YOLO v3 predicts 3 bounding boxes for every cell. Each of the bounding boxes have 5 + C attributes, which describe the center coordinates, the dimensions, the objectness score and C class confidences for each bounding box. According to the paper, each of these B bounding boxes may specialize in detecting a certain kind of object. B represents the number of bounding boxes each cell can predict. Though the technically correct term to describe a unit in the feature map would be a neuron, calling it a cell makes it more intuitive in our context.ĭepth-wise, we have (B x (5 + C)) entries in the feature map. In YOLO v3 (and it's descendants), the way you interpret this prediction map is that each cell can predict a fixed number of bounding boxes. Since we have used 1 x 1 convolutions, the size of the prediction map is exactly the size of the feature map before it. Now, the first thing to notice is our output is a feature map. In YOLO, the prediction is done by using a convolutional layer which uses 1 x 1 convolutions. Typically, (as is the case for all object detectors) the features learned by the convolutional layers are passed onto a classifier/regressor which makes the detection prediction (coordinates of the bounding boxes, the class label. Generally, stride of any layer in the network is equal to the factor by which the output of the layer is smaller than the input image to the network. For example, if the stride of the network is 32, then an input image of size 416 x 416 will yield an output of size 13 x 13.
The network downsamples the image by a factor called the stride of the network. This is needed to concatenate multiple images into a large batch (concatenating many PyTorch tensors into one) However, in practice, we might want to stick to a constant input size due to various problems that only show their heads when we are implementing the algorithm.Ī big one amongst these problems is that if we want to process our images in batches (images in batches can be processed in parallel by the GPU, leading to speed boosts), we need to have all images of fixed height and width. This helps in preventing loss of low-level features often attributed to pooling.īeing a FCN, YOLO is invariant to the size of the input image. No form of pooling is used, and a convolutional layer with stride 2 is used to downsample the feature maps. It has 75 convolutional layers, with skip connections and upsampling layers.
YOLO makes use of only convolutional layers, making it a fully convolutional network (FCN). Before we get out hands dirty with code, we must understand how YOLO works. It's an object detector that uses features learned by a deep convolutional neural network to detect an object. I've provided the link at the end of the post in case you fall short on any front. You should be able to create simple neural networks with ease. What is object detection, bounding box regression, IoU and non-maximum suppression.īasic PyTorch usage. This also includes knowledge of Residual Blocks, skip connections, and Upsampling. You should understand how convolutional neural networks work. Part 5 : Designing the input and the output pipelines Part 4 : Objectness score thresholding and Non-maximum suppression Part 3 : Implementing the the forward pass of the network Part 2 : Creating the layers of the network architecture
Part 1 (This one): Understanding How YOLO works It can be found in it's entirety at this Github repo.
#Permute by row torch code#
The code for this tutorial is designed to run on Python 3.5, and PyTorch 0.4. We will use PyTorch to implement an object detector based on YOLO v3, one of the faster object detection algorithms out there. This is exactly what we'll do in this tutorial. One of the biggest takeaways from this experience has been realizing that the best way to go about learning object detection is to implement the algorithms by yourself, from scratch. Recent years have seen people develop many algorithms for object detection, some of which include YOLO, SSD, Mask RCNN and RetinaNet.įor the past few months, I've been working on improving object detection at a research lab. Object detection is a domain that has benefited immensely from the recent developments in deep learning. Check out his YOLO v3 real time detection video here
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