Tomadoc Flash
This model is a fine-tuned version of google/vit-base-patch16-224 designed to identify the exact ripening stage of a tomato. It was trained on the extensive FreshCheck dataset to provide highly accurate agricultural...
Model source
- Kind
- Other
- File size
- 327 MB
- Version
- vRevision e5f5ce2
- Updated
- Sep 17, 2026
- Downloads
- 8
- Source
- Hugging Face
Source description
Tomadoc Advanced Tomato Ripeness Classifier
This model is a fine-tuned version of google/vit-base-patch16-224 designed to identify the exact ripening stage of a tomato. It was trained on the extensive FreshCheck dataset to provide highly accurate agricultural insights for the Tomadoc application.
Model Description
- Model Type: Vision Transformer (ViT)
- Task: Image Classification
- Classes: Unripe, Ripe, Overripe, Damaged
- Base Model: google/vit-base-patch16-224
Training & Hardware Details
- Dataset: FreshCheck Fruit Ripeness Classification Dataset
- Hardware Used: NVIDIA T4 GPU
- Precision: Mixed Precision (FP16)
- Evaluation Strategy: Epoch-based
- Learning Rate: 2e-5
- Epochs: 5
Evaluation Results
- Final Test Accuracy: 0.9983
Sources
1 source
Sources
1 sourceVerified Sep 17
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- language: - en license: mit tags: - image-classification - vit - agriculture - tomato-ripeness - freshcheck metrics: - accuracy pipeline_tag: image-classification --- # Tomadoc Advanced Tomato Ripeness Classifier This model is a fine-tuned version of `google/vit-base-patch16-224` designed to identify the exact ripening stage of a tomato. It was trained on the extensive FreshCheck dataset to provide highly accurate agricultural insights for the Tomadoc application. ## Model Description - **Model Type:** Vision Transformer (ViT) - **Task:** Image Classification - **Classes:** Unripe, Ripe, Overripe, Damaged - **Base Model:** google/vit-base-patch16-224 ## Training & Hardware Details - **Dataset:** FreshCheck Fruit Ripeness Classification Dataset - **Hardware Used:** NVIDIA T4 GPU - **Precision:** Mixed Precision (FP16) - **Evaluation Strategy:** Epoch-based - **Learning Rate:** 2e-5 - **Epochs:** 5 ## Evaluation Results - **Final Test Accuracy:** 0.9983
Source context: 8 downloads · 0 likes · Pipeline image-classification · Repo oshriagronov/tomadoc-flash
