Keypoint Detection
Transformers
Safetensors
LightGlue
keypoint-matching
model_hub_mixin
pytorch_model_hub_mixin
Instructions to use ETH-CVG/lightglue_disk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ETH-CVG/lightglue_disk with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForKeypointMatching processor = AutoImageProcessor.from_pretrained("ETH-CVG/lightglue_disk") model = AutoModelForKeypointMatching.from_pretrained("ETH-CVG/lightglue_disk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from ETH-CVG/lightglue_disk: direct link, hf CLI and curl.
- Browser
- Download file 235 Bytes
-
https://huggingface.co/ETH-CVG/lightglue_disk/resolve/refs%2Fpr%2F2/preprocessor_config.json
- Command line
-
hf download hf://ETH-CVG/lightglue_disk@refs/pr/2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ETH-CVG/lightglue_disk/resolve/refs%2Fpr%2F2/preprocessor_config.json
235 Bytes
| { | |
| "do_grayscale": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_processor_type": "LightGlueImageProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 480, | |
| "width": 640 | |
| } | |
| } | |