Train Your Own Object Detector: A Practical Guide to Custom YOLOv7 Models
Object detection is now within your reach; with the YOLOv7, you can identify objects in images with impressive accuracy, speed, and ease. This guide walks you through training and using a custom YOLOv7 model, even without prior experience.
What is Object Detection and Why YOLOv7?
Object detection goes beyond simple image recognition. It identifies where objects are in an image and what they are. This means identifying the location of each object in an image, using a bounding box and successfully classifying it.
YOLO (You Only Look Once) stands out due to its accuracy, speed, and relative simplicity. YOLOv7 is the latest and greatest!
Benefits of YOLOv7:
- Accuracy: Accurately identifies objects
- Speed: Offers quick results, perfect for real-time applications
- Ease of Use: Simpler than previous object detection methods
Prerequisites: Getting Ready to Train Your YOLOv7 Model
Before diving in, make sure you have these:
- Python Knowledge: Practical experience is a must
- Deep Learning Basics: Understanding the core deep learning principles
- Sufficient Computing Power: Local GPU or consider using online GPU platforms like DigitalOcean GPU Droplets.
Understanding YOLO: How It Works
YOLO simplifies object detection by processing an image in a single pass.
- Grid Division: The image is divided into a grid.
- Object Prediction: Each grid cell predicts bounding boxes, object labels, and confidence scores.
- Non-Maximal Suppression: Overlapping predictions are filtered to identify the most accurate bounding boxes using a technique called non-maximal suppression.
What Makes YOLOv7 Special: Key Improvements
YOLOv7 brings several key upgrades that boost its performance. These enhancements allow for notably faster and more efficient object detection.
- Extended Efficient Layer Aggregation Networks (E-ELAN): Improves the network's learning without disrupting the gradient path, leading to better accuracy.
- Model Scaling for Concatenation-Based Models: Optimizes the model's depth and width for different use cases without sacrificing performance.
- Trainable Bag of Freebies: Integrates various training techniques to enhance accuracy without increasing inference costs.
- Coarse-to-Fine Learning: Employs a hierarchical label generation method for both auxiliary and lead heads, improving overall learning.
Step-by-Step: Training a Custom YOLOv7 Model for NBA Player Detection
Ready to build your own object detector? Let's create a YOLOv7 model that identifies the ballhandler in NBA game footage.
1. Gathering and Preparing Your Dataset
- Source Videos: Download NBA highlight reels from YouTube.
- Frame Extraction: Use VLC's snapshot feature to convert videos into image sequences.
- Data Annotation: Annotate images with bounding boxes and labels using a tool like RoboFlow.
2. Labeling Your Data with RoboFlow
Easily label uploaded data. To label the data with RoboFlow once it is uploaded, all you need to do is click the “Annotate” button on the left hand menu, click on the dataset, and then drag your bounding boxes over the desired objects, in this case basketball players with and without the ball.
- Create Classifications: Define
ball-handlerandplayerlabels. - Annotation Strategy: Label each player as "player." The player with the ball is labeled "ball-handler" and not labeled as a player.
- Data Augmentation: Use RoboFlow's augmentation features to diversify your dataset.
Aim for at least 2000 images per class for optimal results. For this demo, we will be using 1668 training photos, 81 images for the test set, and 273 images for the validation set.
3. Setting up Your Environment
Use the following code snippets to prepare your environment:
!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/
Install necessary packages, downgrading Torch and Torchvision for YOLOv7 compatibility:
!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
4. Code Snippets for Data Handling
The file ‘data/coco.yaml’ is configured to work with our data.
Clean up extra files:
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)
Start Training Your Custom YOLOv7 Model
With your data prepared and environment set up, you're ready to train your custom YOLOv7 model. This guide provides the foundational steps and insights needed to create an effective object detection system. Fine-tuning your model with more data ensures even better reliability and accuracy.