Instructions to use Thastp/rf-detr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Thastp/rf-detr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Thastp/rf-detr", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForObjectDetection model = AutoModelForObjectDetection.from_pretrained("Thastp/rf-detr", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Thastp/rf-detr: direct link, hf CLI and curl.
- Browser
- Download file 401 Bytes
-
https://huggingface.co/Thastp/rf-detr/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Thastp/rf-detr/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Thastp/rf-detr/resolve/main/preprocessor_config.json
401 Bytes
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_rf_detr.RFDetrImageProcessor" | |
| }, | |
| "config": { | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ] | |
| }, | |
| "image_processor_type": "RFDetrImageProcessor", | |
| "model_name": "RFDETRBase", | |
| "post_process_config": { | |
| "num_select": 300 | |
| } | |
| } | |