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AI based image classification inspired MobileNet V2 architecture by implementing changes in base architecture and details about using it as a quick response model (proposition) for rapid application as well as comparing it with other models for the same application.
Knocking occurs when fuel burns unevenly in your engine. When everything is going as it should, and the cylinders have the correct mix of air and fuel, the mixture burns in a controlled, progressive manner. After each cylinder's air/fuel mixture burns, it should create a small “shock wave” in your engine. This project is a knocking prediction app.
Deep CNN models ResNet34 and VGG16 have been trained and tested for image classification task using MNIST and CIFAR dataset (part of mini-project from the Deep learning and computer vision module)
U-Net segmentation algorithm with options of pretrained resnet34 and resnet50 encoders. All of the project dockerized with gpu suppport on anaconda environment with multiple loss support..