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
- Xet hash:
- 12e2c74cac753cdd44bbc13b772e2ecdd559ce21b66e3a5b6f60eddb5b9ccc09
- Size of remote file:
- 1.04 GB
- SHA256:
- 677951855b34eb54b6af2350e9611e5d71fbf8396a3285387b164ade97cad76c
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