Detección de Objetos Jobs

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...

Hi, I am looking for a basic AI object detection demo. Requirement: - Detect common objects like person, bottle, mobile, etc. - Should work using webcam or sample video - Output should show bounding boxes with object names This is mainly for learning/demo purpose, so it doesn't have to be very advanced. Preferred: - Python with OpenCV or any simple framework - Basic explanation of how it works Deliverables: - Working demo (video or live) - Source code - Short explanation Budget is limited, but if work is good, I may come back with more tasks. Thank you.

I’m putting together a small outdoor rack and need an electronic sensing solution that can tell me, in real time, whether something has been placed on—or removed from—each shelf. Cameras are off-limits, so I’m looking for a clean sensor-based approach that works equally well with mixed materials such as metal, plastic, or any everyday composite an item might be made from. Because the rack will sit outside, the hardware must shrug off normal weather exposure—sun, light rain, the usual seasonal swings—but it doesn’t have to survive arctic freezes or tropical monsoons. A compact, low-power design is preferred, and I’m open to ultrasonic, IR, capacitive, load-cell, or any other sensor technology you feel is best suited to the job, provided it st...

I need to set up an end-to-end computer-vision workflow that detects specific objects in my video library. The raw footage is already organized; what it still lacks is accurate frame-level annotation, a robust detection model, and an easy way to run inference on new clips. Here’s what the project looks like from my side. First, every video frame that contains the target classes must be labeled with tight bounding boxes and class IDs. Once the ground-truth dataset is ready, I want a state-of-the-art detector trained—YOLOv5, YOLOv8, Detectron2, or another modern PyTorch/TensorFlow solution is fine as long as the mAP holds up in validation. After training, the model should be optimized for real-time inference (TTA off, ONNX or TensorRT export where possible) and tested on unseen ...