...project (source code) - APK file - Clear setup instructions: - How to install APK on tablet - How to connect webcam - How to run the app Existing Code (Important) We will provide: - PDF explaining the tracking system (AI + smoothing logic) - Setup guide from previous developer - Existing project files The system includes: - Face tracking (MediaPipe) - Body tracking fallback (YOLO) - Smooth camera motion (PID control) You will need to: - Review and adapt this code for the new webcam input Preferred Tech Stack Developers may use: - Java or Kotlin (Android Studio) - Experience with: - Camera2 API OR USB (UVC) camera integration - MediaPipe / TensorFlow Lite (preferred) - OpenCV (bonus) Important Notes - No backend required - No database required - Foc...
...Upload CT scan or X-ray images AI detects lung nodules Cancer risk prediction Heatmap highlighting suspicious areas Doctor dashboard Patient history tracking PDF medical report generation Technologies Frontend: Vue.js / React Backend: Node.js or Python Flask AI Model: TensorFlow / PyTorch Database: MySQL / PostgreSQL Image Processing: OpenCV AI Models CNN (Convolutional Neural Network) ResNet50 YOLO for object detection Users Doctors Radiologists Hospitals Patients 2. AI + IoT Lung Monitoring System A smart healthcare platform connected to wearable devices. Features Real-time breathing monitoring Oxygen level tracking AI predicts lung disease risk Emergency alerts Mobile app notifications Patient monitoring dashboard Hardware ESP32 Pulse Oximeter Sensor Temperature Sensor AI Fu...
...000-camera architecture with high availability and low latency • Optimize DeepStream pipelines (NvInfer, NvTracker, NVDEC, GStreamer) for stable FPS under heavy load • Deploy an manage CV models on Jetson or equivalent edge devices • Fine-tune YOLO models and export optimized TensorRT engines • Build and improve infrastructure using Docker, Kafka/MQTT, GPU clusters, load balancing, and autoscaling • Improve real-time analytics dashboards and monitoring systems • Document deployment workflows and architecture decisions Required Skills: • NVIDIA DeepStream SDK • YOLO v5/v8/v9 • TensorRT, ONNX, TAO Toolkit • Jetson Nano/Orin • Docker, Kafka/MQTT, Nginx • RTSP, ONVIF, H.264/H.265 • Python and C++ Experience Req...
I have a growing library of MP4 recordings of casino-grade slot machines and I need a reliable way to turn each session into structured data. For every spin I want the script to capture the start time, end time, bet size, win amount, any bonus triggers, and jackpots, then tally an overall spin count. The video...example command that reproduces your results on my sample MP4s Acceptance criteria • Works on at least three different game layouts without manual retuning • ≥99.95 % accuracy on spin count, bet size and win amount when compared with my hand-labeled ground truth • Correctly flags 100 % of visually distinct bonus/jackpot events in the provided test set Feel free to use OpenCV, Tesseract, YOLO, or any modern deep-learning framework—whatever achi...
...timeout → auto ambulance call with GPS location + live camera image. --- CANCEL WINDOW: Via: app button / numeric code / voice codeword / voice recognition (POST /api/alarm/cancel). Time window configurable per alarm level. No cancel → auto escalation to emergency services. --- FALSE ALARM SUPPRESSION: - Steam in bathroom: ignore smoke trigger if humidity > 80% + person present - Pets: YOLO class filter — only humans trigger intrusion alarm - Battery beeping: audio pattern match, suppress + log for morning report - Audio classification: glass breaking, water sounds, smoke detector patterns --- PRIVACY ZONES (must be respected): - Bathroom: no cameras ever. Radar sensor only (fall detection). - Bedroom: cameras with physical shutter, def...
I am looking for an expert to develop a high-performance, 8-channel automated farming system for the 3D tactical shooter Delta Force. My requirements: Technical Skills & Experience: - Proven experience in real-time computer vision (YOLO, TensorRT) with batch inference and low latency on RTX 4070Ti/4060Ti GPUs. - Strong proficiency in C++ (or high-performance Python with CUDA/C++ backend) and PCIe capture card integration. - Familiarity with KMBOX Net HID-level mouse/touch emulation and anti-cheat evasion (Tencent ACE or similar). - Experience designing centralized dashboards and robust fail-safe management for 24/7 operations. Core Deliverables: - Real-time detection (loot, enemies, extraction points) and UI state recognition via 8x 1080p 60Hz streams. - Visual navigation usi...
