🚗 Honda Accord vs. Toyota RAV4 (2014-2017) Classifier (My 1st 'hello world' ML project)
Model Description
This is an image classification model built using the PyTorch framework. Its primary function is to perform binary classification on car images, specifically differentiating between the Honda Accord and the Toyota RAV4 from the 2014 to 2017 model years.
The model takes a car image as input and outputs a prediction indicating whether the vehicle is a Honda Accord or a Toyota RAV4. Model trained with supervised learning
Intended Use
This model is intended for research and demonstration purposes in computer vision and car make/model classification.
Key Use Case:
- Differentiating between images of the 2014-2017 Honda Accord and the 2014-2017 Toyota RAV4.
- It is not optimized for general car classification beyond these two specific models and year ranges. (yet)
Training Details
| Parameter | Value |
|---|---|
| Framework | PyTorch |
| Total Epochs Trained | 100 |
| Best Performing Model Weights | Epoch 8 |
The model was trained for a total of 100 epochs. The weights corresponding to the best performance (e.g., highest validation accuracy or lowest validation loss) were achieved and saved after the 8th epoch.
Dataset
The model was trained on a curated subset of images from the publicly available car dataset on Hugging Face:
- Dataset Source: rosjerry/cars
Data scraped from copart.com (Do not report my dataset please)
The images used for training were filtered and labeled to specifically include and distinguish between the Honda Accord and the Toyota RAV4 within the specified 2014-2017 year range.
GitHub Repository
- Model Source Repository rosjerry/image-labeling-python
Use Readme from repository to train model yourself, use different config if you want.
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