Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use OTrain/vit-puppers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OTrain/vit-puppers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="OTrain/vit-puppers") 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("OTrain/vit-puppers") model = AutoModelForImageClassification.from_pretrained("OTrain/vit-puppers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
vit-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
Example Images
dog doing nothing
dog lying down
dog shaking hands
dog sitting
dog standing
- Downloads last month
- 6
Evaluation results
- Accuracyself-reported0.764




