https://github.com/arkasarkar19/car-detection-using-yolo

Refer Readme.md
https://github.com/arkasarkar19/car-detection-using-yolo

anchor-boxes bounding-boxes car-detection-neural-network compter-vision dimension encoding machine-learning non-maximum-suppression yolo yolo-architecture

Last synced: 2 months ago
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Refer Readme.md

• Host: GitHub
• URL: https://github.com/arkasarkar19/car-detection-using-yolo
• Owner: ArkaSarkar19
• Created: 2020-06-15T21:33:11.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-06-16T09:02:35.000Z (almost 5 years ago)
• Last Synced: 2025-01-19T07:15:14.272Z (4 months ago)
• Topics: anchor-boxes, bounding-boxes, car-detection-neural-network, compter-vision, dimension, encoding, machine-learning, non-maximum-suppression, yolo, yolo-architecture
• Language: Python
• Homepage:
• Size: 58.9 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Car-Detection-using-YOLO

YOLO ("you only look once") is a popular algoritm because it achieves high accuracy while also being able to run in real-time. This algorithm "only looks once" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes. 

## YOLO model Architecture 

 ![YOLO](readme_images/model_architecture.png?raw=true)

 

 

 

### Model details

* Inputs and outputs   

  * The **input** is a batch of images, and each image has the shape (m, 608, 608, 3) 

  * The **output** is a list of bounding boxes along with the recognized classes. Each bounding box is represented by 6 numbers (pc,bx,by,bh,bw,c)(pc,bx,by,bh,bw,c) as explained above. If you expand cc into an 80-dimensional vector, each bounding box is then represented by 85 numbers. 

* Anchor Boxes 

  * Anchor boxes are chosen by exploring the training data to choose reasonable height/width ratios that represent the different classes. For this assignment, 5 anchor boxes were chosen for you (to cover the 80 classes), and stored in the file './model_data/yolo_anchors.txt' 

  * The dimension for anchor boxes is the second to last dimension in the encoding: (m,nH,nW,anchors,classes)(m,nH,nW,anchors,classes). 

  * The YOLO architecture is: IMAGE (m, 608, 608, 3) -> DEEP CNN -> ENCODING (m, 19, 19, 5, 85). 

  

### NOTE

* To generate yolo.h5 file go to this [link](https://github.com/allanzelener/YAD2K). Place that in **model_data** folder. 

* Input images are in the **images** directory and the correcponding output images are in the **out** directory.