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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- image-classification |
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- vision-transformer |
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- aquaculture |
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- fish-disease |
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- generated_from_trainer |
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metrics: |
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- name: accuracy |
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type: accuracy |
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value: 0.9728 |
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model-index: |
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- name: fish_disease_datasets |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: fish_disease_datasets |
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type: image |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9728 |
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--- |
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🐟 Fish Disease Classifier (ViT) |
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This model is a fine-tuned version of google/vit-base-patch16-224-in21k, trained on a custom fish disease image dataset for Indian aquaculture. |
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✅ Detected Classes (Fish) |
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Bacterial Red Disease |
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Bacterial diseases – Aeromoniasis |
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Bacterial Gill Disease |
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Fungal diseases (Saprolegniasis) |
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Parasitic diseases |
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Viral diseases (White Tail Disease) |
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Healthy Fish |
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⚠️ Planned Prawn Model (Upcoming) |
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We are currently working on a separate fine-tuned model to detect: |
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Bacterial Gill Disease (BG) |
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White Spot Syndrome Virus (WSSV) |
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Healthy Prawn |
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This model will be released in the next version once prawn dataset collection and training is complete. |
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📊 Evaluation Metrics |
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Metric Value |
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Accuracy 97.28% |
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Validation Loss 0.0866 |
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Final Epoch 4 |
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🧠 Model Description |
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Architecture: Vision Transformer (ViT) |
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Base model: google/vit-base-patch16-224-in21k |
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Dataset: Custom-labeled images of freshwater fish diseases |
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Data augmentation: Albumentations |
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Optimized for WhatsApp-based diagnosis tools |
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🚜 Intended Use |
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This model is optimized for: |
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Farmers needing fast disease detection via image |
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WhatsApp or mobile-based advisory tools |
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NGO/hatchery/government pilots in India and South Asia |
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🏋️ Training Summary |
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Learning rate: 0.0002 |
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Batch size: 16 (train) / 8 (eval) |
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Epochs: 4 |
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Mixed Precision: AMP |
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Framework: Hugging Face Transformers, PyTorch |
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### 🏋️ Training Results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|---------------|-------|------|-----------------|----------| |
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| 0.3865 | 0.76 | 100 | 0.4161 | 0.8913 | |
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| 0.1206 | 1.53 | 200 | 0.2170 | 0.9457 | |
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| 0.1132 | 2.29 | 300 | 0.1317 | 0.9674 | |
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| 0.0547 | 3.05 | 400 | 0.0879 | 0.9810 | |
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| 0.0209 | 3.81 | 500 | 0.0866 | 0.9728 | |
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