Capturing and Labeling Images for Training an Object Detection Model

📷Capturing high-quality images is an essential step in building a dataset for training an object detection model.

🏷️Labeling the objects in the images accurately is crucial for teaching the model to recognize and locate specific objects.

📐Drawing bounding boxes around the objects helps define their boundaries and provide reference points for the model.

💡Consider the application and environment in which the object detection model will be used when capturing and labeling images.

🔁Repeat the process with multiple images and perspectives to build a diverse and robust dataset for training the model.

How many images are needed to train an object detection model?

—For an initial model, at least 200 images are recommended, with at least 50 examples of each object.

Should the bounding boxes around objects overlap?

—Yes, it is acceptable for bounding boxes to overlap, especially if the objects are close to each other.

What if an object is partially obscured in an image?

—Draw a bounding box around the visible part of the object and label it accordingly.

What should I do if I'm unsure how to label an image?

—If you're confused or unsure about labeling an image, it's best to skip it and focus on clear and obvious examples.

What are some labeling tips for better model performance?

—Ensure clear and well-defined boundaries for objects, avoid cluttered images, and start with simple examples before tackling more challenging visual situations.

00:01Introduction to capturing and labeling images for training an object detection model.

01:03Considerations for the application and environment of the object detection model.

02:00Setting up the camera and capturing high-quality images of the objects.

04:17Labeling the objects using the label image tool and best practices for drawing bounding boxes.

06:26Tips for faster labeling using hotkeys and creating a predefined label map.

09:23Additional tips and guidelines for effective labeling and ensuring clear examples for the model.

12:35Completion of the labeling process and final thoughts on building a diverse and robust dataset.