Instructions to use Skullly/Testing_purposes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skullly/Testing_purposes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Skullly/Testing_purposes") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Skullly/Testing_purposes") model = AutoModelForImageClassification.from_pretrained("Skullly/Testing_purposes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 2.496, | |
| "eval_accuracy": 0.9910666666666667, | |
| "eval_loss": 0.027157384902238846, | |
| "eval_runtime": 543.4232, | |
| "eval_samples_per_second": 55.206, | |
| "eval_steps_per_second": 1.726 | |
| } |