YOLO Jobs

I need a robust YOLO model that spots vehicles with high accuracy. Because I do not yet have a labeled dataset, the job begins with sourcing or capturing varied vehicle images and annotating them in classic YOLO format. Once the dataset is in place, the next step is to train and validate the network—YOLOv5 or YOLOv8 are both fine as long as the final mAP holds up under real-world conditions. Please apply best-practice augmentation, tune hyper-parameters, and track training with clear metrics so I can reproduce the results later in PyTorch. Deliverables • Curated and fully annotated vehicle image dataset (bounding-box labels in YOLO txt format) • Trained YOLO weights and configuration files • Short report summarising dataset composition, training settings, and ev...

I have a fixed-angle camera that watches every truck as it rolls through a single gate into our yard. What I need is a reliable, end-to-end Python pipeline that will: • Detect and track each individual truck in real time with a pre-trained YOLO model, keeping the ID stable from the moment the vehicle enters the frame until it leaves. • Within that per-truck track, run additional YOLO passes (or custom classes) to locate the key regions I care about: license plates, DOT numbers, chassis numbers, container numbers, and container type markings. Accuracy on these regions is critical; false positives must be minimal. • Crop each detected region, perform OCR, and then clean the raw text with solid post-processing logic—regex, checksums, or any heuristic that standardises ...

I want to build a ChatGPT-style application that works exclusively with images and imposes no built-in content filters. The core capability is image analysis, specifically object detection and recognition, with an immediate focus on identifying people and faces in any photo a user uploads. You’re free to choose the tech stack, but I expect modern computer-vision frameworks—think PyTorch, TensorFlow, or a YOLO-family model—backed by a concise, well-documented API so the system can later expand into other tasks such as classification or captioning if I choose. Fast, server-side inference is important; cloud GPU deployment or an optimized on-prem setup is acceptable as long as latency stays low. Please include: • A clean front-end where users drop an image and i...

I want a small, self-contained Python program that opens my webcam, runs a YOLO pre-trained model, and draws labeled bounding boxes around the usual everyday items—person, bottle, mobile phone, etc.—whenever they appear in the frame. Please write it in clear, well-commented code that leans on OpenCV (cv2) for video capture and display. You’re free to pull the model weights from any reliable public source as long as setup remains simple (a short and one-step download script are perfect). Deliverables • A runnable demo script that starts the webcam and shows real-time detection • All source files, neatly organised, including a brief README explaining environment setup, how to launch the app, and where to place or fetch the YOLO weights • A short ...

I need a straightforward desktop application built in Python that can spot two key classes—Person and Mobile—in a sample video and draw clear bounding boxes with the class name over each detection. This is purely for demonstration and learning, so the implementation should stay clean and easy to read, ideally relying on OpenCV together with a pre-trained YOLO model (v5, v8, or any recent weight file you are comfortable with). How the app should behave • Load a local MP4 (or similar) sample video. • Run real-time inference frame-by-frame, highlighting every detected person or mobile phone. • Display the processed video in a simple desktop window; no fancy UI is needed beyond the live frame and the FPS readout. • Keep all dependencies to standard libr...