Instructions to use tkclam/sam3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tkclam/sam3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="tkclam/sam3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("tkclam/sam3") model = AutoModel.from_pretrained("tkclam/sam3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,256 Bytes
4c7c7aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ---
license: other
license_name: sam-license
license_link: LICENSE
library_name: transformers
pipeline_tag: mask-generation
tags:
- sam3
---
# SAM 3 (ungated copy)
An unmodified copy of Meta's [SAM 3](https://huggingface.co/facebook/sam3) checkpoint in Transformers format, taken from `facebook/sam3` at commit `3c879f39826c281e95690f02c7821c4de09afae7`. It is hosted so that [spintrack](https://github.com/NeLy-EPFL/spintrack) can find a trackball in a video without a Hugging Face login.
Every file except this README is byte-identical to Meta's. `model.safetensors` has SHA-256 `6d06f0a5f84e435071fe6603e61d0b4cc7b40e0d39d487cfd4d67d8cc11cc14a`.
The model is redistributed under the SAM License (`LICENSE`), as its section 1.b.i allows, and its use is governed by that license. Among other terms, publications of research that uses it must acknowledge the use of SAM (section 1.b.ii). For the model itself, its intended uses and how to cite it, see [Meta's model card](https://huggingface.co/facebook/sam3) and the [SAM 3 repository](https://github.com/facebookresearch/sam3).
```python
from transformers import Sam3Model, Sam3Processor
model = Sam3Model.from_pretrained("tkclam/sam3")
processor = Sam3Processor.from_pretrained("tkclam/sam3")
```
|