Object detection - How to detect and extract features using CNN and classify them using a classifier?

Question

I have an image classification problem where the number of classes increases over time and when a new class is created I just trained the model with images of the new class. I know this is not possible to do with a CNN, so to solve this problem I did transfer learning where I used a Keras pretrained model to extract the features of the images but instead of replacing the last layers (used for classification) with new layers, I used a Random Forest that is able to increase the number of classes. I achieved an accuracy of 86% using the InceptionResnetV2 trained on the imagenet dataset, which is good for now.

Now I want to do the same but on an object detection problem. How can I achieve this? Can I use the Tensorflow Object Detection API?

Is it possible to replace the last layers, of a pretrained CNN with a detection algorithm like Faster-RCNN or SSD, with a random forest?

Answer 1

Yes, you could implement the above-mentioned approach using Tensorflow object detection API. Also, you could use your InceptionResnetV2 trained model as a feature extractor. The tensorflow object detection API already has InceptionResnetV2 feature extractor trained on coco dataset. Its available at https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

Or if you want to provide or create custom feature extractor, please follow the link https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/defining_your_own_model.md

If you are new to Tensorflow object detection API. Please follow this tutorial, https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10

Hope this helps.