Mask Generation
Transformers
Safetensors
sam2
sam2_video
feature-extraction
libreyolo
promptable-segmentation
image-segmentation
Instructions to use LibreYOLO/LibreSAM2large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LibreYOLO/LibreSAM2large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="LibreYOLO/LibreSAM2large")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LibreYOLO/LibreSAM2large") model = AutoModel.from_pretrained("LibreYOLO/LibreSAM2large") - sam2
How to use LibreYOLO/LibreSAM2large with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(LibreYOLO/LibreSAM2large) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained(LibreYOLO/LibreSAM2large) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>): # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("LibreYOLO/LibreSAM2large")
model = AutoModel.from_pretrained("LibreYOLO/LibreSAM2large")Quick Links
LibreSAM2large
SAM-2.1 Hiera Large rehosted for LibreYOLO's LibreSAM promptable segmentation tier.
Source
Derived from facebook/sam2.1-hiera-large at commit
665f8e2ad61cf5f53d65644ff27c8ee525124610 and the Apache-2.0
facebookresearch/sam2 source
release.
Modifications
Learned parameters are unchanged. The upstream Transformers-compatible snapshot
files are mirrored here for LibreYOLO distribution. This repository adds
LibreYOLO model-card packaging plus LICENSE and NOTICE files for Apache-2.0
redistribution.
Usage
from libreyolo import LibreSAM
model = LibreSAM("sam2-large")
result = model("image.jpg", points=[500, 375], labels=[1])
License
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Model tree for LibreYOLO/LibreSAM2large
Base model
facebook/sam2.1-hiera-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="LibreYOLO/LibreSAM2large")