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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
epoch: double
eval_accuracy: double
eval_loss: double
eval_runtime: double
eval_samples_per_second: double
eval_steps_per_second: double
train_loss: double
total_flos: double
train_samples_per_second: double
train_runtime: double
train_steps_per_second: double
to
{'epoch': Value('float64'), 'total_flos': Value('float64'), 'train_loss': Value('float64'), 'train_runtime': Value('float64'), 'train_samples_per_second': Value('float64'), 'train_steps_per_second': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              epoch: double
              eval_accuracy: double
              eval_loss: double
              eval_runtime: double
              eval_samples_per_second: double
              eval_steps_per_second: double
              train_loss: double
              total_flos: double
              train_samples_per_second: double
              train_runtime: double
              train_steps_per_second: double
              to
              {'epoch': Value('float64'), 'total_flos': Value('float64'), 'train_loss': Value('float64'), 'train_runtime': Value('float64'), 'train_samples_per_second': Value('float64'), 'train_steps_per_second': Value('float64')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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epoch
float64
total_flos
float64
train_loss
float64
train_runtime
float64
train_samples_per_second
float64
train_steps_per_second
float64
4
645,382,209,997,357,000
0.25741
180.8127
46.059
2.898

🐟 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. βœ… 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. πŸ“Š 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

πŸ‹οΈ Training Summary

Learning rate: 0.0002

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

Epochs: 4

Mixed Precision: AMP

Framework: Hugging Face Transformers, PyTorch

πŸ‹οΈ 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
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