Image Classification
Transformers
Safetensors
English
custom_vit_nano
vit
nano
patch16
img224
custom_code
Instructions to use kd13/vit-nano-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/vit-nano-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kd13/vit-nano-patch16-224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("kd13/vit-nano-patch16-224", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from kd13/vit-nano-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 328 Bytes
-
https://huggingface.co/kd13/vit-nano-patch16-224/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://kd13/vit-nano-patch16-224/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/kd13/vit-nano-patch16-224/resolve/main/preprocessor_config.json
328 Bytes
| { | |
| "image_processor_type": "ViTImageProcessor", | |
| "do_resize": true, | |
| "size": {"shortest_edge": 256}, | |
| "do_center_crop": true, | |
| "crop_size": {"height": 224, "width": 224}, | |
| "do_rescale": true, | |
| "rescale_factor": 0.00392156862745098, | |
| "do_normalize": true, | |
| "image_mean": [0.5, 0.5, 0.5], | |
| "image_std": [0.5, 0.5, 0.5] | |
| } |