Image Segmentation
PyTorch
sam2
English
computer-vision
segmentation
few-shot-learning
zero-shot-learning
clip
Instructions to use ParallelLLC/Segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use ParallelLLC/Segmentation with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(ParallelLLC/Segmentation) 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(ParallelLLC/Segmentation) 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
| # Core ML/DL libraries | |
| torch>=2.0.0 | |
| torchvision>=0.15.0 | |
| transformers>=4.30.0 | |
| diffusers>=0.21.0 | |
| # SAM 2 and related | |
| segment-anything-2>=0.1.0 | |
| groundingdino-py>=0.4.0 | |
| ultralytics>=8.0.0 | |
| # Computer Vision | |
| opencv-python>=4.8.0 | |
| Pillow>=10.0.0 | |
| albumentations>=1.3.0 | |
| kornia>=0.6.0 | |
| # Data processing | |
| numpy>=1.24.0 | |
| pandas>=2.0.0 | |
| scipy>=1.10.0 | |
| scikit-learn>=1.3.0 | |
| scikit-image>=0.21.0 | |
| # Visualization | |
| matplotlib>=3.7.0 | |
| seaborn>=0.12.0 | |
| plotly>=5.15.0 | |
| wandb>=0.15.0 | |
| # Jupyter and notebooks | |
| jupyter>=1.0.0 | |
| ipywidgets>=8.0.0 | |
| # Utilities | |
| tqdm>=4.65.0 | |
| pyyaml>=6.0 | |
| click>=8.1.0 | |
| rich>=13.0.0 | |
| # Domain-specific | |
| rasterio>=1.3.0 # Satellite imagery | |
| fiona>=1.9.0 # Geospatial data | |
| geopandas>=0.13.0 # Geospatial analysis | |
| # Evaluation metrics | |
| pycocotools>=2.0.6 | |
| timm>=0.9.0 | |
| # Optional: GPU acceleration | |
| # cupy-cuda11x>=12.0.0 # Uncomment for CUDA 11.x | |
| # cupy-cuda12x>=12.0.0 # Uncomment for CUDA 12.x |