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
| language: [] | |
| license: mit | |
| tags: | |
| - pytorch | |
| - image-segmentation | |
| - sam | |
| - glove | |
| - baseball | |
| - computer-vision | |
| - custom-model | |
| library_name: pytorch | |
| datasets: | |
| - custom | |
| metrics: | |
| - dice | |
| - iou | |
| inference: false | |
| widget: [] | |
| model-index: | |
| - name: glove_labelling | |
| results: [] | |