README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - vision-transformer
  - aquaculture
  - fish-disease
  - generated_from_trainer
metrics:
  - name: accuracy
    type: accuracy
    value: 0.9728
model-index:
  - name: fish_disease_datasets
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: fish_disease_datasets
          type: image
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9728

🐟 Fish Disease Classifier (ViT)

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. ✅ Detected Classes (Fish)

Bacterial Red Disease

Bacterial diseases – Aeromoniasis

Bacterial Gill Disease

Fungal diseases (Saprolegniasis)

Parasitic diseases

Viral diseases (White Tail Disease)

Healthy Fish

⚠️ Planned Prawn Model (Upcoming)

We are currently working on a separate fine-tuned model to detect:

Bacterial Gill Disease (BG)

White Spot Syndrome Virus (WSSV)

Healthy Prawn

This model will be released in the next version once prawn dataset collection and training is complete. 📊 Evaluation Metrics Metric Value Accuracy 97.28% Validation Loss 0.0866 Final Epoch 4 🧠 Model Description

Architecture: Vision Transformer (ViT)

Base model: google/vit-base-patch16-224-in21k

Dataset: Custom-labeled images of freshwater fish diseases

Data augmentation: Albumentations

Optimized for WhatsApp-based diagnosis tools

🚜 Intended Use

This model is optimized for:

Farmers needing fast disease detection via image

WhatsApp or mobile-based advisory tools

NGO/hatchery/government pilots in India and South Asia

🏋️ Training Summary

Learning rate: 0.0002

Batch size: 16 (train) / 8 (eval)

Epochs: 4

Mixed Precision: AMP

Framework: Hugging Face Transformers, PyTorch

🏋️ Training Results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3865	0.76	100	0.4161	0.8913
0.1206	1.53	200	0.2170	0.9457
0.1132	2.29	300	0.1317	0.9674
0.0547	3.05	400	0.0879	0.9810
0.0209	3.81	500	0.0866	0.9728