Instructions to use PekingU/rtdetr_v2_r101vd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PekingU/rtdetr_v2_r101vd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="PekingU/rtdetr_v2_r101vd")# Load model directly from transformers import AutoTokenizer, RtDetrV2ForObjectDetection tokenizer = AutoTokenizer.from_pretrained("PekingU/rtdetr_v2_r101vd") model = RtDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r101vd") - Notebooks
- Google Colab
- Kaggle
Add image processor from convert_rt_detr_v2_original_pytorch_checkpoint_to_pytorch.py
d6e6b71 verified | { | |
| "do_convert_annotations": true, | |
| "do_normalize": false, | |
| "do_pad": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "format": "coco_detection", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "RTDetrImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "pad_size": null, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 640, | |
| "width": 640 | |
| } | |
| } | |