Train Your Own Object Detector: A Practical Guide to Custom YOLOv7 Models

Updated: September 17, 2024

Want to build a custom object detection system? This guide walks you through training your own YOLOv7 model for specialized tasks, from gathering training data to deploying your finished model. This article provides the steps to develop a custom object detector model for your object detection task.

What is YOLOv7, and Why Use It?

YOLOv7 is the latest iteration of the popular "You Only Look Once" object detection algorithm. YOLO object detectors are the choice of many due to their speed, accuracy, and relative ease of use. It combines image classification and object identification, locating objects within an image and correctly classifying them. YOLOv7 stands out, offering significant improvements over previous versions.

Is YOLOv7 right for your project?

• Real-time object detection: If you need rapid analysis of video feeds or images, YOLOv7's speed is a major advantage.
• Custom detection tasks: Train YOLOv7 on your specific dataset to identify unique objects or scenarios.
• Edge deployment: Run YOLOv7 on resource-constrained devices for on-site analysis.

Prerequisites for Training Your YOLOv7 Model

Before diving in, make sure you have:

• Python experience: Familiarity with Python code is essential.
• Deep learning basics: A foundational understanding of deep learning concepts.
• Sufficient hardware: Access to a machine powerful enough to run the code. A GPU (like DigitalOcean GPU Droplets) is highly recommended for faster training.

Understanding the Core Principles of YOLO

YOLO (You Only Look Once) revolutionized object detection by processing an entire image in a single pass. Here's how it works:

1. Grid division: The image is divided into N equal-sized S×S grids.
2. Object detection: Each grid cell predicts bounding boxes, object labels, and confidence scores.
3. Non-Maximal Suppression: YOLO uses Non-Maximal Suppression to filter out redundant bounding boxes, keeping only the most confident and accurate detections.

Key Improvements in YOLOv7

YOLOv7 boasts several architectural enhancements, resulting in superior performance:

• Extended Efficient Layer Aggregation Networks (E-ELAN): Improves the network's learning capacity without disrupting the gradient path, leading to better accuracy.
• Model Scaling for Concatenation-Based Models: Optimizes network depth and width for different use cases, maintaining efficiency across various model sizes.
• Trainable Bag of Freebies: Employs techniques like re-parameterized convolution to enhance training and improve model performance.
• Coarse-to-Fine Supervision: Uses a lead head for final output and an auxiliary head for assisted training, generating hierarchical labels for improved learning.

Step-by-Step: Training Your Custom YOLOv7 Model

1. Gather and Prepare Your Custom Dataset

This is crucial for a successful custom YOLOv7 model.

• Collect images or video: Source data relevant to your object detection task.
• Extract image frames: If using video, convert it into a series of images. VLC media player offers a convenient snapshot filter.
• Labeling your data for training YOLOv7: Accurately label each object of interest in your images. Tools like Roboflow simplify this process.

2. Data Annotation with RoboFlow

RoboFlow provides a user-friendly interface for labeling your data:

• Create an account & project: Sign up and start a new project.
• Upload your images: Add your collected and extracted images to the project.
• Define classifications: Specify the object types you want to detect (e.g., ball-handler, player).
• Draw bounding boxes: Annotate each object in your images with a bounding box and its corresponding label.

3. Choosing the Correct Data

• Strategic Tagging: A good tip is to avoid confusion by not double-labeling. So the ball handler is not double labeled as a player in any frames.
• Image Count: Aim for roughly 2000 images per classification for best results.
• Dataset Split: Divide your data into training, testing, and validation sets. A common ratio is 70% training, 15% testing, and 15% validation.

4. Code Implementation: Putting it all Together

Follow these steps to set up your custom YOLOv7 model for training:

• Install required packages, use code below:

 !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

5. Run this code snippet

To access a demo dataset, use:

curl -L "https://app.roboflow.com/ds/4E12DR2cRc?key=LxK5FENSbU" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip

6. Fine-tuning

Load the required data and the model baseline to fine-tune:

 !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 Steps: Evaluate and Refine Your Model

After training, evaluate your model's performance on the test dataset and new, unseen data. Fine-tune your training parameters, add more data, or adjust your labeling strategy to improve accuracy.