<p>There are two categories for detectors, one stage and two stage. Yolo, SSD, RetinaNet, CenterNet etc. fall in one stage while R-FCN, R-CNN, Faster R-CNN, etc. fall in two stage category.</p>
<p>Direct quote from <strong>[1]</strong> about advantage two stage detector comprated to one stage,</p>
<blockquote>
<p>Compared to one-stage detectors, the two-stage ones have the following
advantages: 1) By sampling a sparse set of region proposals, two-stage
detectors filter out most of the negative proposals; while one-stage
detectors directly face all the regions on the image and have a
problem of class imbalance if no specialized design is introduced. 2)
Since two-stage detectors only process a small number of proposals,
the head of the network (for proposal classification and regression)
can be larger than one-stage detectors, so that richer features will
be extracted. 3) Two-stage detectors have high-quality features of
sampled proposals by use of the RoIAlign [10] operation that extracts
the location consistent feature of each proposal; but different region
proposals can share the same feature in one-stage detectors and the
coarse and spatially implicit representation of proposals may cause
severe feature misalignment. 4) Two-stage detectors regress the object
location twice (once on each stage) and the bounding boxes are better
refined than one-stage methods.</p>
</blockquote>
<p>Quote accuracy vs efficiency,</p>
<blockquote>
<p>One-stage detectors are more efficient and elegant in design, but
currently the two-stage detectors have domination in accuracy.</p>
</blockquote>
<p>One stage detectors can be deployed on edge devices such as phones for fast real-time detection. This can save more energy compared to more compute intensive detectors.</p>
<p>In summary, go for two stage detectors if accuracy is more important, otherwise go for one stage for faster detection while maintaining good enough accuracy.</p>
<p>Related works section of <strong>[1]</strong> contains easy to read details as well as each referenced papers have review on two stage vs one stage.</p>
<h3>Object detection benchmarks</h3>
<p><a href="https://paperswithcode.com/task/object-detection" rel="nofollow noreferrer">https://paperswithcode.com/task/object-detection</a></p>
<h3>References</h3>
<p>[1] MimicDet, <a href="https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590528.pdf" rel="nofollow noreferrer">https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590528.pdf</a></p>
<p>[2] Speed/accuracy trade-offs for modern convolutional object detectors, <a href="https://arxiv.org/pdf/1611.10012.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1611.10012.pdf</a></p>
<p>[3] RetinaNet, <a href="https://arxiv.org/pdf/1708.02002.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1708.02002.pdf</a></p>
<p>[4] Object detection review, <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=9186021" rel="nofollow noreferrer">https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=9186021</a></p>
<p>[5] CSPNET, <a href="https://arxiv.org/pdf/1911.11929v1.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1911.11929v1.pdf</a></p>
<p>[6] CenterNet, <a href="https://arxiv.org/pdf/1904.08189v3.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1904.08189v3.pdf</a></p>
<p>[7] EfficientDet, <a href="https://arxiv.org/pdf/1911.09070.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1911.09070.pdf</a></p>
<p>[8] SpineNet, <a href="https://arxiv.org/pdf/1912.05027.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1912.05027.pdf</a></p>
<h3>Related articles</h3>
<p><a href="https://jonathan-hui.medium.com/object-detection-speed-and-accuracy-comparison-faster-r-cnn-r-fcn-ssd-and-yolo-5425656ae359" rel="nofollow noreferrer">https://jonathan-hui.medium.com/object-detection-speed-and-accuracy-comparison-faster-r-cnn-r-fcn-ssd-and-yolo-5425656ae359</a></p>
<p><a href="https://www.jeremyjordan.me/object-detection-one-stage/" rel="nofollow noreferrer">https://www.jeremyjordan.me/object-detection-one-stage/</a></p>
Criteria to choose a object detection Method
<p>I'm in the research phase of my project and I'm trying to make an object detector using CNN. I know that in general there's 2 &quot;type&quot; of CNN object detector, Region Proposal based (i.e R-CNN and R-FCN ) and Regression/Classification based method (i.e YOLO and SSD). The problem is I'm not so sure which method should I use. I would like to know what are the usual reasoning to choose a Method over the other. there's a few general criteria such as Speed vs Accuracy. But is there any other commonly used reasoning ?</p>