Instructions to use Andron00e/ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andron00e/ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Andron00e/ViTForImageClassification") 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("Andron00e/ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("Andron00e/ViTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Andron00e/ViTForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 518 Bytes
-
https://huggingface.co/Andron00e/ViTForImageClassification/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Andron00e/ViTForImageClassification/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Andron00e/ViTForImageClassification/resolve/main/preprocessor_config.json
518 Bytes
| { | |
| "crop_size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "CLIPImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 224 | |
| } | |
| } | |