Faster R-CNN object detector implemented in PyTorch and TensorFlow 2
Fresh and (I think) clean implementations of Faster R-CNN in PyTorch and TensorFlow 2/Keras. I wanted to learn about object detectors and decided to understand and implement a foundational model in the field, Faster R-CNN (elements of which are still used in modern models to this day), using the paper alone. That proved to be more difficult than expected and I had to relent and take a peak at existing implementations to fill in some important gaps. I've documented my struggles and learnings in the README for others to benefit from. I also wanted to solidify my understanding of…
In plain words
This software provides implementations of Faster R-CNN, a foundational object detection model, in both PyTorch and TensorFlow 2/Keras. Built by a developer learning the architecture from the original research paper, it includes detailed documentation of implementation challenges and solutions. The project serves researchers and developers studying object detection, offering clean code examples in two popular machine learning frameworks to understand how this model works and why certain design decisions matter.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Fresh and (I think) clean implementations of Faster R-CNN in PyTorch and TensorFlow 2/Keras. I wanted to learn about object detectors and decided to understand and implement a foundational model in the field, Faster R-CNN (elements of which are still used in modern models to this day), using the paper alone. That proved to be more difficult than expected and I had to relent and take a peak at existing implementations to fill in some important gaps. I've documented my struggles and learnings in the README for others to benefit from. I also wanted to solidify my understanding of TensorFlow/Keras and learn PyTorch, so I made sure to implement the model in both. Faster R-CNN is fairly challenging in that it doesn't quite map to Keras tutorial examples. For example, losses are not computed simply as a function of model output and input but rather, some of the training data is actually computed on-the-fly within the model during training and the losses have to explicitly be constructed as part of the graph. Personally, I found the "official" TensorFlow reference implementation (https://github.com/tensorflow/models/blob/master/research/ob...) to be very difficult to follow and I hope this proves to be useful to learners like myself.
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