Certificate in Object Detection

It covers dataset preparation, model architectures (Faster R-CNN, YOLO, SSD), training workflows, evaluation metrics, and deployment techniques.

Computer vision engineers, data scientists, software developers, AI students, and QA engineers working with vision applications.

Yes. Basic knowledge of neural networks and practical experience with a deep learning framework are recommended.

The exam is online, featuring multiple-choice questions and scenario-based problems with timed conditions.

You will work with Intersection over Union (IoU), mean Average Precision (mAP), precision, recall, and precision-recall curves.

Yes. The exam tests your ability to apply transfer learning and fine-tune pre-trained object detection models.

Yes. The course includes model optimization, quantization, and deployment on CPU, GPU, or edge devices.

Once you pass, your certification does not expire and remains valid indefinitely.

Practice with open datasets (COCO, Pascal VOC), experiment with different architectures, and review object detection tutorials and code examples.

Yes. You will be tested on multi-scale detection, attention mechanisms, and emerging transformer-based models.