Complete 5+ Deep Learning Projects: AI & ML Hands-On Project

Create a Yolo v7 emotion detection project using Roboflow and Google Colab, covering account setup, dataset creation, annotation, training, validation, and running the project in PyCharm.

Create a Roboflow account and set up an emotion detection project workspace, selecting object detection, classification, or instance segmentation, then publish a public project under CC BY 4.0.

Create a dataset for emotion detection using Yolov7 by sourcing images from Roboflow, Kaggle, Google images, and by selecting files, folders, or YouTube videos.

Annotate images in Roboflow with bounding boxes and polygons, using AI tools to label emotions like angry, sad, happy, and fearful, and set train, validation, and test splits for Yolov7.

Train Yolov7 model on an emotion detection dataset by creating a version, pre-processing and augmenting images and labels, downloading the dataset, and choosing train with Roboflow or custom train.

Validate a Yolov7 trained model on Roboflow by inspecting mAP, precision, and recall graphs, then drag and drop images or videos to test real-time emotion detection with a webcam.

Learn to run a PyCharm ide project with Roboflow hosted api options (on device, native sdk, cloud, utilities); import images and mp4s; generate annotation, blur, and count predictions using Yolov7.

Learn to build real-time face recognition with yolov7 on Roboflow by creating accounts and datasets, annotating images, training, validating, and running projects in PyCharm, with Roboflow and Google Colab guidance.

Create a Roboflow account and sign in with Google to set up a real-time face detection project, using YOLO v7. Choose object detection, classification, or instance segmentation.

Create a dataset for real-time facial recognition with yolo v7 by importing videos and images into Roboflow, generating frames, and preparing images for annotation.

Learn to annotate images on the Roboflow platform using bounding boxes, polygons, and smart polygon tools, assign class names, and manage train-valid-test splits for real-time face recognition projects.

Train your dataset with the YOLOv7 model on Roboflow by creating a new version, exporting as Yolov7 PyTorch, and selecting a fast training option, then validate in the next class.

Validate a Yolov7 model in Roboflow by inspecting mean average precision, precision, and recall, using drag-and-drop images or video to verify real-time face recognition with adjustable thresholds.

Navigate project execution in the PyCharm IDE using Roboflow, Python, and Yolov7 to run, predict, and visualize real-time face recognition with OpenCV and cloud options.

Introduce Roboflow and its features for custom object detection with YOLO v7, covering datasets, annotation, and cloud training without GPUs, plus setting up accounts and project workspaces.

Learn to create a Roboflow account, set up a project workspace, and choose a plan to build object detection, classification, and instance segmentation using Yolo v7 for helmet detection.

Create a helmet detection dataset using Google images and Kaggle datasets, then import photos into Roboflow. Use the Image Assistant extension to download images and target at least 1000 images.

Annotate helmet images using bounding box, smart polygon, and polygon tools on the Roboflow platform, then set 70/20/10 train/valid/test splits and prepare dataset for Yolov7 training.

Learn how to train a dataset with the Yolov7 model on Roboflow, including preparing, annotating, exporting to the Yolov7 PyTorch format, and training in the cloud without a GPU.

Validate the trained Yolov7 model on Roboflow by examining mean average precision, precision and recall, and loss graphs up to epoch 300, with drag-and-drop validation achieving 94% helmet detection.

Execute your helmet-detection project in the PyCharm IDE using Roboflow, install Roboflow, run Python code with the hosted API and Yolov7, and validate results from live predictions.

Explore google colab for free gpu and tpu training, create a project, set runtime to python 3 with gpu, connect a t4 gpu, and begin yolov7 helmet detection.

Import Yolov7 into Google Colab by cloning from GitHub, installing requirements, importing a Roboflow dataset for helmet detection, and retrieving the Yolov7 PyTorch file to prepare training data.

Train the Yolov7 model in Google Colab by running train.py with batch size and epoch settings, using data.yaml and weights.

Learn how to validate a trained PyTorch helmet-detection model in Google Colab using Yolov7, detect.py, and the best.pt weights, setting a confidence score and looping through images to display results.

Demonstrate downloading the trained PyTorch model from Colab, fetching Yolov7, extracting and organizing files, and preparing a helmet-detection project for deployment in PyCharm with Roboflow.