Image Classification
Transformers
Safetensors
vit
image-classification, screenshots detection
Generated from Trainer
Instructions to use al-css/Screenshots_detection_to_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use al-css/Screenshots_detection_to_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="al-css/Screenshots_detection_to_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/Screenshots_detection_to_classification") model = AutoModelForImageClassification.from_pretrained("al-css/Screenshots_detection_to_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from al-css/Screenshots_detection_to_classification: direct link, hf CLI and curl.
- Browser
- Download file 208 Bytes
-
https://huggingface.co/al-css/Screenshots_detection_to_classification/resolve/main/eval_results.json
- Command line
-
hf download hf://al-css/Screenshots_detection_to_classification/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/al-css/Screenshots_detection_to_classification/resolve/main/eval_results.json
208 Bytes
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 0.9880952380952381, | |
| "eval_loss": 0.11922617256641388, | |
| "eval_runtime": 9.0858, | |
| "eval_samples_per_second": 9.245, | |
| "eval_steps_per_second": 1.211 | |
| } |