Instructions to use tangg555/clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tangg555/clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tangg555/clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("tangg555/clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist") model = AutoModelForImageClassification.from_pretrained("tangg555/clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81db248506f0e287e1fffe4a2a46991b53bddbea22ea8978eb965d00bc55d93b
- Size of remote file:
- 5.24 kB
- SHA256:
- e70f226bb79a1ad7e11fbafee3ed866b95dba8361fcc753e31eae69dbded1193
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.