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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 datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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 |
π 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. β Detected Classes (Fish)
Bacterial Red Disease
Bacterial diseases β Aeromoniasis
Bacterial Gill Disease
Fungal diseases (Saprolegniasis)
Parasitic diseases
Viral diseases (White Tail Disease)
Healthy Fish
β οΈ 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. π Evaluation Metrics Metric Value Accuracy 97.28% Validation Loss 0.0866 Final Epoch 4 π§ 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
π 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
ποΈ Training Summary
Learning rate: 0.0002
Batch size: 16 (train) / 8 (eval)
Epochs: 4
Mixed Precision: AMP
Framework: Hugging Face Transformers, PyTorch
ποΈ 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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