YOLO Jobs

Poker Vision and GTO Analysis Tool — Developer Specification Objective Develop an offline application that analyzes recorded poker footage, screenshots, or manually entered hand data to detect the game state and generate GTO-based recommendations for post-session review and training. The system is intended for study and hand analysis only. It must not provide live in-game assistance. Scope The application should: • Detect hole cards, community cards, player positions, pot size, bet sizing, and approximate stack sizes from recorded media. • Reconstruct the full hand state in a structured format. • Pass the reconstructed state into a GTO analysis module or solver-based recommendation engine. • Display recommendations, alternative lines, and relevant metrics...

I need a YOLOv8 model trained to detect and classify approximately 12 types of fishing vessels from provided images. The dataset will be in YOLO/COCO format. Requirements: - Target accuracy: ~90%+ - Input image resolution: 1280x1280 - Performance metrics: Precision, Recall, F1 Score - Inference optimization: Balanced between speed and accuracy Deliverables: - Trained weights (.pt) - Training script - Inference code Ideal Skills & Experience: - Expertise in YOLOv8 and PyTorch - Experience with object detection and model training - Familiarity with performance metrics and optimization techniques

I’m building an MVP that inspects coal quality from both photos and short videos. The system must return a simple breakdown—e.g., “Coal 75 % / Stone 25 %”—and lay the groundwork for optional size-distribution analysis in later releases. What you’ll have to work with • A batch of truck-loading videos and corresponding lab quality reports. Phased scope Phase 1 – Extract still frames from the supplied videos and organise them into a clean, labelled image set. Phase 2 – Train a computer-vision model (YOLO or a comparable CNN in Python) able to distinguish coal from stone. Baseline accuracy is sufficient for now; we can iterate later. Both real-time and batch inference modes should be supported. Phase 3 – Wrap the model in...