...from double-bag events and still keep a reliable tally. Boxes and tins also come through the same point, each in more than one size, so the detection logic has to cope with differing dimensions rather than relying on a fixed template. Here’s the workflow I have in mind: • Your application pulls the RTSP/HTTP stream from my existing IP cameras. • A computer-vision model (OpenCV, TensorFlow, YOLO or similar) detects the item type, counts it, and determines the direction of movement—onto or off the truck. • Counts are logged with time-stamps and can be viewed on a simple web dashboard and exported as CSV. • If the camera loses connection or the count confidence falls below a threshold, the system raises an on-screen alert so we can check manu...
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 compositio...
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...
...detection 3. Character segmentation and OCR 4. Output structured data: * Vehicle number * Timestamp * Image snapshot 5. Support for: * Day and night conditions * Motion blur handling * Non-standard Indian number plates 6. Offline operation (no cloud dependency) Technical Requirements:** * Strong experience in Computer Vision and Deep Learning * Hands-on experience with: * YOLO / SSD (object detection) * OCR models (CRNN, LPRNet, Tesseract improvements) * Experience with: * OpenCV * PyTorch / TensorFlow * RTSP stream handling (FFmpeg / GStreamer preferred) * Experience deploying models on: * Linux systems * Edge devices (Jetson preferred) --- **SDK Requirements:** * Deliverable must be a reusable SDK (not just an application) * Provide APIs ...
...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...
...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...
...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 libraries plus OpenCV, torch/onnxruntime (if required for your chosen YOLO flavour), and any lightweight helper you feel is essential. Because I only need a basic overview of how it works, a conci...
Vær venlig at Tilmelde dig eller Log ind for at se detaljer.
...am looking for an experienced Computer Vision / AI developer to build a real-time video processing prototype. Scope of work (Phase 1): - Connect to RTSP stream (IP camera / NVR) - Process live video feed - Implement human detection using a lightweight model (e.g., YOLO or similar) - Display bounding boxes on detected objects - Ensure near real-time performance (minimum 8–10 FPS) Technical requirements: - Strong experience with Python and OpenCV - Experience handling RTSP streams - Familiar with object detection models (YOLO, TensorFlow, PyTorch, etc.) - Experience with video pipelines (FFmpeg / GStreamer is a plus) - Able to run solution on local machine (no cloud dependency) Deliverables: - Working demo (video or live screen) - Source code - Setup instructions...
...clips that isolate each participant’s key moments. • Overlay a configurable sponsor logo on every generated clip. • Add seamless WhatsApp integration so the system can push the personalised clips straight to athletes or organisers. • Clean up, document and hand over the code so the next developer—or I—can maintain it easily. Tech context The previous developer used Python with OpenCV and a YOLO-style model; feel free to refactor or swap frameworks as long as inference remains lightning fast. Expect video streams up to 4K, multiple concurrent cameras and fairly busy backgrounds. Success criteria • ≥95 % accurate bib recognition measured on my test footage. • Clip creation and logo overlay processed within 30 s of finish-line ...
...Output Clean, editable AutoCAD DWG file containing: Correctly placed blocks for all detected casework, sinks, fixtures, shelving, etc. Accurate dimensions (height, width, spacing) Countertop outlines with detailed top-view information Section views based on cabinet types Proper layers, line types, and drafting standards Visual flags / notes for any unmatched or uncertain items Core Features YOLO-based (or equivalent) object detection for cabinets, sinks, pegboards, etc. Vision LLM assistance for annotation and context understanding Block matching engine with fallback logic Natural language rule engine (“teach” the AI in plain English) Self-training interface (view, edit, enable/disable rules) Drawing-based learning (compare AI DWG vs. user-corrected DWG) Confidenc...
