Tomadoc Mythos

Training & Hardware Details

• Hardware Used: NVIDIA T4 GPU (Google Colab)
• Precision: Mixed Precision (FP16)
• Learning Rate: 2e-5
• Batch Size: 32
• Epochs: 5

Intended Uses & Limitations

This model is intended to classify leaf images of tomato plants into their respective healthy or diseased categories. It performs best when provided with clear, centered images of single leaves under decent lighting.

Evaluation Results

The model was evaluated on a dedicated test split from the filtered dataset:

• Final Test Accuracy: 0.9961

--- language: - en license: mit tags: - image-classification - vit - agriculture - tomato-diseases datasets: - nateraw/plant-village metrics: - accuracy pipeline_tag: image-classification --- # Tomadoc - Tomato Disease Classifier This model is a fine-tuned version of `google/vit-base-patch16-224` optimized for identifying diseases in tomato plant leaves. It was trained to serve as the backbone core for the Tomadoc application. ## Model Description - **Model Type:** Vision Transformer (ViT) - **Task:** Image Classification (10 Tomato classes) - **Base Model:** google/vit-base-patch16-224 - **Dataset:** PlantVillage (Tomato leaf subset containing healthy and diseased leaves) ## Training & Hardware Details - **Hardware Used:** NVIDIA T4 GPU (Google Colab) - **Precision:** Mixed Precision (FP16) - **Learning Rate:** 2e-5 - **Batch Size:** 32 - **Epochs:** 5 ## Intended Uses & Limitations This model is intended to classify leaf images of tomato plants into their respective healthy or diseased categories. It performs best when provided with clear, centered images of single leaves under decent lighting. ## Evaluation Results The model was evaluated on a dedicated test split from the filtered dataset: - **Final Test Accuracy:** 0.9961