Instructions to use rafaelpadilla/porting_rt_detr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rafaelpadilla/porting_rt_detr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rafaelpadilla/porting_rt_detr")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("rafaelpadilla/porting_rt_detr") model = AutoModel.from_pretrained("rafaelpadilla/porting_rt_detr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from rafaelpadilla/porting_rt_detr: direct link, hf CLI and curl.
- Browser
- Download file 207 Bytes
-
https://huggingface.co/rafaelpadilla/porting_rt_detr/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://rafaelpadilla/porting_rt_detr/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/rafaelpadilla/porting_rt_detr/resolve/main/preprocessor_config.json
207 Bytes
| { | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_processor_type": "RTDetrImageProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 640, | |
| "width": 640 | |
| } | |
| } | |