Studijní materiály (lectures)

1 - Intro: Autonomous Vehicle, Object Detection, Image Features
1.1 Appendix: sliding_window.mp4
2 - Convolutional networks: LeNet, PyTorch
3 - CNN Architectures: AlexNet, ZFNet, VGG, GoogLeNet, ResNet, DenseNet
3.1 - Appendix: Dropout, Pytorch Dataset
3.2 - Appendix: ResNet, SqueezeNet, EfficientNet
4 - Object Detection: RCNN, YOLO, SSD
4.1 Appendix: car_detection.mp4
5 - Generative Adversarial Networks (GANs)
5.1 Appendix: parking_gan.mp4
5.2 Appendix: parking_dcgan.mp4

Cvičení (exercises)

Name Description References
1. Parking Lot Analysis Experiments with classical edge detection, or Haar/HOG/LBP for parking lot analysis. video
2. Car Detection - Sliding Window Experiments with HOG and car localization. Create a program using a sliding window in combination with the HOG features. video
SVM sklearn
3. PyTorch/NN Link all parts together and add a testing phase and display the results using matplotlib. PyTorch_dataset
4. CNN - Pytorch Experiments with CNN and parking lot analysis. For homework, use trainimages (for training) and big parking images from (for testing) tutorial
5. RCNN/YOLO/SSD Experiments with RCNN (YOLO/SSD) - In the first step, the goal is to find all cars using RCNN methods (or YOLO/SSD). In the second step, use CNN for recognition of specific car (e.g. BMW). You can use training images from previous tasks or create new ones.
6. GAN Use GAN to extend the dataset in one of the above examples. Compare results with and without the extended set. video