--- library_name: nobg license: apache-2.0 tags: - model_hub_mixin - nobg - nobg-sam3 - promptable - bbox - pytorch_model_hub_mixin ---

MultiMatte

Cut anything you can name.

MultiMatte is a **promptable matting model**: name an object and it returns an alpha matte for it. It is a LoRA fine-tune of [SAM 3](https://huggingface.co/facebook/sam3), retrained to produce continuous opacity instead of binary masks, with the adapter merged into the released weights. ## Prompt steering The same photograph, four prompts.
A bulldog looking up at a bowl held by a person in jeans. The dog cut out of the photograph. The metal bowl cut out of the same photograph. The person's jeans cut out of the same photograph.
input "the dog" "the dog bowl" "the jeans"
Plural concepts return every match in one matte, and small objects stay addressable.
Two tabby cats sprawled on a pink couch beside two remote controls. Both cats cut out together. Only the two remote controls cut out.
input "the cats" "the remote"
The output is a continuous alpha matte, not a threshold, so edges hold up at 1:1 zoom.
Full-resolution crop of a white vehicle's front fender against a red wall. The same crop with the red wall removed and the fender edge intact.
input (crop) cutout (crop)
## Installation ```bash pip install nobg ``` ## Usage ```python from nobg import AutoModel, AutoProcessor model = AutoModel.from_pretrained("feyninc/multimatte") processor = AutoProcessor.from_pretrained("feyninc/multimatte") # Prompt-free: uses the processor's default_prompt ("the main foreground subject"). model.predict(processor, "photo.jpg").save("output.png") # Named concept. model.predict(processor, "photo.jpg", "the dog").save("dog.png") ``` `predict` runs the whole pipeline — load, preprocess, forward under `no_grad` in eval mode, post-process, composite — and returns an RGBA cutout at the input's original resolution. `image` accepts anything [`loadimg`](https://github.com/not-lain/loadimg) takes: a path, URL, base64 string, numpy array or PIL image. The signature is `predict(processor, image, prompt, boxes)`, everything optional after `image`. Useful keywords: `batch_size` (images per forward pass, default 1 to keep peak memory flat) and `return_type="alpha"` for the raw `(H, W)` matte tensor instead of a cutout. ## Results S-measure (`S_α`), prompt-free, higher is better. Both columns come from one scoring harness on identical rows, so the difference isolates the weights — the SAM 3 numbers are a fresh rescore, not values copied from a paper. Changes below 0.002 `S_α` are treated as measurement noise. † No sibling in the training mix. DAVIS-S and DUT-OMRON are the two fully cross-domain splits here, so they are the pair to read for generalization — and MultiMatte's best absolute score lands on DAVIS-S at 0.979. **Naming the concept helps, before and after training.** On DIS-VD, a real human-written phrase adds 0.150 `S_α` to base SAM 3 for zero gradient steps, and still adds 0.036 to MultiMatte after fine-tuning. Prompt supervision made the model better at both pathways rather than making it prompt-insensitive. ## Training | feature | detail | |:--|:--| | Base model | [`facebook/sam3`](https://huggingface.co/facebook/sam3), 0.86 B parameters | | Method | LoRA, rank 16, merged into the released weights | | Trainable | 19.49 M parameters — 2.27 % of the model | | Targets | Attention and MLP projections in every tower, including the CLIP text tower | | Objective | Focal loss + Dice loss (SAM 3's own semantic segmentation objective) | | Steps | 14,000 | | Data | 19,953 images: salient objects, camouflage, high-resolution subjects, hair, marine scenes | | Prompt supervision | 4,949 images (24.8 %) with human-written per-image concept phrases | | Input resolution | 1008 × 1008 | ## Citation ```bibtex @note{multimatte2026, title = {MultiMatte: Cut Out Anything You Can Name}, author = {Hichri, Hafedh and Feyn Research}, year = {2026}, venue = {Feyn Field Notes} } ``` Please also cite the base model and the adaptation method: ```bibtex @article{sam3, title={SAM 3: Segment Anything with Concepts}, author={Carion, Nicolas and Gustafson, Laura and Hu, Yuan-Ting and Debnath, Shoubhik and Hu, Ronghang and Suris, Didac and Ryali, Chaitanya and Alwala, Kalyan Vasudev and Khedr, Haitham and Huang, Andrew and Lei, Jie and Ma, Tengyu and Guo, Baishan and Marks, Markus and Greer, Joseph and Wang, Meng and Sun, Peize and R{\"a}dle, Roman and Afouras, Triantafyllos and Mavroudi, Effrosyni and Dollar, Piotr and Ravi, Nikhila and Saenko, Kate and Zhang, Pengchuan and Feichtenhofer, Christoph}, journal={arXiv preprint arXiv:2511.16719}, year={2025}, url={https://ai.meta.com/research/publications/sam-3-segment-anything-with-concepts/}, } @article{lora, title={LoRA: Low-Rank Adaptation of Large Language Models}, author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu}, journal={arXiv preprint arXiv:2106.09685}, year={2021}, } ``` ## Acknowledgements Built on Meta's SAM 3. FlowDIS supplied the human-written DIS5K phrases used for training and evaluation. Thinking Machines' LoRA analysis informed the adapter configuration. Thanks to the dataset authors whose released work made the training mix and evaluation possible.