Image Classification
Transformers
Safetensors
vit
image-classification, faces-recognition
Generated from Trainer
Instructions to use al-css/faces_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use al-css/faces_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="al-css/faces_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("al-css/faces_classification") model = AutoModelForImageClassification.from_pretrained("al-css/faces_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from al-css/faces_classification: direct link, hf CLI and curl.
- Browser
- Download file 210 Bytes
-
https://huggingface.co/al-css/faces_classification/resolve/main/eval_results.json
- Command line
-
hf download hf://al-css/faces_classification/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/al-css/faces_classification/resolve/main/eval_results.json
210 Bytes
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.5480769230769231, | |
| "eval_loss": 2.2189159393310547, | |
| "eval_runtime": 20.1264, | |
| "eval_samples_per_second": 10.335, | |
| "eval_steps_per_second": 1.292 | |
| } |