Master Custom Object Detection: Training a YOLOv7 Model for Basketball Play Recognition

Updated: September 17, 2024

Want to elevate your computer vision skills? This article dives into training a custom YOLOv7 model, illustrating object detection's power and walking you through building a model that identifies basketball players and ball handlers in NBA game footage.

Why YOLOv7 for Object Detection?

Object detection combines image classification and object localization. YOLO (You Only Look Once) stands out due to its:

• Accuracy: Delivers reliable object detection.
• Speed: Processes images quickly, enabling real-time applications.
• Efficiency: Achieves high performance with relatively low computational resources.

YOLOv7, the latest iteration, significantly improves upon previous versions, making it a top choice for custom object detection tasks. This tutorial provides a complete guide on how to leverage YOLOv7.

Prerequisites: Getting Ready to Train Your Custom YOLOv7 Model

Before starting, ensure you have:

• Python Knowledge: Familiarity with Python syntax and basic programming concepts.
• Deep Learning Basics: A fundamental understanding of deep learning principles.
• Sufficient Hardware: Access to a machine that can handle the computational demands of training (consider DigitalOcean GPU Droplets).

Understanding YOLO: How It Works

YOLO tackles object detection in a single stage. The process involves:

• Grid Division: Dividing an image into SxS grids.
• Object Prediction: Each grid predicts bounding box coordinates, object labels, and confidence scores.
• Non-Maximal Suppression: Filtering overlapping proposals using probability scores for refined results.

What's New in YOLOv7? Key Improvements for Better Performance

YOLOv7 implements several key innovations:

• Extended Efficient Layer Aggregation Networks (E-ELAN): Enhances the network's learning capacity without disrupting the gradient path through model re-parameterization.
• Model Scaling for Concatenation-Based Models: Optimizes network depth and width scaling for various use cases.
• Trainable Bag of Freebies: Integrates re-parameterized convolution with different network structures, yielding improved results.
• Coarse-to-Fine Hierarchical Supervision: A lead head predicts guidance to generate course-to-fine hierarchical labels, which are used for auxiliary head and lead head learning, respectively.

These advancements contribute to YOLOv7's superior performance compared to prior versions.

Step-by-Step: Creating Your Custom YOLOv7 Dataset for Ball Handler Detection

Let's create a dataset for NBA player and ball handler detection.

1. Gather Video Footage: Download NBA highlight reels from platforms like YouTube.
2. Extract Frames: Use VLC's snapshot feature to break down videos into image sequences.
3. Annotation Tool: Use RoboFlow to label data.
   • Create a RoboFlow account and start a new project.
   • Upload your image sequences.
   • Define two classes, ball-handler and player.
   • Annotate players with and without the ball using bounding boxes.
4. Dataset Generation:
   • Aim for 2000 images per class (though smaller samples can work for initial experiments).
   • Generate training, testing and validation sets.
   • Export Data: Export the labeled dataset in YOLOv7 - PyTorch format.
   • Use the curl command provided by RoboFlow to download the data directly to your notebook.

Code Time: Training your Custom YOLOv7 Object Detection Model

Here's the code to train your YOLOv7 model:

1. Download Data and Pre-trained Model

!curl -L "https://app.roboflow.com/ds/4E12DR2cRc?key=LxK5FENSbU" > 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/

2. Install Dependencies

!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

3. Data Preparation Helper

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
    dest = 'v-test/test/labels/'+j[0]+'.txt'
    os.rename(source, dest)

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

Next Steps: Training Your Model and Beyond

This tutorial provided the groundwork for training a custom YOLOv7 model. It's time to train the model using the downloaded data and weights. Consider refining the model with more images and more classes. With dedication, you will have a world class custom object detection model.