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Master fine-tuning ConvNeXT Vision Transformer for custom dog breed classification using PyTorch and Hugging Face. Learn to process datasets, implement training loops with validation, and deploy your model for accurate predictions.
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.
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.
Learn to implement and train a U-Net model for animal segmentation using TensorFlow/Keras. Covers data preprocessing, model architecture, training, and evaluation with practical code examples.
Learn to implement and train a U-Net model for person segmentation using TensorFlow/Keras. Covers data preprocessing, model architecture, training, and evaluation with practical code examples.
Step-by-step guide to implement U-Net for medical polyp image segmentation using TensorFlow/Keras, covering data preprocessing, model architecture, training, and evaluation.
Learn to segment dogs in images and videos using YOLOv8 and Python. Train AI, isolate dogs from backgrounds, and merge with custom scenes. Step-by-step guide with code included.
Learn to build and train a custom Detectron2 model for instance segmentation on your own dataset, covering annotation, training on Windows and Linux, and testing on new images.
Learn to detect floods using Yolo V8 image segmentation. Covers dataset preparation, model training, and testing on new images to generate segmentation masks highlighting flooded areas.
Learn to build an advanced object detection system using DETR, covering data preparation, model implementation, training, and inference with PyTorch, Transformers, and PyTorch Lightning.
Learn to build a YOLOv8-based dog breed detection system using deep learning. Master data preparation, model training, and making predictions with bounding boxes.
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.
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