Instructions to use CardAnvilModels/sam2.1-hiera-tiny-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use CardAnvilModels/sam2.1-hiera-tiny-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('mask-generation', 'CardAnvilModels/sam2.1-hiera-tiny-ONNX'); - sam2
How to use CardAnvilModels/sam2.1-hiera-tiny-ONNX with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("CardAnvilModels/sam2.1-hiera-tiny-ONNX") 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("CardAnvilModels/sam2.1-hiera-tiny-ONNX") 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
Download processor_config.json from CardAnvilModels/sam2.1-hiera-tiny-ONNX: direct link, hf CLI and curl.
- Browser
- Download file 932 Bytes
-
https://huggingface.co/CardAnvilModels/sam2.1-hiera-tiny-ONNX/resolve/main/processor_config.json
- Command line
-
hf download hf://CardAnvilModels/sam2.1-hiera-tiny-ONNX/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/CardAnvilModels/sam2.1-hiera-tiny-ONNX/resolve/main/processor_config.json
932 Bytes
| { | |
| "image_processor": { | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": null, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "Sam2ImageProcessorFast", | |
| "image_seq_length": null, | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "input_data_format": null, | |
| "mask_size": { | |
| "height": 256, | |
| "width": 256 | |
| }, | |
| "pad_size": null, | |
| "processor_class": "Sam2Processor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "height": 1024, | |
| "width": 1024 | |
| } | |
| }, | |
| "point_pad_value": -10, | |
| "processor_class": "Sam2Processor", | |
| "target_size": 1024 | |
| } | |