Instructions to use Andron00e/ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andron00e/ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Andron00e/ViTForImageClassification") 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("Andron00e/ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("Andron00e/ViTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Andron00e/ViTForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 209 Bytes
-
https://huggingface.co/Andron00e/ViTForImageClassification/resolve/main/train_results.json
- Command line
-
hf download hf://Andron00e/ViTForImageClassification/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Andron00e/ViTForImageClassification/resolve/main/train_results.json
209 Bytes
| { | |
| "epoch": 4.0, | |
| "total_flos": 1.4879528813592576e+19, | |
| "train_loss": 0.11711712082227071, | |
| "train_runtime": 8681.751, | |
| "train_samples_per_second": 22.115, | |
| "train_steps_per_second": 0.173 | |
| } |