Answers.Body: <h2>There are several key differences between <strong>YOLOv3</strong> and <strong>RetinaNet</strong>.</h2>
  <ul>
  <li><p><strong>RetinaNet</strong> is an object detection model that utilizes <strong>two-stage cascade</strong> and <strong>sampling heuristics</strong> to address class imbalance during training. whereas <strong>YOLOv3</strong> is a real-time, <strong>single-stage</strong> object detection model that builds on <strong>YOLOv2</strong> with several improvements.</p>
  </li>
  <li><p><strong>RetinaNet</strong> uses a <a href="https://iq.opengenus.org/resnet50-architecture/" rel="nofollow noreferrer">ResNet50</a> backbone, while <strong>YOLOv3</strong> uses a <a href="https://www.researchgate.net/figure/Darknet-53-Structure_fig2_343582547" rel="nofollow noreferrer">Darknet53</a> backbone.</p>
  </li>
  <li><p><strong>RetinaNet</strong> uses <code>Focal Loss</code> to prevent the easy background examples from overwhelming the network during training, while <strong>YOLOv3</strong> uses <code>Cross-Entropy</code> loss.</p>
  </li>
  <li><p><strong>YOLOv3</strong> is fast and accurate in terms of <strong>mean average precision</strong> (<code>mAP</code>) and <strong>intersection over union</strong> (<code>IOU</code>) values as well.</p>
  </li>
  </ul>
  <blockquote>
  <p><strong>YOLOv3</strong> predicts bounding boxes and class probabilities directly from full images in a single pass, while <strong>RetinaNet</strong> first classifies proposal regions and then predicts bounding boxes and class probabilities for each region. This makes <strong>YOLOv3</strong> faster and simpler, but also less accurate than RetinaNet. hence suitable for use where the priority is <strong>real-time</strong> performance with <code>FPS: 51</code>. <strong>RetinaNet</strong> has a high <code>mAP</code> <code>82.89%</code>, but the detection speed <code>FPS: 17</code> is <strong>not</strong> fast enough for <strong>real-time</strong> application.</p>
  </blockquote>
  <blockquote>
  <p>The accuracy of detecting objects with <strong>YOLOv3</strong> can be made equal to the accuracy when using <strong>RetinaNet</strong> by having a larger dataset, making it an ideal option for models that can be trained with large datasets.</p>
  </blockquote>
  <ul>
  <li><p>The loss function of <strong>YOLOv3</strong> converges faster, indicating that the training time of the <strong>YOLOv3</strong> model is shorter than <strong>RetinaNet</strong>.</p>
  </li>
  <li><p><strong>RetinaNet</strong> is better at detecting <strong>rotated</strong> objects.</p>
  </li>
  <li><p><strong>YOLOv3</strong> can be trained on more data more quickly than <strong>RetinaNet</strong></p>
  </li>
  <li><p>The <strong>loss functions</strong> used by each algorithm are different.</p>
  </li>
  <li><p><strong>YOLOv3</strong> may not be ideal for using niche models where large datasets can be hard to obtain but its sutible for models like traffic detection, where plenty of data can be used to train the model since the number of images of different vehicles is plentiful.</p>
  </li>
  </ul>
  <h2><strong>Precision for Small Objects:</strong></h2>