...detection 3. Character segmentation and OCR 4. Output structured data: * Vehicle number * Timestamp * Image snapshot 5. Support for: * Day and night conditions * Motion blur handling * Non-standard Indian number plates 6. Offline operation (no cloud dependency) Technical Requirements:** * Strong experience in Computer Vision and Deep Learning * Hands-on experience with: * YOLO / SSD (object detection) * OCR models (CRNN, LPRNet, Tesseract improvements) * Experience with: * OpenCV * PyTorch / TensorFlow * RTSP stream handling (FFmpeg / GStreamer preferred) * Experience deploying models on: * Linux systems * Edge devices (Jetson preferred) --- **SDK Requirements:** * Deliverable must be a reusable SDK (not just an application) * Provide APIs ...
...User guide for running the system • Instructions for adjusting output styles and using custom backgrounds 5. Source Files • Complete source code • Trained models (if custom training is involved) • Configuration templates Ideal Candidate Profile We're looking for someone with: Required Skills: • Strong experience with AI/ML video processing • Expertise in computer vision (OpenCV, MediaPipe, YOLO) • Knowledge of generative AI models for video (Stable Diffusion, ControlNet, AnimateDiff, or similar) • Proficiency in Python and video processing libraries (MoviePy, FFmpeg, PyTorch/TensorFlow) • Experience with style transfer or video-to-video translation • Experience with video compositing and background replacement techniques...
...Tích hợp code với Camera AI / Camera thông minh để quét hình ảnh realtime Xử lý hình ảnh đầu vào từ camera Detect viền đế giày, bề mặt tiếp xúc, độ nghiêng Kẻ line trực quan thể hiện phần cao thấp lệch nhau Tính toán sai số / độ chênh lệch theo pixel hoặc mm Tối ưu tốc độ xử lý để chạy ổn định realtime Bàn giao source code hoàn chỉnh Yêu cầu ứng viên: Thành thạo Python / C++ Có kinh nghiệm OpenCV, YOLO, TensorFlow, PyTorch hoặc thư viện AI tương đương Đã từng làm dự án camera công nghiệp / camera AI / vision inspection là lợi thế lớn Biết xử lý realtime stream RTSP / USB Camera Chủ động, làm việc đ&...
...plug-and-play as possible. When a new football pitch is added: 1. Cameras are connected 2. The local computer is configured 3. The software is installed 4. Data automatically syncs to the main system --- # Technical Requirements The freelancer should have proven experience with: * Computer Vision * Multi-object tracking * Re-identification (ReID) * DeepSORT, ByteTrack, BoTSORT or similar systems * YOLO, Detectron or similar detection models * Multi-camera synchronization * Cross-camera player tracking * Real-time video processing * GPU optimization * Web panel development * Backend and database development * API development Preferred experience: * Sports analytics * Football / basketball tracking projects * NVIDIA GPU optimization * Multi-camera systems Please share: * ...
I need a rock-solid, real-time player tracking module for football matches that guarantees the ID assigned to each athlete at kick-off never changes until the final whistle. Right now, our OpenCV–TensorFlow–YOLO pipeline sometimes swaps or loses IDs when athletes overlap, leave the frame briefly, or the camera angle shifts, and that ruins every speed, distance, position, and heat-map metric we generate. Key requirements • Sport: football. • Camera setup: five or more synchronized feeds. • Existing stack: OpenCV, TensorFlow, YOLO – your solution must plug into this environment. What I expect 1. A multi-object tracker with integrated re-identification that preserves the same unique ID through occlusion, crossings, short disappearances, or ...
...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 trade-offs instead of simply listing ...
...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 trade-offs instead of simply listing ...
Hello, I have been working in data annotation for almost 3 years, gaining extensive experience in annotations. This makes me a valuable addition to your team. In addition, I have much experience in image annotation, segmentation, bounding boxes, polygons, key points, 2D and 3D annotations, and even LIDAR annotations. Tools: C... and even LIDAR annotations. Tools: CVAT Roboflow LabelImg Labelbox VGG Doccano Label Studio Annotation Solutions: Bounding Boxes Image annotation Object labeling/tagging Semantic Segmentation Polygons Annotation/masks Polylines Annotation Key Points annotation Sentiment, Text & Topic Analysis Image classification and categorization Object Tracking Data ...
