Instructions to use Diginsa/Plant-Disease-Detection-Project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Diginsa/Plant-Disease-Detection-Project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Diginsa/Plant-Disease-Detection-Project") 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("Diginsa/Plant-Disease-Detection-Project") model = AutoModelForImageClassification.from_pretrained("Diginsa/Plant-Disease-Detection-Project", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from Diginsa/Plant-Disease-Detection-Project: direct link, hf CLI and curl.
- Browser
- Download file 211 Bytes
-
https://huggingface.co/Diginsa/Plant-Disease-Detection-Project/resolve/main/all_results.json
- Command line
-
hf download hf://Diginsa/Plant-Disease-Detection-Project/all_results.json
-
curl -L -o all_results.json https://huggingface.co/Diginsa/Plant-Disease-Detection-Project/resolve/main/all_results.json
211 Bytes
| { | |
| "epoch": 5.99, | |
| "total_flos": 2.353803972968448e+18, | |
| "train_loss": 0.48255133992552973, | |
| "train_runtime": 7781.6451, | |
| "train_samples_per_second": 113.054, | |
| "train_steps_per_second": 0.282 | |
| } |