Instructions to use feyninc/multimatte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- nobg
How to use feyninc/multimatte with nobg:
pip install nobg
# Option 1: use via the predict method from nobg import AutoModel, AutoProcessor model = AutoModel.from_pretrained("feyninc/multimatte").eval() processor = AutoProcessor.from_pretrained("feyninc/multimatte") cutout = model.predict(processor, "image.jpg", "prompt")# Option 2: use the model and processor directly import torch from loadimg import load_img from nobg import AutoModel, AutoProcessor model = AutoModel.from_pretrained("feyninc/multimatte").eval() processor = AutoProcessor.from_pretrained("feyninc/multimatte") image = load_img("image.jpg").convert("RGB") inputs = processor(image, return_tensors="pt") with torch.no_grad(): outputs = model(pixel_values=inputs["pixel_values"]) alpha = processor.post_process_alpha_matting(outputs, target_sizes=[(image.height, image.width)])[0] processor.cutout(image, alpha).save("output.png") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -113,99 +113,8 @@ S-measure (`S_α`), prompt-free, higher is better. Both columns come from one sc
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identical rows, so the difference isolates the weights — the SAM 3 numbers are a fresh rescore, not
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values copied from a paper. Changes below 0.002 `S_α` are treated as measurement noise.
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<tr>
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<th>Split</th>
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<th style="text-align: right;">SAM 3</th>
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<th style="text-align: right;">MultiMatte</th>
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<th style="text-align: right;">Δ</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td colspan="4"><strong>Dichotomous (DIS5K)</strong></td>
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</tr>
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<tr>
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<td>DIS-VD</td>
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<td style="text-align: right;">0.667</td>
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<td style="text-align: right;"><strong>0.901</strong></td>
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<td style="text-align: right;">+0.233</td>
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</tr>
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<tr>
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<td>DIS-TE1</td>
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<td style="text-align: right;">0.667</td>
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<td style="text-align: right;"><strong>0.901</strong></td>
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<td style="text-align: right;">+0.234</td>
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</tr>
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<td>DIS-TE2</td>
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<td style="text-align: right;">0.703</td>
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<td style="text-align: right;"><strong>0.923</strong></td>
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<td style="text-align: right;">+0.220</td>
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</tr>
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<td>DIS-TE3</td>
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<td style="text-align: right;">0.685</td>
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<td style="text-align: right;"><strong>0.921</strong></td>
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<td style="text-align: right;">+0.236</td>
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</tr>
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<td>DIS-TE4</td>
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<td style="text-align: right;">0.649</td>
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<td style="text-align: right;"><strong>0.893</strong></td>
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<td style="text-align: right;">+0.245</td>
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</tr>
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<tr>
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<td colspan="4"><strong>Salient objects</strong></td>
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</tr>
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<td>DAVIS-S †</td>
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<td style="text-align: right;">0.913</td>
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<td style="text-align: right;"><strong>0.979</strong></td>
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<td style="text-align: right;">+0.066</td>
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</tr>
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<td>HRSOD-TE</td>
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<td style="text-align: right;">0.930</td>
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<td style="text-align: right;"><strong>0.969</strong></td>
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<td style="text-align: right;">+0.039</td>
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</tr>
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<td>UHRSD-TE</td>
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<td style="text-align: right;">0.877</td>
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<td style="text-align: right;"><strong>0.961</strong></td>
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<td style="text-align: right;">+0.084</td>
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</tr>
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<td>DUTS-TE</td>
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<td style="text-align: right;">0.892</td>
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<td style="text-align: right;"><strong>0.954</strong></td>
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<td style="text-align: right;">+0.062</td>
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</tr>
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<td>DUT-OMRON †</td>
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<td style="text-align: right;">0.792</td>
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<td style="text-align: right;"><strong>0.901</strong></td>
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<td style="text-align: right;">+0.109</td>
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</tr>
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<tr>
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<td colspan="4"><strong>Camouflage</strong></td>
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</tr>
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<tr>
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<td>COD10K-TE</td>
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<td style="text-align: right;">0.787</td>
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<td style="text-align: right;"><strong>0.934</strong></td>
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<td style="text-align: right;">+0.148</td>
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</tr>
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<tr>
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<td>CAMO-TE</td>
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<td style="text-align: right;">0.827</td>
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<td style="text-align: right;"><strong>0.914</strong></td>
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<td style="text-align: right;">+0.086</td>
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</tr>
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</tbody>
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</table>
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† No sibling in the training mix. DAVIS-S and DUT-OMRON are the two fully cross-domain splits here,
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so they are the pair to read for generalization — and MultiMatte's best absolute score lands on
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identical rows, so the difference isolates the weights — the SAM 3 numbers are a fresh rescore, not
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values copied from a paper. Changes below 0.002 `S_α` are treated as measurement noise.
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+
<img width="33%" src="https://cdn-uploads.huggingface.co/production/uploads/6527e89a8808d80ccff88b7a/x93Ond7tAw8aoLYkucUQn.png"/>
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† No sibling in the training mix. DAVIS-S and DUT-OMRON are the two fully cross-domain splits here,
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so they are the pair to read for generalization — and MultiMatte's best absolute score lands on
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