...Output Clean, editable AutoCAD DWG file containing: Correctly placed blocks for all detected casework, sinks, fixtures, shelving, etc. Accurate dimensions (height, width, spacing) Countertop outlines with detailed top-view information Section views based on cabinet types Proper layers, line types, and drafting standards Visual flags / notes for any unmatched or uncertain items Core Features YOLO-based (or equivalent) object detection for cabinets, sinks, pegboards, etc. Vision LLM assistance for annotation and context understanding Block matching engine with fallback logic Natural language rule engine (“teach” the AI in plain English) Self-training interface (view, edit, enable/disable rules) Drawing-based learning (compare AI DWG vs. user-corrected DWG) Confidenc...
...device connectivity. -Vehicle number plate recognition / LPR systems. -Cloud architecture, DevOps, and deployment pipelines. -QA and testing for real-time and AI-driven systems. Preferred Technical Stack Experience Experience with the following technologies will be highly valuable: or similar dashboard frameworks. , Python, or similar backend stacks. -OpenCV, TensorFlow, PyTorch, YOLO, FaceNet, DeepFace, EasyOCR, or OpenALPR. -FFmpeg, WebRTC, or GStreamer for video processing. -Docker, Kubernetes, CI/CD, and cloud deployment. -Secure authentication, RBAC, logging, and scalable system architecture. Who Should Apply: We are looking for freelancers who can contribute to a serious, real-world, production-grade platform. This project requires people who understand the complexity
I need a computer-vision pipeline that can ingest an uploaded logo image (PNG or JPG) together with a video file and then return every instance of that logo throughout the clip. The videos that come in wi...whatever image is uploaded. I’m after well-commented, maintainable code, and I’ll need brief setup instructions so I can reproduce your results locally (Python virtual-env, , maybe a Dockerfile). Fast turnaround is important—I’d like to start reviewing working code as soon as possible. If you’ve already built anything similar—logo finders, object matchers, ORB/SIFT/SURF pipelines, YOLO or Faster-RCNN tweaks—tell me about it. Strong communication during the build is a must, and please start your reply with “SPORTS” so I kno...
...automates baggage screening and validation at airport checkpoints. The unit already houses a Logitech C920 Pro HD webcam, upper-mounted ultrasonic/IR distance sensors, and a base weight scale; your code has to read from all three in real time, stream the data through a unified pipeline, and return a clear decision to the passenger-facing touchscreen. Core vision logic The heart of the project is a YOLO-based model fine-tuned on our curated luggage set. My primary goal is accurate baggage classification; precise dimensioning and weight checks come next, so the model must fuse vision, distance, and scale readings to flag oversize or overweight items. You are free to optimise in PyTorch or TensorFlow, but the final network should run locally on the kiosk and expose an optional Go...
...vision (YOLO, vision LLMs, or similar) GPT-4o / Claude vision API Python (OpenAI API, PyMuPDF, Pillow) Experience generating editable DXF files (ezdxf or similar CAD libraries is mandatory) Background in AEC / CAD automation is highly preferred Timeline: Within 2 weeks (flexible – longer is acceptable if higher quality). How to apply: Please reply with: Brief description of how you would approach this (vision LLM + YOLO + DXF generation?) Any similar past projects (especially CAD/DXF automation or technical drawing conversion) Estimated time to deliver the working POC using the sample files I will provide the full Before and After PDFs attached. Can provide more if needed. I'm guessing this would be the pipeline: Architectural Elevation (A407 or similar) ...
...config • Help chatbot — embedded support bot • Clip export — MP4, 9:16 TikTok, 16:9 YouTube --- REQUIRED TECH STACK Frontend: + React + Tailwind CSS Backend: Laravel, Django, or FastAPI (your choice) Database: Supabase (PostgreSQL + real-time) AI/GPU: Python + Hugging Face (VideoMAE, CLIP, Whisper) on RTX 3080 Queue: Redis + BullMQ (priority by tier) Storage: Cloudflare R2 Video: FFmpeg + YOLO + MediaPipe Payments: PayPal REST API Monitoring: Prometheus + Grafana --- WHO I'M LOOKING FOR • You have shipped at least one full SaaS product end-to-end • You are comfortable with GPU model deployment (Hugging Face, Docker) • You've worked with BullMQ or similar job queues • You write clean, maintainable code — not just co...
