Applied Computer Vision: Object Detection and Recognition

Laptop Imaginative and prescient and Object Recognition

This course offers a complete journey into laptop imaginative and prescient and object recognition, guiding you from the foundational ideas to superior mannequin implementation and analysis. By a hands-on method, you’ll discover key laptop imaginative and prescient duties similar to picture classification, object detection, semantic segmentation, and occasion segmentation. The course makes use of in style datasets like COCO-2017 and CamVid, and frameworks similar to PyTorch and FiftyOne to reinforce your sensible abilities.

Part 1: Introduction We start with an outline of the course and object recognition, adopted by organising the mandatory surroundings for environment friendly implementation.

Part 2: Recap of Convolutional Neural Networks (CNNs) This part refreshes your data of CNNs and introduces important instruments like FiftyOne for dataset administration, together with tutorials to get aware of PyTorch.

Part 3: Picture Classification You’ll study to construct and practice a multi-class picture classifier utilizing the COCO-2017 dataset, specializing in lessons like cats, canine, and horses. The classifier is constructed utilizing a pre-trained ResNet mannequin, demonstrating the method of switch studying and hyperparameter tuning.

Part 4: Object Detection We delve into object detection utilizing two in style fashions, Sooner-RCNN and YOLOv8. You’ll put together datasets, practice each fashions, and analyze their efficiency utilizing FiftyOne, gaining hands-on expertise with each region-based and single-shot detection strategies.

Part 5: Semantic Segmentation On this part, you’ll work with the CamVid dataset to grasp semantic segmentation, which entails assigning a category to each pixel in a picture. Utilizing the segmentation_models_pytorchlibrary, you’ll practice and consider a segmentation mannequin to acknowledge objects in scenes.

Part 6: Occasion Segmentation We cowl occasion segmentation, the place the purpose is to distinguish between a number of situations of the identical object class. You’ll construct and practice a Masks-RCNN mannequin for this job, working with segmentation annotations from the COCO-2017 dataset.

All through the course, we place a robust emphasis on hands-on workouts, real-world datasets, and mannequin analysis to equip you with the abilities wanted to deal with sensible laptop imaginative and prescient challenges. By the top, you can be well-prepared to implement and consider varied laptop imaginative and prescient fashions, with a stable understanding of the nuances concerned in several duties like classification, detection, and segmentation.