Create Custom Yolo Object Detector for Game and News videos

Collect diverse training data by capturing screenshots from YouTube videos and other sources, label frames with text boxes and message boxes, and organize pre images for a custom yolo detector.

Split data into training and testing sets for a YOLO v3 detector, using training and testing prefixes to force key examples into both pools and improve model performance.

Set up a new Vott project by creating a source folder, copying images, and configuring local source and target connections. Enable only tagged assets in export before labeling.

Label images for a YOLO object detector in game and news videos using source-specific labeling, draw boxes around key regions, and consistently label all occurrences for training and testing.

Export and organize your vott project for yolo training by configuring export settings, generating labeled json annotations, and packaging training data into a zip ready for local or colab training.

build a yolo v3 model with docker, set up pipelines, and learn when Google Cloud or Colab is preferred while managing memory constraints on Windows and Mac GPUs.

Build a YOLO v3 model on Google Colab by preparing data with darknet, training in notebooks, and managing datasets and artifacts via Dropbox or Google Drive.

Verify your trained model by processing sample frames and a video through the verification repo, using local and remote modes and the frames, artifacts, and result folder.

Verify a yolov3 model works locally by running it against provided sample frames and a video feed, placing artifacts in the correct folders, and inspecting detections and labeling for accuracy.

Verify that a YOLO v3 model runs on Google Colab by performing a quick sanity check with sample frames from Dropbox, unzipping results, and comparing detections.

Conclude by exploring automatic text tracking and auto labeling, using a real-time visualizer to show detections and a fallback labeler to reduce false positives.