Update README.md

README.md

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+
 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:
 - accuracy
 model-index:
 - name: fish_disease_datasets
-  results: []
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: fish_disease_datasets
+    metrics:
+      - name: Accuracy
+        type: accuracy
+        value: 0.9728
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+🐟 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)
 
-# fish_disease_datasets
+    Base model: google/vit-base-patch16-224-in21k
 
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the fish_disease_datasets dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0866
-- Accuracy: 0.9728
+    Dataset: Custom-labeled images of freshwater fish diseases
 
-## Model description
+    Data augmentation: Albumentations
 
-More information needed
+    Optimized for WhatsApp-based diagnosis tools
 
-## Intended uses & limitations
+🚜 Intended Use
 
-More information needed
+This model is optimized for:
 
-## Training and evaluation data
+    Farmers needing fast disease detection via image
 
-More information needed
+    WhatsApp or mobile-based advisory tools
 
-## Training procedure
+    NGO/hatchery/government pilots in India and South Asia
 
-### Training hyperparameters
+🏋️ Training Summary
 
-The following hyperparameters were used during training:
-- learning_rate: 0.0002
-- train_batch_size: 16
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 4
-- mixed_precision_training: Native AMP
+    Learning rate: 0.0002
 
-### Training results
+    Batch size: 16 (train) / 8 (eval)
 
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
-|:-------------:|:------:|:----:|:---------------:|:--------:|
-| 0.3865        | 0.7634 | 100  | 0.4161          | 0.8913   |
-| 0.1206        | 1.5267 | 200  | 0.2170          | 0.9457   |
-| 0.1132        | 2.2901 | 300  | 0.1317          | 0.9674   |
-| 0.0547        | 3.0534 | 400  | 0.0879          | 0.9810   |
-| 0.0209        | 3.8168 | 500  | 0.0866          | 0.9728   |
+    Epochs: 4
 
+    Mixed Precision: AMP
 
-### Framework versions
+    Framework: Hugging Face Transformers, PyTorch
 
-- Transformers 4.52.3
-- Pytorch 2.7.0+cu126
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+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