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Model: Grain Classification Model
Description:
This model is design to classify 4 different types of grains: noodles, rice, couscous, and oatmeal by using the FastAI library with Fastbook and ResNet-18 convolutional neural network architecture.
The model files can be found in the "Files and Versions" section.
Training Data
- Limited dataset of 400 labeled images of grains, with 100 images for each category.
- Obtained using DuckDuckGo Images API
- Resized to 128x128 pixels to reduce storage and computation usage.
- 3 additional training epochs are performed to fine-tune the model for grain classification task.
Metrics
Model performance are evaluated using confusion matrix. Confusion matrix measures the metric of accuracy and precision. The prediction is in 4 categories:
- True positive: Model correctly predicted positive class.
- False Positive: Model incorrectly predicted when actual is negative class.
- True Negative: Model correctly predict negative class.
- False Negative: Model incorrectly predict negative class when actual is positive.
Results
In the images below, you can see that there are different shades.
- The darker shade represents correct predictions
- The lighter shade represents incorrect predictions.
First training: | Second training: |
We can see that the model has improved after cleaning the data.
- it improved in predicting the images correctly.
- there is better accuracy in the second training compared to the first training. The number of darker shades is higher.
- there is less confusion in the second training compared to the first training. The number of lighter shades is lower.
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