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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from autoevaluate/image-multi-class-classification: direct link, hf CLI and curl.
- Browser
- Download file 204 Bytes
-
https://huggingface.co/autoevaluate/image-multi-class-classification/resolve/main/eval_results.json
- Command line
-
hf download hf://autoevaluate/image-multi-class-classification/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/autoevaluate/image-multi-class-classification/resolve/main/eval_results.json
204 Bytes
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.9833333333333333, | |
| "eval_loss": 0.05558411777019501, | |
| "eval_runtime": 38.4928, | |
| "eval_samples_per_second": 155.873, | |
| "eval_steps_per_second": 4.884 | |
| } |