Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use oschamp/vit-artworkclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oschamp/vit-artworkclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="oschamp/vit-artworkclassifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("oschamp/vit-artworkclassifier") model = AutoModelForImageClassification.from_pretrained("oschamp/vit-artworkclassifier") - Notebooks
- Google Colab
- Kaggle
Why is there only 9 classes in config?
#3
by hiyuchang - opened
Is there a missed class "ukiyo_e"?
Yes, there is. When I made this I was working in a hackathon and had limited time, and I couldn't find enough data for ukiyo_e initially
I believe lots of the ukiyo_e images were of a format other than standard RGB arrays when I downloaded the data, but I didn't have to troubleshoot the issue and simply left the class out of my train and validation sets.
I believe lots of the ukiyo_e images were of a format other than standard RGB arrays when I downloaded the data, but I didn't have to troubleshoot the issue and simply left the class out of my train and validation sets.
Got it. Thank you for your reply.
oschamp changed discussion status to closed