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---
datasets:
- rice
metrics:
- accuracy
model-index:
- name: rice_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: rice
type: rice
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9768
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# image_classification
This model is a CNN model on the rice dataset to classify rice into 5 classes (Arborio, Basmati, Ipsala, Jasmine and Karacadag).
It achieves the following results on the evaluation set:
- Loss: 0.0116
- Accuracy: 0.9768
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- optimizer: Adam
- num_epochs: 5
### Training results
| Epoch | Loss | Accuracy |
|:-----:|:------:|:--------:|
| 1.0 | 0.0510 | 0.9363 |
| 2.0 | 0.0099 | 0.9695 |
| 3.0 | 0.5962 | 0.9767 |
| 4.0 | 0.4232 | 0.9828 |
| 5.0 | 0.0011 | 0.9859 |
|