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Develop a custom YOLOv8 model for real-time basketball object detection, covering automatic labeling, model training, and live inference on game footage.
Learn to create a horse race detection model using deep learning without a pre-existing dataset. Extract frames, automate annotations, train YOLOv8, and apply the model to videos for real-time object detection.
Learn to implement underwater object detection using YOLO-NAS and Python. Covers model import, custom dataset training, and making predictions with bounding boxes for sea images.
Implement object detection for bone fractures using YOLOv8 and Python. Learn to load models, process images, and visualize predictions with hands-on coding examples.
Implement YOLOv8 for ship detection in sea views using Python. Learn data preparation, model training, and prediction visualization for effective object detection.
Learn to implement object detection for playing cards using YOLOv8 and Python. Covers model loading, data processing, training, and visualization of predictions and annotations.
Learn to boost YOLOv8's object detection using SAHI library. From installation to implementation, discover how to perform sliced predictions, visualize results, and compare performance with standard YOLOv8 for improved computer vision projects.
Learn to build a custom object detector using Detectron2. Step-by-step guide covers dataset preparation, model training, and making predictions on new images. Ideal for beginners in deep learning and computer vision.
Step-by-step guide to implement and train a Res-UNet model for Melanoma detection using TensorFlow and Keras. Learn to build, train, test, and visualize results for accurate skin lesion segmentation.
Step-by-step guide to implementing a U-Net model for melanoma detection using TensorFlow/Keras, covering data preparation, augmentation, model building, training, testing, and result visualization.
Build and train a U-Net pipeline to segment polyps in medical images, from preparing image-mask pairs to testing predictions on new images.
Build and train a U-Net that turns person images into binary segmentation masks, then run inference on new images.
Builds and trains a U-Net to produce animal segmentation masks, from preparing image-mask data through testing predictions on new images.
Learn to classify vehicles using VGG16 and XGBoost in TensorFlow. Covers data preparation, feature extraction, model training, and testing. Gain practical skills in object classification and deep learning.
Discover how to transform old, low-quality videos into full HD using AI-powered video enhancement tools, Python libraries, and GPU processing for superior resolution and clarity.
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