Instructions to use mrsarthakgupta/baseonnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrsarthakgupta/baseonnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="mrsarthakgupta/baseonnx") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForZeroShotImageClassification processor = AutoImageProcessor.from_pretrained("mrsarthakgupta/baseonnx") model = AutoModelForZeroShotImageClassification.from_pretrained("mrsarthakgupta/baseonnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from mrsarthakgupta/baseonnx: direct link, hf CLI and curl.
- Browser
- Download file 336 Bytes
-
https://huggingface.co/mrsarthakgupta/baseonnx/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://mrsarthakgupta/baseonnx/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/mrsarthakgupta/baseonnx/resolve/main/preprocessor_config.json
336 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48, | |
| 0.46, | |
| 0.41 | |
| ], | |
| "image_processor_type": "ViTFeatureExtractor", | |
| "image_std": [ | |
| 0.27, | |
| 0.26, | |
| 0.28 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } |