Instructions to use asnelt/mnistvit-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asnelt/mnistvit-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="asnelt/mnistvit-transformers", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("asnelt/mnistvit-transformers", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from asnelt/mnistvit-transformers: direct link, hf CLI and curl.
- Browser
- Download file 221 Bytes
-
https://huggingface.co/asnelt/mnistvit-transformers/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://asnelt/mnistvit-transformers/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/asnelt/mnistvit-transformers/resolve/main/preprocessor_config.json
221 Bytes
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_mnistvit.MnistViTImageProcessor" | |
| }, | |
| "image_processor_type": "MnistViTImageProcessor", | |
| "size": 28, | |
| "normalization": "image", | |
| "do_auto_invert": true | |
| } | |