Title: Image Annotation Specialist (LabelMe) + YOLO Dataset Preparation (34–36 Classes) — Phase 1: Image Scrubbing & Classification Description: I am building an AI-based visual inspection system using real-world highway images and need support preparing a high-quality dataset for YOLO training. This is a structured, multi-phase project. Accuracy, consistency, and attention to detail are critical. ⚠️ Important: This job will begin with Phase 1 only (image scrubbing and classification). Further phases (annotation and YOLO dataset preparation) will follow based on performance. --- Scope of Work: Phase 1 – Image Scrubbing & Pre-Classification (Current Phase) This is the most critical step of the project. * Review large volumes of real-wo...
...angles Detect key events (e.g. impacts, direction changes, motion patterns) Sync and utilise audio signals where relevant (optional but preferred) Output structured data (event timestamps, classifications) Optimise for performance and real-world conditions (lighting, motion, occlusion) Preferred Skills: Strong experience with Python and computer vision libraries (OpenCV, etc.) Experience with YOLO or similar object detection frameworks Experience working with sports or motion tracking (preferred but not essential) Understanding of video processing and frame analysis Familiarity with tools like Roboflow or similar platforms is a plus Experience integrating with mobile or lightweight systems is beneficial Nice to Have: Experience with multi-camera calibration Audio signal proce...
Hello Everyone This job is very simple: I have a set of 900 pictures of toy animals. There are 10 different toy animals. In each of the pictures, lable the animal(s) correctly. Download the Free Software "labellmg" or use your own image labler. Use the "PASCAL VOC" method, not the "YOLO". The Labels: Please use these EXACT labels. If you label it differently, the label renders useless. t_contessa t_muhriel t_elisabeth t_lucky t_heisi t_zanki t_darco t_hawking t_brummelbatz t_kleckser The animals: You will receive the label instructions similar to the attached picture and the image set after accepting my job! Note: "t_contessa" is the large cow. "t_muhriel" is the little cow. "t_brummelbatz" is the large donkey. "t_...
I am looking for a developer to train a custom YOLO model (YOLOv8, YOLOv11, or the newer YOLOv12/v26) specialized in detecting and tracking objects in real-time video. The primary focus is the mussel, and the model must distinguish between two specific classes: "mussel" (individual lost mussels) and "group" (clusters). Project Requirements: * Real-time Performance: The model will be used with a live camera feed. It must maintain at least 15 FPS on a standard NVIDIA GPU, prioritizing accuracy without sacrificing the fluid processing required for live monitoring. * Counting & Tracking: The system must count every lost mussel per frame and maintain consistent IDs (Object Tracking) to follow individual movements over time. * High Confidence: It must identify ...
I’m building a camera-based system that runs on an NVIDIA Jetson and, in real time, detects faces and recognises emotions. The entire solution must be coded in Python. For face localisation I’d like a fast deep-learning detector—SSD or YOLO—so the frame rate stays smooth on Jetson hardware. Once a face is found, a TensorFlow model should assign an emotion label (happy, sad, angry, surprised, neutral, etc.) together with its confidence score. The video stream has to overlay these results live, log every reading with a timestamp, and trigger a visual or audible alert whenever negative emotions are detected repeatedly within a short window. A lightweight dashboard served with either Streamlit or Flask will let me: • watch the annotated video feed •...
I am looking for a developer to train a custom YOLO model (YOLOv8, YOLOv11, or the newer YOLOv12/v26) specialized in detecting and tracking objects in real-time video. The primary focus is the mussel, and the model must distinguish between two specific classes: "mussel" (individual lost mussels) and "group" (clusters). Project Requirements: * Real-time Performance: The model will be used with a live camera feed. It must maintain at least 15 FPS on a standard NVIDIA GPU, prioritizing accuracy without sacrificing the fluid processing required for live monitoring. * Counting & Tracking: The system must count every lost mussel per frame and maintain consistent IDs (Object Tracking) to follow individual movements over time. * High Confidence: It must identify ...
