Instructions to use glopez/cifar-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use glopez/cifar-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="glopez/cifar-10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("glopez/cifar-10") model = AutoModelForImageClassification.from_pretrained("glopez/cifar-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from glopez/cifar-10: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/glopez/cifar-10/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://glopez/cifar-10/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/glopez/cifar-10/resolve/main/preprocessor_config.json
228 Bytes
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ViTFeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "size": 224 | |
| } | |