Mask Generation
sam2
swift-object-cutout
segment-anything
interactive-segmentation
background-removal
core-ai
apple-silicon
macos
Instructions to use arraypress/cutout-sam2-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use arraypress/cutout-sam2-tiny with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("arraypress/cutout-sam2-tiny") 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("arraypress/cutout-sam2-tiny") 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: 875 Bytes
a0b4f4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | cutout-sam2-tiny: SAM 2.1 hiera tiny, Copyright (c) Meta Platforms, Inc. and
affiliates. Code: facebookresearch/sam2 at commit
2b90b9f5ceec907a1c18123530e92e794ad901a4. Weights: facebook/sam2.1-hiera-tiny
at revision de431c4043854a71d8101e17995dfe596bf101a5 (sam2.1_hiera_tiny.pt).
Both Apache License 2.0; see LICENSE.
Changed for Core AI, in export_sam2.py:
- The image predictor's own steps are traced as two entry points: `encode`
(normalisation, image encoder, no-memory embedding) and `decode` (prompt
encoder for one point, mask decoder, multimask output).
- Weights and arithmetic are float16, except the prompt encoder, which stays
float32; every input and output is float32.
- The 256x256 mask logits are returned unclamped (the PyTorch predictor clamps
them to +/-32; the host thresholds them).
The trained weights are otherwise the authors', unmodified.
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