Instructions to use DamarJati/GreenLabel-Waste-Types with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DamarJati/GreenLabel-Waste-Types with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DamarJati/GreenLabel-Waste-Types") 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("DamarJati/GreenLabel-Waste-Types") model = AutoModelForImageClassification.from_pretrained("DamarJati/GreenLabel-Waste-Types", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from DamarJati/GreenLabel-Waste-Types: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
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https://huggingface.co/DamarJati/GreenLabel-Waste-Types/resolve/main/eval_results.json
- Command line
-
hf download hf://DamarJati/GreenLabel-Waste-Types/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/DamarJati/GreenLabel-Waste-Types/resolve/main/eval_results.json
190 Bytes
| { | |
| "epoch": 5.87, | |
| "eval_accuracy": 1.0, | |
| "eval_loss": 0.014997297897934914, | |
| "eval_runtime": 0.7188, | |
| "eval_samples_per_second": 111.294, | |
| "eval_steps_per_second": 6.956 | |
| } |