Mask Generation
Transformers
Safetensors
sam2
sam2_video
feature-extraction
libreyolo
promptable-segmentation
image-segmentation
Instructions to use LibreYOLO/LibreSAM2tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LibreYOLO/LibreSAM2tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="LibreYOLO/LibreSAM2tiny")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LibreYOLO/LibreSAM2tiny") model = AutoModel.from_pretrained("LibreYOLO/LibreSAM2tiny", device_map="auto") - sam2
How to use LibreYOLO/LibreSAM2tiny with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(LibreYOLO/LibreSAM2tiny) 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/LibreSAM2tiny) 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
File size: 5,695 Bytes
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}
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