Train Your Own Object Detector: A Practical Guide to YOLOv7

Object detection is a game-changing AI application, combining image classification with precise object identification. YOLOv7 stands out as a powerful, accurate, and relatively easy-to-use solution. This guide will show you how to leverage YOLOv7, the latest iteration of the popular "You Only Look Once" algorithm, to create a custom object detection model.

Updated on September 17, 2024 for clarity and ease of use, this guide is for anyone looking to implement custom object detection using YOLOv7.

What is YOLOv7 and Why Should You Use It?

YOLO is an object detection algorithm celebrated for its speed, accuracy, and real-time processing capabilities. YOLOv7 enhances previous versions by improving accuracy without sacrificing speed, making it ideal for various applications.

Benefits of this guide

• Create a custom object detection model using NBA game footage, distinguishing the ball handler from other players.
• Learn how YOLOv7 works, its architecture, and its advantages over previous versions.

Who Should Read this Guide?

This article is useful for people with:

• Some proficiency in Python code.
• A foundational understanding of deep learning concepts.

Access to a machine capable of running the provided code is also required. If you don't have a GPU, using DigitalOcean GPU Droplets is a great option.

Understanding YOLO: How Does "You Only Look Once" Work?

The original YOLO paper revolutionized object detection with its single-stage approach. Unlike older RCNN-based models, YOLO streamlined the process, reducing training and inference times.

How YOLO Works:

• Grid Division: Divides the image into N grids of equal size to detect and localize objects.
• Bounding Box Prediction: Predicts bounding box coordinates (B) with object labels and probability scores for each grid.
• Non-Maximal Suppression: Uses Non-Maximal Suppression to filter overlapping predictions, retaining the most accurate bounding boxes.

Key Improvements in YOLOv7

Compared to previous versions, YOLOv7 introduces several key enhancements that significantly boost performance:

• Extended Efficient Layer Aggregation Networks (E-ELAN): Improves the network's learning capabilities without affecting the gradient path by merging multiple computational models.
• Model Scaling for Concatenation-Based Models: Simultaneously scales network depth and width while concatenating layers.
• Trainable Bag of Freebies: Optimizes the placement of convolutional blocks for enhanced performance.
• Coarse-to-Fine Hierarchical Labels: Uses the lead head prediction for guidance to generate coarse-to-fine hierarchical labels, which are used for auxiliary head and lead head learning, respectively.

Step-by-Step: Training Your Custom YOLOv7 Model

Let's dive into the practical steps of training a custom YOLOv7 model using NBA game footage to identify ball handlers:

1. Setting Up Your Custom Dataset

1. Gather Video Footage: Download NBA highlight reels from platforms like YouTube.
2. Frame Extraction: Use VLC's snapshot filter to break down the videos into a sequence of images.

2. Data Labeling with Roboflow

1. Create a RoboFlow Account: Sign up for a free account at RoboFlow.
2. Start a New Project: Create a new project and upload your image dataset.
3. Define Classifications: Set classes - 'ball-handler' and 'player'.
4. Annotate Your Data: Label each image by drawing bounding boxes around basketball players, differentiating ball handlers from other players.
5. Data Augmentation: Use RoboFlow's augmentation tools to diversify your dataset, increasing its robustness.

3. Data Splits

• Training Set: 1668 images (556x3).
• Test Set: 81 images.
• Validation Set: 273 images.

4. Exporting the Dataset

Generate your dataset and export it in YOLOv7 - PyTorch format using the curl command provided by RoboFlow.

curl -L "https://app.roboflow.com/ds/4E12DR2cRc?key=SomeRandomKey" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip

5. Code Implementation

 !curl -L "https://app.roboflow.com/ds/4E12DR2cRc?key=SomeRandomKey" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip
 !wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt
 ! mkdir v-test
 ! mv train/ v-test/
 ! mv valid/ v-test/

6. Package Installation and Setup

 !pip install -r requirements.txt
 !pip install setuptools==59.5.0
 !pip install torchvision==0.11.3+cu111 -f https://download.pytorch.org/whl/cu111/torch_stable.html

7. Helper Functions

 import os

 # remove roboflow extra junk

 count = 0
 for i in sorted(os.listdir('v-test/train/labels')):
 if count >=3:
 count = 0
 count += 1
 if i[0] == '.':
 continue
 j = i.split('_')
 dict1 = {1:'a', 2:'b', 3:'c'}
 source = 'v-test/train/labels/'+i
 dest = 'v-test/train/labels/'+j[0]+dict1[count]+'.txt'
 os.rename(source, dest)

 count = 0
 for i in sorted(os.listdir('v-test/train/images')):
 if count >=3:
 count = 0
 count += 1
 if i[0] == '.':
 continue
 j = i.split('_')
 dict1 = {1:'a', 2:'b', 3:'c'}
 source = 'v-test/train/images/'+i
 dest = 'v-test/train/images/'+j[0]+dict1[count]+'.jpg'
 os.rename(source, dest)

 for i in sorted(os.listdir('v-test/valid/labels')):
 if i[0] == '.':
 continue
 j = i.split('_')
 source = 'v-test/valid/labels/'+i
 dest = 'v-test/valid/labels/'+j[0]+'.txt'
 os.rename(source, dest)

 for i in sorted(os.listdir('v-test/valid/images')):
 if i[0] == '.':
 continue
 j = i.split('_')
 source = 'v-test/valid/images/'+i
 dest = 'v-test/valid/images/'+j[0]+'.jpg'
 os.rename(source, dest)
 for i in sorted(os.listdir('v-test/test/labels')):
 if i[0] == '.':
 continue
 j = i.split('_')
 source = 'v-test/test/labels/'+i

Takeaways on Training YOLOv7

With YOLOv7, you can train models that are reliable and accurate in object detection. This guide walks you through how to accomplish custom object detection that is tailored to your requirements and vision.