How To Roll Your Own Custom Object Detection Neural Network

Feb 14, 2023 · coding ·

Object detection is a powerful technique in computer vision that allows machines to recognize and identify objects within an image or video. With the emergence of deep learning, object detection has become significantly more accurate and easier to implement. This tutorial will guide you through the process of creating your own custom object detection neural network.

The first step is to prepare the data that will be used to train the model. You’ll need to collect images of the objects you want to detect along with annotated labels indicating which objects are present in each image. Once your dataset is collected and labeled, it is ready to be split into training, validation and testing sets. The training set should be used to teach the model while the validation set should be used to measure its performance.

The next step is to choose an architecture for your model. There are many architectures available but some of the most popular architectures for object detection include RetinaNet, YOLO, Mask R-CNN, and SSD. Each architecture has strengths and weaknesses so it’s important to do research to determine which architecture best suits your needs.

Once the model architecture has been chosen, the model can be trained. Training involves feeding the model the prepared training set and having it learn how to classify objects in images. Common techniques used to improve the accuracy of the model include transfer learning, data augmentation, and hyperparameter tuning. Transfer learning involves using a pre-trained model as the starting point for your own object detection model, while data augmentation improves the model by adding new images with different variations of the same objects. Hyperparameter tuning adjusts the model’s parameters to optimize its performance.

Once the model is trained and tuned, it is ready to be tested on the test set. Testing is done to make sure the model is performing optimally and is ready for deployment. After testing is complete, the model can be deployed in production environments.

In summary, this tutorial provided an overview of how to roll your own custom object detection neural network. The process starts by preparing the dataset and choosing an appropriate architecture. Next, the model is trained, optimized, and tested before being deployed in production. With these steps, you can create a powerful object detection model capable of accurately detecting objects in images.