...videos or live webcam stream) and detect objects/events in real time. Key Requirements: * Detect objects such as people, vehicles, or custom categories * Support live webcam streaming and/or video uploads * Display detection results visually (bounding boxes, labels) * Provide backend processing and API integration * Ensure good performance and low latency Preferred Technologies: * AI Models: YOLO, OpenCV, or similar * Backend: Python (FastAPI/Flask) or Node.js * Frontend: JavaScript (React or similar) * Optional: Experience with cloud platforms like AWS or Google Cloud Nice to Have: * Experience with real-time video processing * Ability to optimize models for performance * Knowledge of deploying AI systems Deliverables: * Fully working feature integrated into website * Cl...
...traffic-analytics pipeline that ingests live IP camera streams, runs fast and accurate inference, and pushes results reliably from edge devices. The current target hardware is NVIDIA Jetson, so every design choice—from model architecture to post-processing—must respect its compute limits while still keeping total end-to-end latency under 200 ms. The core work revolves around training, tuning, and deploying YOLO-style detectors in PyTorch (TensorFlow knowledge is welcome if it helps optimisation). You will refine the models for two challenging scenarios that matter most to our roadside installations: low-light environments and high-speed vehicle movement. Image enhancement, motion-blur compensation, and clever data-augmentation strategies are all fair game as long as ...
I have a fully curated dataset and need an AI engineer who can turn it into a production-ready model that detects and classifies people, vehicles, and animals. The plan is to build a custom detector using YOLO and optimise it for low-latency inference with TensorFlow RT/TensorRT so it can run reliably on edge hardware as well as GPUs in the cloud. Here is what I’m expecting: • End-to-end training pipeline: data augmentation, transfer learning on the latest YOLO variant, and fine-tuning until we hit solid precision/recall numbers. • Exported weights plus a clean inference script (Python) that loads in under a second and returns bounding boxes, class labels, and confidences. • Clear documentation of your environment and commands so I can reproduce th...
Quiero desarrollar un agente de inte...anterior en un único archivo CSV listo para importar en mis herramientas de scouting. Valoraría que propongas funciones extra —por ejemplo, visualizaciones interactivas, detección de formaciones dinámicas, mapas de calor o alertas en tiempo real— siempre que no comprometan la entrega principal. Incluye en tu propuesta: • La arquitectura y los modelos que piensas emplear (por ejemplo, OpenCV, YOLO, DeepSort, pose estimation, transformers de acción). • Un plan claro de entrenamiento o ajuste fino con datos de ejemplo. • Tiempo estimado de desarrollo y posibles fases de validación. Busco una solución robusta pero escalable. Estoy abierto a sugerencias siempre qu...
...implement a real-time stop sign detection system directly on the JetRacer. The system should process the onboard camera feed, detect a stop sign, and trigger a reliable stop action. Important: * Focus is only on stop sign detection * The solution must run on the Jetson Nano (Waveshare-Jetracer) (onboard, not external PC) Scope of work: * Develop and train a lightweight object detection model (YOLO) * Optimize the model specifically for Jetson Nano * Improve inference speed using TensorRT * Integrate the solution into a ROS-based pipeline * Ensure stable real-time behavior (low latency detection → stop) Technical environment: * Python * NVIDIA Jetson Nano (Waveshare JetRacer) * Camera-based detection * Computer Vision / Deep Learning * ROS (optional but preferred) Re...
...sequences. You may work in CVAT, Labelbox, VGG Image Annotator, or any other tool that can export COCO JSON or YOLO-format text files; direct integration with AWS SageMaker Ground Truth is welcome. Deliverables • Complete annotation files (COCO JSON or YOLO txt) for all images and extracted video frames • A brief quality-control report describing checks performed (IoU thresholds, peer review, etc.) • A sample export demonstrating correct label structure before full hand-off Acceptance criteria • Every target object is fully enclosed—no clipping, no missed instances • Labels match the agreed taxonomy exactly, one label per object • Output files pass standard COCO/YOLO validators without errors Include a note on...
...and tags it for easy retrieval. • It attempts facial recognition when the image quality is sufficient, flagging matches from a watch-list I will provide. Because the cameras operate 24/7 in very mixed environments—low-light corridors, exposed outdoor zones that face rain or glare, and busy high-traffic entry points—the model must remain accurate under those conditions. Solutions that leverage YOLO, TensorFlow, PyTorch, OpenCV or comparable frameworks are fine as long as they run on my existing Nvidia GPU server (CUDA-enabled). Deliverables 1. Trained model files plus any custom scripts. 2. A lightweight API or service (Python preferred) that ingests RTSP streams, performs detection, and triggers my existing alerting webhook. 3. Setup instructions and ...
