YOLOv3 paper

What is YOLOv3?

YOLOv3 (You Only Look Once, Version 3) is a real-time object detection algorithm that identifies specific objects in videos, live feeds, or images. The YOLO machine learning algorithm uses features learned by a deep convolutional neural network to detect an object.

Is YOLOv5 better than YOLOv3?

The results of using YOLOv5 for poultry detection are compared with other popular CNN architectures, YOLOv3, YOLOv4 models. The results show that YOLOv5x (XLarge depth) model records the highest accuracy, resulting in a mean average precision at 0.5 IOU of %99.5.

Who is the author of YOLOv3?

Joseph Redmon, creator of the popular object detection algorithm YOLO (You Only Look Once), tweeted last week that he had ceased his computer vision research to avoid enabling potential misuse of the tech — citing in particular “military applications and privacy concerns.”

Is YOLOv3 better than YOLOv4?

YOLOv4 is twice as fast as EfficientDet (competitive recognition model) with comparable performance. In addition, AP (Average Precision) and FPS (Frames Per Second) increased by 10% and 12% compared to YOLOv3.

Is YOLOv3 a CNN?

YOLO v3 passes this image to a convolutional neural network (CNN). The last two dimensions of the above output are flattened to get an output volume of (19, 19, 425): Here, each cell of a 19 x 19 grid returns 425 numbers. 425 = 5 * 85, where 5 is the number of anchor boxes per grid.

Is YOLOv3 faster than YOLOv5?

Experimental results reveal that YOLOv3 outperforms YOLOv5 in terms of speed. However, YOLOv5 had the best recognition accuracy.

Which Yolo version is fastest?

The version YOLOv7-X achieves 114 FPS inference speed compared to the comparable YOLOv5-L with 99 FPS, while YOLOv7 achieves a better accuracy (higher AP by 3.9%). Compared with models of a similar scale, the YOLOv7-X achieves a 21 FPS faster inference speed than YOLOv5-X.

Which Yolo model is best?

YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. It outperforms YOLOv5 in detection accuracy and inference speed, making it the best OS version of YOLO architecture for production applications.

Why is YOLOv5 controversial?

Roboflow YOLOv5 Article Controversy

YOLOv5 was incorrectly discussed by Roboflow, who have thus published another article correcting their mistake. In the original article “YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS”, multiple facts were misconstrued.

How many layers are in YOLOv3?

The 53 layers of the darknet are further stacked with 53 more layers for the detection head, making YOLO v3 a total of a 106 layer fully convolutional underlying architecture.

How accurate is YOLOv3?

YOLOv3 is extremely fast and accurate. In mAP measured at . 5 IOU YOLOv3 is on par with Focal Loss but about 4x faster. Moreover, you can easily tradeoff between speed and accuracy simply by changing the size of the model, no retraining required!

Is YOLOv3 a deep learning model?

YOLOv3 is an deep learning model for detecting the position and the type of an object from the input image. It can classify objects in one of the 80 categories available (eg. car, person, motorbike…), and compute bounding boxes for those objects from a single input image. Below is a sample video of YOLOv3 recognition.

What can I use instead of Yolo v3?

The best alternatives to YOLO are hear, hear! , ChallengeMe and And Be Honest. If these 3 options don't work for you, we've listed a few more alternatives below.

What is better than YOLOv3?

Our investigation also shows that the YOLOv5l algorithm outperforms YOLOv4 and YOLOv3 in terms of accuracy of detection while maintaining a slightly slower inference speed.

What are the advantages of YOLOv3?

A target detector called YOLOv3 has the advantages of detection speed and accuracy and meets the real-time requirements for ship detection. However, YOLOv3 has a large number of backbone network parameters and requires high hardware performance, which is not conducive to the popularization of applications.

What is the difference between Yolo and YOLOv3?

It processes images at a resolution of 608 by 608 pixels, which is higher than the 416 by 416 resolution used in YOLO v3. This higher resolution allows YOLO v7 to detect smaller objects and to have a higher accuracy overall.

What are the benefits of YOLOv3?

Is YOLOv3 a deep learning model?

What is the difference between YOLOv3 and SSD?

YOLO (You Only Look Once) is an open-source object detection system. It can recognize objects on a single image or a video stream rapidly. SSD (Single-Shot Multi-box Detection) detects objects with high precision in a single forward pass computing feature map.

What are the pros and cons of YOLOv3?

The main advantages of YOLOv3-tiny are that the network is simple, the calculation is small, and it can run on the mobile terminal or the device side [24][25]. The disadvantage is that the accuracy is relatively low (both candidate frame and classification accuracy are relatively low).

Which Yolo model is best?

In general, YOLOv7 surpasses all previous object detectors in terms of both speed and accuracy, ranging from 5 FPS to as much as 160 FPS. The YOLO v7 algorithm achieves the highest accuracy among all other real-time object detection models – while achieving 30 FPS or higher using a GPU V100.

How accurate is YOLOv3?

How many layers are in YOLOv3?

The 53 layers of the darknet are further stacked with 53 more layers for the detection head, making YOLO v3 a total of a 106 layer fully convolutional underlying architecture.

How many layers are there in YOLOv3?

First, YOLO v3 uses a variant of Darknet, which originally has 53 layer network trained on Imagenet. For the task of detection, 53 more layers are stacked onto it, giving us a 106 layer fully convolutional underlying architecture for YOLO v3. This is the reason behind the slowness of YOLO v3 compared to YOLO v2.

What dataset is YOLOv3 trained on?

For training YOLOv3 we use convolutional weights that are pre-trained on Imagenet. We use weights from the darknet53 model. You can just download the weights for the convolutional layers here (76 MB) and put it in the main directory of the darknet.