Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use surprisedPikachu007/tomato-disease-detection_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surprisedPikachu007/tomato-disease-detection_V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="surprisedPikachu007/tomato-disease-detection_V2") 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("surprisedPikachu007/tomato-disease-detection_V2") model = AutoModelForImageClassification.from_pretrained("surprisedPikachu007/tomato-disease-detection_V2") - Notebooks
- Google Colab
- Kaggle
tomato-disease-detection_V2
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0483
- Accuracy: 0.9887
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1585 | 1.0 | 949 | 0.1547 | 0.9663 |
| 0.1129 | 2.0 | 1898 | 0.0773 | 0.9826 |
| 0.0223 | 3.0 | 2847 | 0.0483 | 0.9887 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
- Downloads last month
- 3
Evaluation results
- Accuracy on imagefolderself-reported0.989