-window mask cast; ~400MB not worth it). fp16 models verified cos >= 0.9998.
- Library
- onnx
- License
- apache-2.0
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026