  <p>The chart below (taken and modified from the <a href="https://arxiv.org/abs/1804.02767" rel="nofollow noreferrer">YOLOv3 paper</a>) shows the average precision (<code>AP</code>) of detecting small, medium, and large images with various algorithms and backbones. The higher the <code>AP</code>, the more accurate it is for that variable.</p>
  <p><a href="https://i.sstatic.net/vwb6m.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/vwb6m.png" alt="YOLOv3 comparison for different object sizes showing the average precision (AP) for AP-S (small object size), AP-M (medium object size), AP-L (large object size)
  Read more at: https://viso.ai/deep-learning/yolov3-overview/" /></a></p>
  <p><strong>Source:</strong> <a href="https://arxiv.org/abs/1708.02002" rel="nofollow noreferrer">Focal Loss for Dense Object Detection</a></p>
  <p><strong>YOLOv3</strong> increased the AP for small objects by 13.3, which is a massive advance from <strong>YOLOv2</strong>. However, the average precision (<code>AP</code>) for all objects (small, medium, large) is still less than <strong>RetinaNet</strong>.</p>
  <h2><strong>Speed:</strong></h2>
  <p>YOLOv3 is fast and accurate in terms of <strong>mean average precision (mAP)</strong> and <strong>intersection over union (IOU)</strong> values as well. It runs significantly faster than other detection methods with comparable performance (hence the name – You only look once). Moreover, you can easily trade off between speed and accuracy simply by changing the model’s size, without the need for model retraining.</p>
  <p><a href="https://i.sstatic.net/ul5zm.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ul5zm.png" alt="Speed comparison between RetinaNet50 vs YOLOv3" /></a></p>
  <p><strong>References:</strong></p>
  <ol>
  <li><p><a href="https://paperswithcode.com/method/retinanet" rel="nofollow noreferrer">RetinaNet(PapersWithCode)</a></p>
  </li>
  <li><p><a href="https://paperswithcode.com/method/yolov3" rel="nofollow noreferrer">YOLOv3(PapersWithCode)</a></p>
  </li>
  <li><p><a href="https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01691-8" rel="nofollow noreferrer">Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification</a></p>
  </li>
  <li><p><a href="https://www.analyticsvidhya.com/blog/2018/12/practical-guide-object-detection-yolo-framewor-python/" rel="nofollow noreferrer">A Practical Guide to Object Detection using the Popular YOLO Framework – Part III (with Python codes)</a></p>
  </li>
  <li><p><a href="https://lilianweng.github.io/posts/2017-12-31-object-recognition-part-3/" rel="nofollow noreferrer">Object Detection for Dummies Part 3: R-CNN Family</a></p>
  </li>
  <li><p><a href="https://www.ijert.org/research/comparison-of-yolov3-and-ssd-algorithms-IJERTV10IS020077.pdf" rel="nofollow noreferrer">Comparison of YOLOv3 and SSD Algorithms</a></p>
  </li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-10-19T15:29:29.960Z
Answers.LastActivityDate: 2022-10-19T17:54:41.347Z
Answers.LastEditDate: 2022-10-19T17:54:41.347Z
Answers.UserDisplayName: Faizy
Body: <p>I am looking at a certain project that compares performance on a certain dataset for an object detection problem using YOLOv3 and RetinaNet (or the &quot;SSD_ResNet50_FPN&quot; from TF Model Zoo). Both YOLOv3 and RetinaNet seem to have similar features like detection at scales, skip connections, etc.</p>
  <p>So, what is the exact main difference between YOLOv3 and SSD_ResNet50_FPN?</p>
  
