--- pipeline_tag: mask-generation license: other license_name: sam-license license_link: https://github.com/facebookresearch/sam3/blob/main/LICENSE base_model: facebook/sam3 library_name: kerasformers tags: - keras - kerasformers - sam3 - mask-generation - image-segmentation - arxiv:2511.16719 - pytorch - jax - tf --- ## ***See [our collection](https://huggingface.co/collections/kerasformers/sam-v1-v2-v3-6a6a8c261dabbc2996e1b4a2) for all versions of SAM.*** # Run SAM3 with Keras 3: JAX, PyTorch, or TensorFlow [![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-black?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-SAM3-blue)](https://imvision12.github.io/KerasFormers/sam3/) [![Collection](https://img.shields.io/badge/HF-SAM%20collection-yellow)](https://huggingface.co/collections/kerasformers/sam-v1-v2-v3-6a6a8c261dabbc2996e1b4a2) # kerasformers/sam3 Paper: [SAM 3: Segment Anything with Concepts (arXiv:2511.16719)](https://arxiv.org/abs/2511.16719) · [HF Papers](https://huggingface.co/papers/2511.16719) SAM3 segments by concept, not location: give it a noun phrase and it finds every matching instance. A ViT-L backbone and FPN feed a DETR-style encoder/decoder with object queries; a CLIP text encoder supplies the open-vocabulary side. Boxes can still be mixed with text. For more details on the model, please go to Meta's original [model card](https://huggingface.co/facebook/sam3). Pure-**Keras 3** conversion of [`facebook/sam3`](https://huggingface.co/facebook/sam3) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**. This is a **concept-prompted** checkpoint (`SAM3InstanceSegment` / `SAM3Detect` / `SAM3SemanticSegment`): pass a text noun phrase (backbone ViT-L/14). ## ✨ Quick start ```python import os os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow" from kerasformers.models.sam3 import SAM3InstanceSegment segmenter = SAM3InstanceSegment(variant="kerasformers/sam3") result = segmenter.predict( images="your_image.jpg", text="person", threshold=0.3 )[0] print(len(result["scores"]), result["masks"].shape) ``` Load any SAM / SAM2 / SAM3 variant the same way with `from_weights("kerasformers/")` (use `SAM3InstanceSegment` for this repo): | Variant | Hub | Family | |---|---|---| | `sam_vit_base` | [`kerasformers/sam_vit_base`](https://huggingface.co/kerasformers/sam_vit_base) | SAM | | `sam_vit_large` | [`kerasformers/sam_vit_large`](https://huggingface.co/kerasformers/sam_vit_large) | SAM | | `sam_vit_huge` | [`kerasformers/sam_vit_huge`](https://huggingface.co/kerasformers/sam_vit_huge) | SAM | | `sam2_hiera_small` | [`kerasformers/sam2_hiera_small`](https://huggingface.co/kerasformers/sam2_hiera_small) | SAM2 | | `sam2_hiera_base_plus` | [`kerasformers/sam2_hiera_base_plus`](https://huggingface.co/kerasformers/sam2_hiera_base_plus) | SAM2 | | `sam2_hiera_large` | [`kerasformers/sam2_hiera_large`](https://huggingface.co/kerasformers/sam2_hiera_large) | SAM2 | | `sam3` | [`kerasformers/sam3`](https://huggingface.co/kerasformers/sam3) | SAM3 | ## Tips - Set `KERAS_BACKEND` **before** importing Keras / kerasformers. - SAM / SAM2: point coordinates are in original pixel space; box prompts need `enable_boxes=True` / `include_box_input=True` when building the graph. - SAM2 in this port is image-only (no video memory bank). - SAM3: prefer `SAM3InstanceSegment.predict(...)` for text prompts; upstream `facebook/sam3` is gated. - See [SAM3 docs](https://imvision12.github.io/KerasFormers/sam3/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/). - Community / upstream safetensors still work via the `hf:` prefix, e.g. `SAM3Model.from_weights("hf:facebook/sam3")`. ## Special Thanks A huge thank you to the Meta SAM 3 authors for creating and releasing these models. License: see the [SAM 3 LICENSE](https://github.com/facebookresearch/sam3/blob/main/LICENSE) (Hub tag: `other` / `sam-license`). Upstream [`facebook/sam3`](https://huggingface.co/facebook/sam3) is gated.