Mask Generation
Transformers
Safetensors
edgetam_video
feature-extraction
libreyolo
edgetam
promptable-segmentation
image-segmentation
Instructions to use LibreYOLO/LibreEdgeTAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LibreYOLO/LibreEdgeTAM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="LibreYOLO/LibreEdgeTAM")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LibreYOLO/LibreEdgeTAM") model = AutoModel.from_pretrained("LibreYOLO/LibreEdgeTAM") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: transformers | |
| pipeline_tag: mask-generation | |
| tags: | |
| - libreyolo | |
| - edgetam | |
| - promptable-segmentation | |
| - image-segmentation | |
| base_model: facebook/EdgeTAM | |
| # LibreEdgeTAM | |
| This is LibreYOLO's Transformers-compatible mirror of the official EdgeTAM | |
| checkpoint. The raw pickle-based `.pt` file is deliberately not included. | |
| ## Source | |
| - Official checkpoint: [`facebook/EdgeTAM`](https://huggingface.co/facebook/EdgeTAM) | |
| at revision `14d7ecc48c656b94e5184519f698cd5386c5a2bf` | |
| - `edgetam.pt` SHA-256: `ed2d4850b8792c239689b043c47046ec239b6e808a3d9b6ae676c803fd8780df` | |
| - Official source: [https://github.com/facebookresearch/EdgeTAM](https://github.com/facebookresearch/EdgeTAM) at commit | |
| `7711e012a30a2402c4eaab637bdb00a521302c91` | |
| - Reference Transformers snapshot: [`yonigozlan/EdgeTAM-hf`](https://huggingface.co/yonigozlan/EdgeTAM-hf) | |
| at revision `c266ce53b3fc00f0f495b583f6a116c4e57f53bb`; its model card declares Apache-2.0 | |
| - Conversion implementation: [`huggingface/transformers`](https://github.com/huggingface/transformers) at | |
| commit `bd37c453544e83eb875ed3608980a1660376007a`, file `src/transformers/models/edgetam_video/convert_edgetam_video_to_hf.py` | |
| ## Modifications | |
| LibreYOLO independently converted the model weights from the safely loaded | |
| official checkpoint using the pinned Apache-2.0 Transformers conversion (key | |
| remapping, lossless key/value tensor splitting, and point-embedding | |
| concatenation). It strict-loaded `EdgeTamVideoModel` and checked all | |
| 984 resulting tensors for exact equality with the pinned reference. | |
| No learned numeric parameter was changed, and `model.safetensors` was not copied | |
| from the reference. | |
| The non-weight `.gitattributes`, `config.json`, `preprocessor_config.json`, | |
| `processor_config.json`, and `video_preprocessor_config.json` files are copied | |
| byte-for-byte from the hash-pinned reference revision above. Every copied file | |
| is SHA-256 verified. The reference repository declares Apache-2.0 in its model | |
| card. | |
| Generated `model.safetensors` SHA-256: `8858f8e4757b0b96dab8763f296ecffd845efbbbf698f64163cfa20a63d5fff4`. | |
| ## Usage | |
| ```python | |
| from transformers import AutoModel | |
| model = AutoModel.from_pretrained("LibreYOLO/LibreEdgeTAM", trust_remote_code=False) | |
| ``` | |
| LibreYOLO users can load it through `LibreEdgeTAM` once the EdgeTAM integration is | |
| installed. | |
| ## License | |
| EdgeTAM code and model checkpoints are licensed under Apache License 2.0. The | |
| verbatim upstream license is included in `LICENSE`; provenance and modification | |
| details are included in `NOTICE`. | |