Object Detection Jobs

I need a clear, evidence-based report that compares the performance of today’s most widely cited object-detection algorithms. The focus is strictly on Computer Vision, zeroing in on Object Detection, and the core goal is to evaluate how the main approaches stack up against each other in terms of accuracy, speed, computational cost, and real-world suitability. Scope • Analyse at least three state-of-the-art methods—think Faster R-CNN, SSD, YOLO (v7/8), DETR or similar. • Draw all claims from peer-reviewed journals, top-tier conference papers, or authoritative benchmark leaderboards (e.g., COCO, PASCAL VOC). • Present metrics consistently (mAP, FPS, FLOPs, params, latency) so direct comparison is effortless. • Highlight strengths, weaknesses, and trad...

I need a clear, evidence-based report that compares the performance of today’s most widely cited object-detection algorithms. The focus is strictly on Computer Vision, zeroing in on Object Detection, and the core goal is to evaluate how the main approaches stack up against each other in terms of accuracy, speed, computational cost, and real-world suitability. Scope • Analyse at least three state-of-the-art methods—think Faster R-CNN, SSD, YOLO (v7/8), DETR or similar. • Draw all claims from peer-reviewed journals, top-tier conference papers, or authoritative benchmark leaderboards (e.g., COCO, PASCAL VOC). • Present metrics consistently (mAP, FPS, FLOPs, params, latency) so direct comparison is effortless. • Highlight strengths, weaknesses, and trad...

I'm looking for an experienced programmer to help me set up a Raspberry Pi personal assistant. This project involves coding and configuring the Raspberry Pi, which is equipped with a camera and microphone, similar to Loona the pet robot. Key functionalities include: - Facial Recognition: Identify and recognize individuals. - Object Detection: Recognize and categorize different objects in the environment. Ideal skills and experience: - Proficiency in Python and Raspberry Pi - Experience with OpenCV or similar libraries for facial recognition and object detection - Familiarity with setting up and configuring Raspberry Pi for various projects - Strong problem-solving skills and attention to detail Please provide examples of similar work done and ensure you can meet the requirements.

I have just 700 indoor photos that must be labelled for an my project upcoming object-detection model. Every visible person and every seat that is currently occupied needs its own bounding box, and because the network will train on oriented-bounding-box (OBB) data, the rectangles have to follow the exact rotation of the body or seat—especially when someone is leaning. Data is of infrared camera of a cinema check samples , please note there can be many people in one image i want each to be marked properly You will work inside the simple browser-based annotation tool I built for this purpose or any other annotation tool you like. drag the box corners, spin the angle handle where needed, hit save, and move to the next frame; the app tracks progress automatically so nothing is missed....

I have just 700 indoor photos that must be labelled for an my project upcoming object-detection model. Every visible person and every seat that is currently occupied needs its own bounding box, and because the network will train on oriented-bounding-box (OBB) data, the rectangles have to follow the exact rotation of the body or seat—especially when someone is leaning. Data is of infrared camera of a cinema check samples , please note there can be many people in one image i want each to be marked properly You will work inside the simple browser-based annotation tool I built for this purpose or any other annotation tool you like. drag the box corners, spin the angle handle where needed, hit save, and move to the next frame; the app tracks progress automatically so nothing is missed....