Instructions to use harness-race/pi-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harness-race/pi-r2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="harness-race/pi-r2")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("harness-race/pi-r2") model = AutoModelForObjectDetection.from_pretrained("harness-race/pi-r2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
davanstrien HF Staff
Train DETR-R50 on loc_beyond_words: final model + eval metrics
591127b verified Download preprocessor_config.json from harness-race/pi-r2: direct link, hf CLI and curl.
- Browser
- Download file 454 Bytes
-
https://huggingface.co/harness-race/pi-r2/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://harness-race/pi-r2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/harness-race/pi-r2/resolve/main/preprocessor_config.json
454 Bytes
| { | |
| "do_convert_annotations": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "format": "coco_detection", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "DetrImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "pad_size": null, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 800, | |
| "shortest_edge": 480 | |
| } | |
| } | |