In this blog post, we will guide you through the process of implementing car recognition technology by fine-tuning a ResNet-152 model using the Cars Dataset from Stanford. Using modern deep learning frameworks, we will explore the steps required to train, validate, and test our model effectively.
Prerequisites: What You Need
Before you dive into the implementation, ensure you have the following dependencies installed:
The Dataset: Understanding Our Data
We utilize the Cars Dataset, which consists of 16,185 images showcasing 196 different classes of cars. This dataset is split into 8,144 training images and 8,041 testing images, with an approximate 50-50 split for each class.
You can obtain the dataset from the Cars Dataset.
Getting Started: Data Acquisition
To start using the dataset, you’ll need to download it. Open your terminal and execute the following commands:
bash
$ cd Car-Recognition
$ wget http://imagenet.stanford.edu/internal/car196/cars_train.tgz
$ wget http://imagenet.stanford.edu/internal/car196/cars_test.tgz
$ wget --no-check-certificate https://ai.stanford.edu/~jkrause/cars/car_devkit.tgz
Fine-Tuning the Model
Next, we’ll utilize a pretrained ResNet-152 model to enhance our car recognition capability.
Start by downloading the pretrained model into your models folder:
bash
Download [ResNet-152](https://drive.google.com/file/d/0Byy2AcGyEVxfeXExMzNN.../view?usp=sharing) into models folder.
Data Pre-processing
To ensure our model is trained effectively, we will preprocess our images. This includes extracting 8,144 training images and splitting them using an 80:20 rule (6,515 for training, 1,629 for validation). Run the following command:
bash
$ python pre_process.py
Training the Model
With our data prepared, we can now train the model. Run the following command:
bash
$ python train.py
If you’d like to visualize the training process, use the following command in a new terminal:
bash
$ tensorboard --logdir path_to_current_dir/logs
Analysis and Validation
Once training is complete, it’s time to analyze our model. Update model_weights_path in the utils.py file with your best model path, and utilize the validation images for analysis by executing the following command:
bash
$ python analyze.py
Our model boasts a validation accuracy of 88.70%!
Testing the Model
To evaluate our model on test data (8,041 images), run:
bash
$ python test.py
The evaluation result shows an impressive test accuracy of 88.88%!
Running a Demo
If you’d like to see the model in action with a pre-trained model, execute the following:
bash
$ python demo.py --i [image_path]
If no argument is provided, a sample image will be used.
Troubleshooting Tips
While implementing the car recognition model, you may face some common issues. Here are a few troubleshooting ideas:
- If the model is not training as expected, ensure that all dependencies are correctly installed.
- Check the dataset path and ensure that the dataset was extracted properly.
- For visualization issues, confirm that TensorBoard is pointed to the right log directory.
- If you’re encountering memory issues, consider reducing the batch size during training.
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Conclusion
By following these steps, you should now be able to implement a car recognition system using the ResNet-152 model efficiently. This hands-on project enhances your machine learning skills while providing practical experience in working with deep learning models.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.