Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use digo-prayudha/vit-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digo-prayudha/vit-emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="digo-prayudha/vit-emotion-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("digo-prayudha/vit-emotion-classification") model = AutoModelForImageClassification.from_pretrained("digo-prayudha/vit-emotion-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from digo-prayudha/vit-emotion-classification: direct link, hf CLI and curl.
- Browser
- Download file 192 Bytes
-
https://huggingface.co/digo-prayudha/vit-emotion-classification/resolve/main/eval_results.json
- Command line
-
hf download hf://digo-prayudha/vit-emotion-classification/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/digo-prayudha/vit-emotion-classification/resolve/main/eval_results.json
192 Bytes
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.6125, | |
| "eval_loss": 1.3801825046539307, | |
| "eval_runtime": 0.9514, | |
| "eval_samples_per_second": 168.179, | |
| "eval_steps_per_second": 21.022 | |
| } |