Mask Generation
Transformers
Safetensors
sam2
sam2_video
feature-extraction
libreyolo
promptable-segmentation
image-segmentation
Instructions to use LibreYOLO/LibreSAM2base-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LibreYOLO/LibreSAM2base-plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="LibreYOLO/LibreSAM2base-plus")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("LibreYOLO/LibreSAM2base-plus") model = AutoModel.from_pretrained("LibreYOLO/LibreSAM2base-plus") - sam2
How to use LibreYOLO/LibreSAM2base-plus with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(LibreYOLO/LibreSAM2base-plus) 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/LibreSAM2base-plus) 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: 683 Bytes
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"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"disable_grouping": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "Sam2ImageProcessorFast",
"image_std": [
0.229,
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],
"input_data_format": null,
"mask_size": {
"height": 256,
"width": 256
},
"processor_class": "Sam2VideoProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"return_tensors": null,
"size": {
"height": 1024,
"width": 1024
}
}
|