Instructions to use avbiswas/sam2.1-hiera-tiny-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use avbiswas/sam2.1-hiera-tiny-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir sam2.1-hiera-tiny-mlx avbiswas/sam2.1-hiera-tiny-mlx
- sam2
How to use avbiswas/sam2.1-hiera-tiny-mlx with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(avbiswas/sam2.1-hiera-tiny-mlx) 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(avbiswas/sam2.1-hiera-tiny-mlx) 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
- Local Apps
- LM Studio
Upload sam2.1_hiera_tiny_image_segmenter.safetensors with huggingface_hub
Browse files
sam2.1_hiera_tiny_image_segmenter.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e3c62dfbe0cab6b279ed7d03b9a39b74e5026b9f34fc2436f975a69e575f559e
|
| 3 |
+
size 181026139
|