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https://sites.google.com/view/darknet-p ... net-market Best Free Datasets for Machine Learning to find relevant data for training your models.YOLO v2YOLO v2, also known as YOLO9000, was introduced in 2016 as an improvement over the original YOLO algorithm. It was designed to be faster and more accurate than YOLO and to be able to detect a wider range of object classes. This updated version also uses a different CNN backbone called Darknet-19, a variant of the VGGNet architecture with simple progressive convolution and pooling layers.One of the main improvements in YOLO v2 is the use of anchor boxes. Anchor boxes are a set of predefined bounding boxes of different aspect ratios and scales. When predicting bounding boxes, YOLO v2 uses a combination of the anchor boxes and the predicted offsets to determine the final bounding box. This allows the algorithm to handle a wider range of object sizes and aspect ratios.Another improvement in YOLO v2 is the use of batch normalization, which helps to improve the accuracy and stability of the model. YOLO v2 also uses a multi-scale training strategy, which involves training the model on images at multiple scales and then averaging the predictions. This helps to improve the detection performance of small objects.YOLO v2 also introduces a new loss function better suited to object detection tasks. The loss function is based on the sum of the squared errors between the predicted and ground truth bounding boxes and class probabilities.The results obtained by YOLO v2 compared to the original version and other contemporary models are shown below.Source: PaperYOLO v3YOLO v3 is the third version of the YOLO object detection algorithm. It was introduced in 2018 as an improvement over YOLO v2, aiming to increase the accuracy and speed of the algorithm.One of the main improvements in YOLO v3 is the use of a new CNN architecture called Darknet-53. Darknet-53 is a variant of the ResNet architecture and is designed specifically for object detection tasks. It has 53 convolutional layers and is able to achieve state-of-the-art results on various object detection benchmarks.Another improvement in YOLO v3 are anchor boxes with different scales and aspect ratios. In YOLO v2, the anchor boxes were all the same size, which limited the ability of the algorithm to detect objects of different sizes and shapes. In YOLO v3 the anchor boxes are scaled, and aspect ratios are varied to better match the size and shape of the objects being detected.YOLO v3 also introduces the concept of "feature pyramid networks" (FPN). FPNs are a CNN architecture used to detect objects at multiple scales. They construct a pyramid of feature maps, with each level of the pyramid being used to detect objects at a different scale. This helps to improve the detection performance on small objects, as the model is able to see the objects at multiple scales.In addition to these improvements, YOLO v3 can handle a wider range of object sizes and aspect ratios. It is also more accurate and stable than the previous versions of YOLO.Comparison of the results obtained by YOLO v3. Source: PaperYOLO v4Note: Joseph Redmond, the original creator of YOLO, has left the AI community a few years before, so YOLOv4 and other versions past that are not his official work. Some of them are maintained by co-authors, but none of the releases past YOLOv3 is considered the "official" YOLO.YOLO v4 is the fourth version of the YOLO object detection algorithm introduced in 2020 by Bochkovskiy et al. as an improvement over YOLO v3.The primary improvement in YOLO v4 over YOLO v3 is the use of a new CNN architecture called CSPNet (shown below). CSPNet stands for "Cross Stage Partial Network" and is a variant of the ResNet architecture designed specifically for object detection tasks. It has a relatively shallow structure, with only 54 convolutional layers. However, it can achieve state-of-the-art results on various object detection benchmarks.Architecture of CSPNet. Source: PaperBoth YOLO v3 and YOLO v4 use anchor boxes with different scales and aspect ratios to better match the size and shape of the detected objects. YOLO v4 introduces a new method for generating the anchor boxes, called "k-means clustering." It involves using a clustering algorithm to group the ground truth bounding boxes into clusters and then using the centroids of the clusters as the anchor boxes. This allows the anchor boxes to be more closely aligned with the detected objects' size and shape.While both YOLO v3 and YOLO v4 use a similar loss function for training the model, YOLO v4 introduces a new term called "GHM loss. https://sites.google.com/view/darknet-m ... hub-market Avinandan Chakraborty & Jacqueline Doremus & Sarah Stith, 2020. https://sites.google.com/view/dark-web- ... anyl-pills Check out more articles under these sections.
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