... User Dashboard, Product Upload System, Image/Video Upload, Product Feed, Shop Management, Order Management System, Story System (24-hour content), Post Upload System, Like and Comment System, Social Feed, Chat System, Real-time Interaction Ready, Analytics Dashboard, Engagement Tracking, Advertisement System, Broadcast System, AI-powered Platform, Computer Vision Integration, Object Detection (YOLO), Smart Recommendations, Data-driven Insights, Python Backend, Advanced JavaScript, HTML5, CSS3, Bootstrap Framework, SQLAlchemy ORM, Flask-Login, API Integration, Modular Architecture, Scalable Application, Production-ready System, Clean Code Architecture, High Performance System, Secure Web Application, End-to-End Development, Custom Web Solutions...
I’m preparing to apply for technical roles in AI, robotics and Python development, and I need a single-page resume that does the heavy lifting for me. The document must: • Stay ATS-friendly—clean layout, logical headings, easy-to-parse fonts, no embedded graphics that screening software might miss. • Spotlight my hands-on work with ROS2, OpenCV and YOLO, especially the robot-vision and navigation projects that show real-world impact. • Present a concise skills matrix (Python, C++, machine learning, computer vision, ROS2 tooling) followed by a punchy project section, then education. No invented experience—everything comes from my actual portfolio. • Use a modern, minimalist design: subtle colour accents are fine, but keep it business-ready...
I’m preparing to apply for technical roles in AI, robotics and Python development, and I need a single-page resume that does the heavy lifting for me. The document must: • Stay ATS-friendly—clean layout, logical headings, easy-to-parse fonts, no embedded graphics that screening software might miss. • Spotlight my hands-on work with ROS2, OpenCV and YOLO, especially the robot-vision and navigation projects that show real-world impact. • Present a concise skills matrix (Python, C++, machine learning, computer vision, ROS2 tooling) followed by a punchy project section, then education. No invented experience—everything comes from my actual portfolio. • Use a modern, minimalist design: subtle colour accents are fine, but keep it business-ready...
I’m preparing to apply for technical roles in AI, robotics and Python development, and I need a single-page resume that does the heavy lifting for me. The document must: • Stay ATS-friendly—clean layout, logical headings, easy-to-parse fonts, no embedded graphics that screening software might miss. • Spotlight my hands-on work with ROS2, OpenCV and YOLO, especially the robot-vision and navigation projects that show real-world impact. • Present a concise skills matrix (Python, C++, machine learning, computer vision, ROS2 tooling) followed by a punchy project section, then education. No invented experience—everything comes from my actual portfolio. • Use a modern, minimalist design: subtle colour accents are fine, but keep it business-ready...
...I can highlight each item in the UI. • Smaller is better: please target a footprint that can comfortably fit into a typical smartphone package while keeping inference times snappy. • I’ll need the trained model file, the training notebook or script, and a short README that explains how to reproduce the training and run inference. If you already have experience with MobileNet, EfficientDet, YOLO-Nano, TensorFlow Lite or similar tiny-model workflows, your expertise will be valuable here. Accuracy is important, but compactness is equally critical, so let me know what trade-offs you recommend and past results you’ve achieved on similar lightweight object-detection tasks. When you reply, please outline: 1. Your preferred architecture and why it suits this...
...dataset of over 12,000 images utilized to assess the Urban Green Space Index in regions like Qassim and Madinah. The current dataset is already in YOLO format, but contains unacceptable labeling errors that must be systematically fixed. The final output must be 100% accurate, strictly formatted, and ready to plug directly into our deep learning training pipeline. Scope of Work & Technical Requirements: Review & Correct: Carefully examine 7,525 PNG images and their corresponding YOLO TXT annotation files. You will adjust, add, or delete bounding boxes to ensure every piece of vegetation is accurately captured. Format: The dataset is already in YOLO format. You must maintain this standard. Strict Naming Convention: Every image and its corresponding annota...
