Instructions to use MTWD/detr-resnet-50-brain-hack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MTWD/detr-resnet-50-brain-hack with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="MTWD/detr-resnet-50-brain-hack")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("MTWD/detr-resnet-50-brain-hack") model = AutoModelForObjectDetection.from_pretrained("MTWD/detr-resnet-50-brain-hack", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from MTWD/detr-resnet-50-brain-hack: direct link, hf CLI and curl.
- Browser
- Download file 816 Bytes
-
https://huggingface.co/MTWD/detr-resnet-50-brain-hack/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://MTWD/detr-resnet-50-brain-hack/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/MTWD/detr-resnet-50-brain-hack/resolve/main/preprocessor_config.json
816 Bytes
| { | |
| "_valid_processor_keys": [ | |
| "images", | |
| "annotations", | |
| "return_segmentation_masks", | |
| "masks_path", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_normalize", | |
| "do_convert_annotations", | |
| "image_mean", | |
| "image_std", | |
| "do_pad", | |
| "format", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "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 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 1333, | |
| "shortest_edge": 800 | |
| } | |
| } | |