| | --- |
| | 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 |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # 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 | |
| | |
