Instructions to use Moreza009/HF_CVcourse_FoodClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Moreza009/HF_CVcourse_FoodClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Moreza009/HF_CVcourse_FoodClassifier") 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("Moreza009/HF_CVcourse_FoodClassifier") model = AutoModelForImageClassification.from_pretrained("Moreza009/HF_CVcourse_FoodClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Moreza009/HF_CVcourse_FoodClassifier: direct link, hf CLI and curl.
- Browser
- Download file 218 Bytes
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https://huggingface.co/Moreza009/HF_CVcourse_FoodClassifier/resolve/main/eval_results.json
- Command line
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hf download hf://Moreza009/HF_CVcourse_FoodClassifier/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/Moreza009/HF_CVcourse_FoodClassifier/resolve/main/eval_results.json
218 Bytes
| { | |
| "epoch": 1.9971844204598779, | |
| "eval_accuracy": 0.7506270627062707, | |
| "eval_loss": 0.9383056163787842, | |
| "eval_runtime": 71.9354, | |
| "eval_samples_per_second": 105.303, | |
| "eval_steps_per_second": 3.295 | |
| } |