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Tomadoc Mythos

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.

KindOtherVersionRevision 8c74865

Model source

Kind
Other
File size
327 MB
Version
vRevision 8c74865
Updated
Sep 17, 2026
Downloads
81
Source
Hugging Face

Source description

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

Sources

1 source
Hugging FacePrimaryVersion 8c74865634528735acca8c750a8be2d36d6d3081
huggingface.co/oshriagronov/tomadoc-mythos/tree/8c74865634528735acca8c750a8be2d36d6d3081

Verified Sep 17

Model artifacts

1 artifact

model.safetensors

safetensors · 327 MB · SHA-256 f173d9e1ba54…b932 · Hugging Face

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Source excerpts

2 excerpts

--- 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

Source context: 81 downloads · 0 likes · Pipeline image-classification · Repo oshriagronov/tomadoc-mythos