Instructions to use Alan04020/vit-plant-disease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alan04020/vit-plant-disease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Alan04020/vit-plant-disease") 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("Alan04020/vit-plant-disease") model = AutoModelForImageClassification.from_pretrained("Alan04020/vit-plant-disease", device_map="auto") - Notebooks
- Google Colab
- Kaggle
vit-plant-disease
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0230
- Accuracy: 0.9949
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7876 | 1.0 | 25 | 0.1737 | 0.9538 |
| 0.0816 | 2.0 | 50 | 0.0704 | 0.9641 |
| 0.0165 | 3.0 | 75 | 0.0628 | 0.9692 |
| 0.0051 | 4.0 | 100 | 0.0625 | 0.9744 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for Alan04020/vit-plant-disease
Base model
google/vit-base-patch16-224Space using Alan04020/vit-plant-disease 1
Evaluation results
- Accuracy on imagefoldervalidation set self-reported0.995