Open set classification of car models. This 3-step classifier solves the problem where dogs are classified as cars, by first filtering these images out using ResNet CNNs transfer-trained on different datasets.

The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China and by now has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths. With limited number of COVID-19 test kits available in medical facilities, it is important to develop and implement an automatic detection system as an alternative diagnosis option for COVID-19 detection that can used on a commercial scale. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Computer vision and deep learning techniques can help in determining COVID-19 virus with Chest X-ray Images. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural network for image analysis and classification. In this research, we have proposed a deep convolutional neural network trained on five open access datasets with binary output: Normal and Covid. The performance of the model is compared with four pre-trained convolutional neural networkbased models (COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the proposed model provides better accuracy on the validation set as compared to the other four pre-trained models. This research work provides promising results which can be further improvise and implement on a commercial scale.

Object detection and identification is one of the most important and challenging branches of computer vision, which has been widely applied in peoples’ life, such as monitoring security, autonomous driving, and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. By using Machine Learning and ResNet, we can easily identify the names of the objects which we needed. For this, firstly the training data is fed to the machine and labeled it correctly based on the nomenclature. By using the Camera Module, the test data is detected and verified with the train data using the ResNet algorithm. By repeated testing of the objects, the data set is updated or deleted based on the errors made by the machine in identification. On the repeated iteration of identifying the objects correctly ie., Accuracy reaching ≥ 95%, the dataset, and the application is used in Real World for automation. For this, I use Keras, an open-source neural-network library written in Python and by using the IoT module, the identified data is transferred to the Display device wirelessly. In Real-Time, this project is used for the identification of objects with more than 95% accuracy and transmit the data from anywhere and anytime using the cloud, and completely automate the process and reduces the manpower. ESP32 : Engineered for mobile devices, wearable electronics and IoT applications, ESP32 achieves ultra-low power consumption with a combination of several types of proprietary software. ESP32 also includes state-of-the-art features, such as fine-grained clock gating, various power modes and dynamic power scaling.

Lidar 3D object detection using FPN ResNet and Pytorch in the Waymo Open Data Set

PyTorch implementation of GNNs for face recognition. Features a Dual-Channel architecture combining GATv2 (geometry) and ResNet-18 (texture) to improve verification and identification in Open-Set scenarios.

# Image Classification using AWS SageMaker Use AWS Sagemaker to train a pretrained model that can perform image classification by using the Sagemaker profiling, debugger, hyperparameter tuning and other good ML engineering practices. This can be done on either the provided dog breed classication data set or one of your choice. ## Project Set Up and Installation Enter AWS through the gateway in the course and open SageMaker Studio. Download the starter files. Download/Make the dataset available. ## Dataset The provided dataset is the dogbreed classification dataset which can be found in the classroom. The project is designed to be dataset independent so if there is a dataset that is more interesting or relevant to your work, you are welcome to use it to complete the project. ### Access Upload the data to an S3 bucket through the AWS Gateway so that SageMaker has access to the data. ## Hyperparameter Tuning ResNet50 Model: * This project uses the ResNet-18 pre-trained model. * The ResNet-50 is a convolutional neural network that is 18 layers deep. This pre-trained version of the network is trained on more than a million images from the ImageNet database The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. * The dataset that we are going to use are an Image dataset which consist of images of dogs. * The dataset is divided into three parts training and validation and testing. For hyperparameters, I tuned the two following ones : - Learning rate : -> default is 0.001 and the chosen range is =```[0.0001, 0.1]``` -> learning rate is a ContinuousParameter. - epochs: -> defaut is 1e-08 and the chosen range is= ```[1e-9, 1e-8]``` -> the epochs is a IntegerParameter - Weight decay: -> default is 0.01 and the chosen range is = ```[1e-3, 1e-1]``` - Batch size : -> The chosen range is = ```[ 64, 128]``` -> batch-size is a CategoricalParameter **Best Training Jobs Hyperparameters after Tuning:**  **Optimizer: [Adam]** - Adam is a popular algorithm in the field of deep learning because it achieves good results fast. - learning_rate for Adam optimizer is a continuous parameter whose values are between ```[0.001, 0.01]``` - Adam is a popular algorithm in the field of deep learning because it achieves good results fast. - Computationally efficient. - Little memory requirements. - Also Well suited for problems that are large in terms of data and/or parameters. **Completed Training Jobs** **Logs Metrics During the Training Process** ## Debugging and Profiling - Debugging and Profiling was done with the help of the sagemaker.debugger module. - Amazon SageMaker Debugger provides full visibility into training jobs of state-of-the-art machine learning models. - This SageMaker Debugger module provides high-level methods to set up Debugger configurations to monitor, profile, and debug your training job. - Configure the Debugger-specific parameters when constructing a SageMaker estimator to gain visibility and insights into your training job.