Image Classification
Transformers
TensorBoard
Safetensors
deit
Generated from Trainer
Eval Results (legacy)
Instructions to use BilalMuftuoglu/deit-base-distilled-patch16-224-85-fold4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BilalMuftuoglu/deit-base-distilled-patch16-224-85-fold4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BilalMuftuoglu/deit-base-distilled-patch16-224-85-fold4") 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("BilalMuftuoglu/deit-base-distilled-patch16-224-85-fold4") model = AutoModelForImageClassification.from_pretrained("BilalMuftuoglu/deit-base-distilled-patch16-224-85-fold4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21987d29ab7927e9ae6804d4722de2e41dacc3b4c1db873430155ca541704e22
- Size of remote file:
- 5.18 kB
- SHA256:
- 8e44f457ab177d97c6fa802aa7741e4d7096ea5f23cbb6a9169d3b8f3eff8a7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.