Car Classification using Inception-v3

Car Classification using Inception-v3

Car Classification using Inception-v3. Article on training 3 models to classify the Make, Model and Year of a car using Monk and deploying them through a Flask API

Introduction

This article is about training 3 deep convolutional neural networks using Monk, which is an open source library for computer vision, and then deploying them through an API. The models take an image of a car as the input and then predict the Make, Model and Year of the car. The models have been trained on the Cars Dataset.

For transfer learning, the Inception-v3 architecture with pre-trained weights was used. Some initial layers were frozen and training was done on the remaining layers.

After training, the models were deployed through a Flask API. It accepts an image through a POST request and returns the predictions to the user.

For the *training notebook, *check this.

For the *Flask API, *check this.

Table of Contents

  1. Installing Monk
  2. The Dataset
  3. Training the models
  4. Results of Training
  5. Deploying the models through API
  6. Running the API
  7. Conclusion

deep-learning computer-vision flask image-classification deployment

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