Instructions to use hf-internal-testing/tiny-random-ConditionalDetrModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConditionalDetrModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-ConditionalDetrModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ConditionalDetrModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ConditionalDetrModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-ConditionalDetrModel: direct link, hf CLI and curl.
- Browser
- Download file 285 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-ConditionalDetrModel/resolve/920d12d8247f043c658ce32e06c873bd87d8067e/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-ConditionalDetrModel@920d12d8247f043c658ce32e06c873bd87d8067e/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-ConditionalDetrModel/resolve/920d12d8247f043c658ce32e06c873bd87d8067e/preprocessor_config.json
285 Bytes
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ConditionalDetrFeatureExtractor", | |
| "format": "coco_detection", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "max_size": 1333, | |
| "size": 800 | |
| } | |