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
File size: 214 Bytes
c993023 | 1 2 3 4 5 6 7 8 | {
"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
} |