Train a Custom YOLOv7 Model: Object Detection for Basketball

Object detection is a game-changer in deep learning, combining image classification with object identification to locate and classify objects within images. YOLOv7 stands out for its accuracy, speed, and user-friendliness. This tutorial guides you through training a custom YOLOv7 model for a specific object detection task. We'll focus on identifying the "ball-handler" in NBA game footage.

Prerequisites to Training a Custom YOLOv7 Model

Before you start, you'll need some Python coding experience and a basic understanding of deep learning concepts. A machine with sufficient processing power, ideally one with a GPU, is also needed.

• Basic Python knowledge
• Fundamental deep learning understanding
• Access to a machine with a GPU (consider DigitalOcean GPU Droplets)

What is YOLO and Why Use YOLOv7?

YOLO (You Only Look Once) revolutionized object detection by processing images in a single pass. YOLOv7, the latest iteration, builds upon its predecessors with significant improvements in speed and accuracy. YOLO is popular because it is comparatively accurate, extremely fast, and easy to use.

Understanding How YOLO Works

YOLO divides an image into a grid, with each grid predicting bounding boxes, object labels, and the probability of an object's presence. To refine these predictions, YOLO uses Non-Maximal Suppression, eliminating overlapping bounding boxes with lower probability scores. The object with the highest probability and appropriate bounding box is then chosen.

Key Improvements in YOLOv7

YOLOv7 introduces several key architectural changes:

• Extended Efficient Layer Aggregation Networks (E-ELAN): Improves the network's learning capacity without disrupting the gradient flow.
• Model Scaling for Concatenation-Based Models: Optimizes model scaling for different sizes, maintaining optimal architecture.
• Trainable Bag of Freebies: Enhancements that improve training without increasing inference cost.
• Coarse-to-Fine Lead Loss Head: Uses a hierarchical labeling approach for more effective training.

Preparing Your Custom Dataset for YOLOv7 Object Detection

To train our "ball-handler" detector, we'll create a custom dataset from NBA highlight videos.

1. Download NBA highlight reels.
2. Extract image frames: Use VLC's snapshot feature to convert videos into image sequences.
3. Label your data: Use a tool like RoboFlow to label each image, identifying "ball-handler" and "player" classifications.

Labeling is crucial. Aim for around 2000 images per classification for optimal results. For this tutorial, we'll use a smaller sample of 1668 training photos, 81 test images, and 273 validation images.

Code Demo: Training Your YOLOv7 Model

Now, let's dive into the code!

!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/

Next, install the necessary packages:

!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

Helpful Code Snippets

This removes extra files from roboflow.

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)