Instructions to use cp500/sam21-hyperkd-rs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use cp500/sam21-hyperkd-rs with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("cp500/sam21-hyperkd-rs") 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("cp500/sam21-hyperkd-rs") 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
SAM 2.1 base-plus, HyperKD-RS fine-tuned decoder
Mask decoder + prompt encoder of SAM 2.1 hiera-base-plus fine-tuned on cp500/hyperkd-rs: point prompt -> track-consensus mask over 38 Landsat spot shards (1985-2025, 30 m). Encoder frozen.
Held-out (temporal holdout, last 2 scenes/spot, argmax-IoU head): zero-shot 0.365 -> fine-tuned 0.565 IoU vs consensus targets. 4,000 steps, BCE+Dice+IoU-head MSE, best-head supervision, lr 1e-5 AdamW.
Load: build SAM 2.1 b+ from the official repo/checkpoint, then load this state dict into sam.sam_mask_decoder / sam.sam_prompt_encoder. Training script: harness/finetune_sam21.py in the dataset repo.
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Base model
facebook/sam2.1-hiera-base-plus