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 all_results.json from al-css/Screenshots_detection_to_classification: direct link, hf CLI and curl.
- Browser
- Download file 400 Bytes
-
https://huggingface.co/al-css/Screenshots_detection_to_classification/resolve/main/all_results.json
- Command line
-
hf download hf://al-css/Screenshots_detection_to_classification/all_results.json
-
curl -L -o all_results.json https://huggingface.co/al-css/Screenshots_detection_to_classification/resolve/main/all_results.json
400 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, | |
| "total_flos": 1.0290936220817818e+17, | |
| "train_loss": 0.11647429920378186, | |
| "train_runtime": 188.7378, | |
| "train_samples_per_second": 7.036, | |
| "train_steps_per_second": 0.89 | |
| } |