<p>You're right - Faster R-CNN already uses RPN.</p>
<p>But you're likely misreading the title of the other table. It is &quot;RPN &amp; <strong>Fast</strong> R-CNN&quot;.</p>
<p><a href="https://arxiv.org/pdf/1504.08083v2.pdf" rel="nofollow noreferrer">Fast R-CNN</a> is the predecessor of <a href="https://arxiv.org/pdf/1506.01497.pdf" rel="nofollow noreferrer">Faster R-CNN</a>. It takes as input an entire image and <strong>a set of object proposals</strong>. These object proposals have to therefore be pre-computed which, in the original paper, was done using <a href="https://ivi.fnwi.uva.nl/isis/publications/2013/UijlingsIJCV2013/UijlingsIJCV2013.pdf" rel="nofollow noreferrer">Selective Search</a>.</p>
<p>Since the object proposal process is not part of the network architecture itself, it could use any other method, including an RPN. This is what you see in the Detectron2 model zoo - the pre-trained Fast R-CNN model uses an independently pre-trained RPN to generate the proposals. See the <a href="https://github.com/facebookresearch/detectron2/blob/main/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml" rel="nofollow noreferrer">config</a> that specifies separate proposal files as part of the dataset.</p>
What is the difference between the Faster R-CNN and RPN & Fast R-CNN models offered in Detectron2 model zoo?
<p>I am trying to implement a pretrained model from the Detectron2 library for object detection and it seems that Faster R-CNN models outperform the RetinaNet models. However, when accessing the model zoo, I came across Faster R-CNN models and RPN Faster R-CNN models. I scoured the internet but I am struggling to find the difference between these models. Does not Faster R-CNN already use RPN?</p>
<p>Model Zoo: <a href="https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md" rel="nofollow noreferrer">https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md</a></p>
<p><a href="https://i.stack.imgur.com/G52TI.png" rel="nofollow noreferrer">Detectron2 Model Zoo</a></p>