Complete Object Detection Using YOLOv7 Project From Scratch

Title: Custom Object Detection Using YOLOv7 with Roboflow and Google Colab

Course Description:

This practical course is designed for individuals eager to dive into the world of custom object detection using YOLOv7. We’ll guide you through the process of creating and training a YOLOv7 model using the Roboflow platform for dataset management and Google Colab for GPU-accelerated model training.

Key Learning Objectives:

1. Introduction to YOLOv7 and Roboflow:
   • Gain an understanding of the YOLOv7 architecture and the Roboflow platform for seamless dataset preparation.
2. Setting Up Roboflow Account:
   • Create an account on Roboflow and learn how to use its intuitive interface for dataset organization and preprocessing.
3. Uploading and Annotating Datasets:
   • Explore the process of uploading datasets to Roboflow and annotating images with bounding boxes for object detection tasks.
4. Generating YOLO-Compatible Dataset:
   • Understand how to generate YOLO-compatible datasets on Roboflow for efficient integration with YOLOv7.
5. Exporting Datasets to Google Colab:
   • Learn how to export your prepared dataset from Roboflow and set up a Google Colab notebook for model training.
6. Installing YOLOv7 on Colab:
   • Execute the necessary commands to install the YOLOv7 repository and dependencies on Google Colab.
7. Custom Configuration for YOLOv7:
   • Understand how to modify the YOLOv7 configuration files to suit the requirements of your specific object detection task.
8. Training YOLOv7 on GPU:
   • Utilize the GPU capabilities of Google Colab to train your custom YOLOv7 model efficiently.
9. Model Evaluation and Export:
   • Evaluate the trained model’s performance and export it for further use in inference.
10. Inference and Object Detection Testing:
    • Use the trained YOLOv7 model to perform object detection on new images or videos and test its accuracy.
11. Fine-Tuning and Iterative Training:
    • Explore the concept of fine-tuning and iterative training for model improvement.
12. Project Deployment:
    • Discuss various options for deploying your custom object detection model in real-world scenarios.

Prerequisites:

Participants are expected to have:

• Basic programming skills in Python.
• Familiarity with machine learning concepts.
• A Google account for accessing Google Colab.

Who Should Attend:

• Students and professionals interested in computer vision and object detection.
• Data scientists and machine learning practitioners.
• Individuals wanting hands-on experience with YOLOv7, Roboflow, and Google Colab.

Materials Needed:

• A computer with internet access.
• Google account for Colab access.
• Roboflow account (free tier available).

Assessment:

Participants will be assessed based on the successful completion of hands-on assignments, including dataset preparation, model training, and inference tasks.

Join us on this practical journey and empower yourself to create custom object detection solutions using YOLOv7 with the help of Roboflow and Google Colab