Image Segmentation
PyTorch
sam2
custom-sam2
glove
baseball
sports-analytics
computer-vision
custom-model
Instructions to use caball21/glove_labelling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use caball21/glove_labelling with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(caball21/glove_labelling) 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(caball21/glove_labelling) 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
Create model_card.yaml
Browse files- model_card.yaml +22 -0
model_card.yaml
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---
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language: []
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license: mit
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tags:
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- pytorch
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- image-segmentation
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- sam
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- glove
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- baseball
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- computer-vision
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- custom-model
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library_name: pytorch
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datasets:
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- custom
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metrics:
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- dice
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- iou
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inference: false
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widget: []
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model-index:
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- name: glove_labelling
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results: []
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