Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use autoevaluate/image-multi-class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/image-multi-class-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="autoevaluate/image-multi-class-classification") 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("autoevaluate/image-multi-class-classification") model = AutoModelForImageClassification.from_pretrained("autoevaluate/image-multi-class-classification") - Notebooks
- Google Colab
- Kaggle
Abhishek Thakur commited on
Commit ·
f8e63f7
1
Parent(s): e014e00
Add evaluation results on mnist
Browse filesBeep boop, I am a bot from Hugging Face's automatic evaluation service! Your model has been evaluated on the [mnist](https://huggingface.co/datasets/mnist) dataset, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-1cd37ed6-b663-40fc-b2e7-0e90c529b179-223264). Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mnist). Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/autoevaluate?dataset=mnist).