Tags: <computer-vision><tensorflow><object-detection><yolo><single-shot-multibox-detector>
Title: What are the main differences between YOLOv3 and RetinaNet object detection algorithms?
UserDisplayName: Ananda
ThreadText: # What are the main differences between YOLOv3 and RetinaNet object detection algorithms?
  
  (Asked by: username_0 on 2021-03-02)
  
  I am looking at a certain project that compares performance on a certain dataset for an object detection problem using YOLOv3 and RetinaNet (or the "SSD\_ResNet50\_FPN" from TF Model Zoo). Both YOLOv3 and RetinaNet seem to have similar features like detection at scales, skip connections, etc.
  
  So, what is the exact main difference between YOLOv3 and SSD\_ResNet50\_FPN?
  
  **Tags:** computer-vision, tensorflow, object-detection, yolo, single-shot-multibox-detector
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-10-19. Score: 3
  
  ## There are several key differences between **YOLOv3** and **RetinaNet**.
  
  * **RetinaNet** is an object detection model that utilizes **two-stage cascade** and **sampling heuristics** to address class imbalance during training. whereas **YOLOv3** is a real-time, **single-stage** object detection model that builds on **YOLOv2** with several improvements.
  * **RetinaNet** uses a [ResNet50](https://iq.opengenus.org/resnet50-architecture/) backbone, while **YOLOv3** uses a [Darknet53](https://www.researchgate.net/figure/Darknet-53-Structure_fig2_343582547) backbone.
  * **RetinaNet** uses `Focal Loss` to prevent the easy background examples from overwhelming the network during training, while **YOLOv3** uses `Cross-Entropy` loss.
  * **YOLOv3** is fast and accurate in terms of **mean average precision** (`mAP`) and **intersection over union** (`IOU`) values as well.
  
  > **YOLOv3** predicts bounding boxes and class probabilities directly from full images in a single pass, while **RetinaNet** first classifies proposal regions and then predicts bounding boxes and class probabilities for each region. This makes **YOLOv3** faster and simpler, but also less accurate than RetinaNet. hence suitable for use where the priority is **real-time** performance with `FPS: 51`. **RetinaNet** has a high `mAP` `82.89%`, but the detection speed `FPS: 17` is **not** fast enough for **real-time** application.
  
  > The accuracy of detecting objects with **YOLOv3** can be made equal to the accuracy when using **RetinaNet** by having a larger dataset, making it an ideal option for models that can be trained with large datasets.
  
  * The loss function of **YOLOv3** converges faster, indicating that the training time of the **YOLOv3** model is shorter than **RetinaNet**.
  * **RetinaNet** is better at detecting **rotated** objects.
  * **YOLOv3** can be trained on more data more quickly than **RetinaNet**
  * The **loss functions** used by each algorithm are different.
  * **YOLOv3** may not be ideal for using niche models where large datasets can be hard to obtain but its sutible for models like traffic detection, where plenty of data can be used to train the model since the number of images of different vehicles is plentiful.
  
  ## **Precision for Small Objects:**
  
  The chart below (taken and modified from the [YOLOv3 paper](https://arxiv.org/abs/1804.02767)) shows the average precision (`AP`) of detecting small, medium, and large images with various algorithms and backbones. The higher the `AP`, the more accurate it is for that variable.
  
  [![YOLOv3 comparison for different object sizes showing the average precision (AP) for AP-S (small object size), AP-M (medium object size), AP-L (large object size)
  Read more at: https://viso.ai/deep-learning/yolov3-overview/](https://i.sstatic.net/vwb6m.png)](https://i.sstatic.net/vwb6m.png)
  
  **Source:** [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002)
  
  **YOLOv3** increased the AP for small objects by 13.3, which is a massive advance from **YOLOv2**. However, the average precision (`AP`) for all objects (small, medium, large) is still less than **RetinaNet**.
  
  ## **Speed:**
  
  YOLOv3 is fast and accurate in terms of **mean average precision (mAP)** and **intersection over union (IOU)** values as well. It runs significantly faster than other detection methods with comparable performance (hence the name – You only look once). Moreover, you can easily trade off between speed and accuracy simply by changing the model’s size, without the need for model retraining.
  
  [![Speed comparison between RetinaNet50 vs YOLOv3](https://i.sstatic.net/ul5zm.png)](https://i.sstatic.net/ul5zm.png)
  
  **References:**
  
  1. [RetinaNet(PapersWithCode)](https://paperswithcode.com/method/retinanet)
  2. [YOLOv3(PapersWithCode)](https://paperswithcode.com/method/yolov3)
  3. [Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01691-8)
  4. [A Practical Guide to Object Detection using the Popular YOLO Framework – Part III (with Python codes)](https://www.analyticsvidhya.com/blog/2018/12/practical-guide-object-detection-yolo-framewor-python/)
  5. [Object Detection for Dummies Part 3: R-CNN Family](https://lilianweng.github.io/posts/2017-12-31-object-recognition-part-3/)
  6. [Comparison of YOLOv3 and SSD Algorithms](https://www.ijert.org/research/comparison-of-yolov3-and-ssd-algorithms-IJERTV10IS020077.pdf)
  
  
  ---
  
  