Image Classification
Transformers
TensorBoard
Safetensors
deit
Generated from Trainer
Eval Results (legacy)
Instructions to use BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5") 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("BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5") model = AutoModelForImageClassification.from_pretrained("BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5: direct link, hf CLI and curl.
- Browser
- Download file 214 Bytes
-
https://huggingface.co/BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5/resolve/main/eval_results.json
- Command line
-
hf download hf://BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold5/resolve/main/eval_results.json
214 Bytes
| { | |
| "epoch": 85.71428571428571, | |
| "eval_accuracy": 0.8227848101265823, | |
| "eval_loss": 0.645160436630249, | |
| "eval_runtime": 1.1931, | |
| "eval_samples_per_second": 66.215, | |
| "eval_steps_per_second": 2.514 | |
| } |