Sam3

--- license: other extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: mask-generation library_name: transformers tags: - sam3 --- # SAM 3 This repository mirrors the official **Segment Anything Model 3 (SAM 3)** weights released by Meta Superintelligence Labs. SAM 3 is a unified foundation model for prompt-driven segmentation in images and videos. It supports open-vocabulary text prompts and visual prompts (points/boxes/masks). Compared to SAM 2, SAM 3 exhaustively segments each instance of a requested concept and reaches ~75–80% of human-level performance on the SA-CO benchmark (270K unique concepts). ## Highlights - **Presence token** improves discrimination between closely related prompts. - **Decoupled detector + tracker** scales better for long video sequences. - **4M+ automatically annotated concepts** ensure broad coverage of open-world categories. > Original paper: *SAM 3: Segment Anything with Concepts* (Meta AI, 2024). > Resources: [Project Page](https://ai.meta.com/sam3) · [Demo](https://segment-anything.com/) ## Files Included - `sam3.safetensors` — detector and tracker weights for image + video segmentation. - Tokenizer/config assets should be copied from the official `facebookresearch/sam3` repository; this mirror only repackages the safetensors weights for self-hosting. ## Quickstart ```bash pip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 pip install git+https://github.com/facebookresearch/sam3.git python - <<'PY' from sam3 import build_sam3_image_model from sam3.model.sam3_image_processor import Sam3Processor model = build_sam3_image_model( bpe_path="sam3/assets/bpe_simple_vocab_16e6.txt.gz", device="cuda", eva...