Prompt Title: compare YOLO

YOLO (You Only Look Once) is a family of object detection models that use a single neural network to predict the class and location of objects in an image. There have been several versions of YOLO developed over the years, each with improvements over the previous version. Here are some of the key differences between the YOLO versions:

1. YOLOv1: This was the first version of YOLO introduced in 2015. It divided the image into a grid of cells and predicted the probability of an object belonging to each cell, along with the bounding box coordinates and class probabilities. However, it suffered from low recall due to the grid-based approach.

2. YOLOv2: YOLOv2, introduced in 2016, improved on the original version by using anchor boxes and feature extraction from multiple layers of the neural network. It also used batch normalization and a new loss function to improve performance.

3. YOLOv3: YOLOv3, introduced in 2018, further improved on YOLOv2 by adding residual connections and a feature pyramid network, which improved the model's ability to detect objects at different scales. It also introduced the use of multiple anchors per grid cell and improved the loss function.

4. YOLOv4: YOLOv4, introduced in 2020, improved on YOLOv3 by using advanced training techniques such as cutmix and mosaic augmentation, as well as introducing new backbone architectures such as CSPResNet and SPP-Net. It also used spatial pyramid pooling to improve performance on small objects and introduced the use of dynamic anchor assignment.

Each version of YOLO has built on the previous version, introducing new techniques and architectures to improve performance. YOLOv4 is currently the state-of-the-art YOLO version, with significantly improved performance compared to previous versions.