Discover Computer Vision Projects Efficiently

We are a growing AI startup seeking a motivated and reliable business partner based in the United States or Canada to assist with the testing and development of our AI-driven market intelligence platform.
Our team has recently developed a computer vision system—built using Python and OpenCV—designed to monitor retail stores, shopping centers, and local markets across the U.S. and Canada to analyze market conditions.
As our platform enters the testing phase, we are looking for a local partner to help with market data preparation, AI model training, system validation, and business development.

Key Responsibilities
- Collect and organize data regarding local markets and the retail sector
- Assist with AI data annotation, labeling, and dataset preparation
- Support the testing and accuracy verification of computer vision models
- Research local retail trends and market conditions
- Provide feedback to improve the performance of AI systems
- Collaborate with the technical team throughout the testing and development process
- Identify opportunities for future business expansion

Qualifications
- Currently residing in the United States or Canada
- Strong communication skills and a systematic approach to tasks
- Basic computer literacy and experience with online research
- Meticulous, highly responsible, and capable of working independently
- An interest in AI technology, market research, or business development
- No prior experience in programming or advanced AI required

What We Offer
- A flexible, remote work environment
- Structured guidance and training from an AI development team
- Hands-on experience in the fields of AI and computer vision
- Potential for long-term collaboration and future business partnership
- Compensation based on the agreed-upon scope of work and responsibilities

We welcome applications from individuals who are passionate about AI innovation, eager to explore local markets, and interested in contributing to a growing technology company.
To confirm that you have read and understood the above requirements, please begin your application with the phrase "BUSINESS PARTNER."

Looking for an experienced mobile app developer to help build an AI-powered fashion styling app, from development to launch on the Apple App Store.

The app allows users to upload their existing clothes into a digital wardrobe, receive AI-generated outfit recommendations based on their preferences and occasions, and see a realistic AI-generated image of themselves wearing the recommended outfits.


Key Features:
- Digital Wardrobe: Users upload clothing photos, and AI automatically identifies and categorizes the items.
- AI Outfit Generator: Generate outfit combinations using clothes users already own, based on occasions and style preferences.
- AI Virtual Try-On (Main Feature): Generate realistic images showing users wearing recommended outfits using their uploaded full-body photos.
- Personalized Styling: Outfit suggestions based on body shape, measurements, and preferences.
- Discover & Shopping: Recommend new clothing items and allow users to virtually try them on.
- Save & Share: Save generated outfits and share images.


What I'm Looking For:
Ideally, one experienced developer who can handle the entire project, including:
- Reviewing and improving my existing Figma wireframes and UI/UX
- Recommending the most suitable technology stack and AI solutions
- Developing the iOS app, backend, and AI integrations
- Testing, debugging, and App Store submission
- Providing source code, documentation, and post-launch support
Experience with AI image generation, virtual try-on technology, or computer vision is essential.


Project Details
- Platform: iOS first, Android in the future
- Timeline: Ideally within 3 months for an MVP
- Budget: Open to fixed-price quotations with milestones
- Scope: Open to developer recommendations on MVP priorities


To Apply
Please share:
1. Examples of mobile apps you've developed, especially AI-powered applications.
2. Your experience with AI virtual try-on or image generation.
3. Your recommended technology stack and AI solution, and why.
4. Your estimated fixed-price quotation and timeline.
5. Whether you can handle the project independently from development to App Store launch.

Please start your proposal with "Fashion App" so I know you've read through the job description.

We are a U.S.-based visual technology startup seeking an experienced Unity and/or Unreal Engine developer to help build an early-stage advanced 3D display prototype.

This is a confidential R&D project focused on creating compelling real-time 3D experiences for emerging display technologies. The initial engagement is a defined prototype phase with the potential for additional work if the prototype is successful.

WHAT WE NEED

We are looking for a developer who can:

• Recommend Unity or Unreal Engine for the prototype and explain the technical reasoning.

• Integrate a selected advanced/spatial 3D display SDK.

• Build a modular real-time 3D application architecture that is not permanently tied to a single display manufacturer.

• Develop prototype experiences across approximately five categories, including sports, automotive, product/retail visualization, architecture/building visualization, and an interactive real-time 3D experience.

• Implement a clean prototype interface for launching and navigating experiences.

• Optimize rendering performance and document FPS, frame time, CPU/GPU utilization, and other relevant performance metrics.

• Maintain clean, organized source code in a private project repository controlled by the client.

• Provide complete setup, build, dependency, licensing, and handoff documentation.

IDEAL EXPERIENCE

Strong candidates should have professional experience with several of the following:

• Unity and/or Unreal Engine
• C# and/or C++
• Real-time 3D rendering
• XR, stereoscopic, multiview, light-field, or spatial display development
• OpenXR or related XR technologies
• GPU/shader programming and performance optimization
• Hardware manufacturer SDK integration
• Interactive 3D application development

Experience with computer vision, depth processing, custom rendering pipelines, or nontraditional display hardware is a strong plus.

PROJECT STRUCTURE

• Fixed-price initial prototype project
• Budget: approximately $2,500
• Milestone-based payments
• Estimated duration: 1–3 months
• Remote / worldwide
• Potential for additional development phases based on performance and prototype results

CONFIDENTIALITY & IP

This is an early-stage R&D initiative. Detailed product information, technical architecture, roadmap, branding, and other proprietary materials will only be shared with shortlisted candidates when appropriate.

The selected developer must be comfortable working under confidentiality and intellectual-property terms and using a client-controlled private repository.

Please do not apply if you require permission to publicly display confidential project work in your portfolio, social media, demo reel, or other public channels.

WHEN APPLYING

Please briefly address the following:

1. Describe your strongest project involving non-standard real-time 3D, XR, stereoscopic, multiview, light-field, or advanced rendering. What did you personally build?

2. Have you integrated a specialized hardware manufacturer's SDK into Unity or Unreal? Please describe the hardware and your role.

3. Which engine would you initially recommend for this type of prototype—Unity or Unreal—and why?

4. How would you architect the application so the core system is not permanently tied to one display manufacturer's SDK?

5. What would you aim to accomplish during your first 10 paid development hours?

Please include links or references to relevant technical work when possible. We are more interested in demonstrated real-time 3D engineering ability than general 3D artwork.

Hello!

I'd like to invite you to take a look at the job I've posted. Please submit a proposal if you're available and interested. My character has a lot of tattoos and i am having trouble making her tattoos stick in cloned videos, nsfw content, etc. Let me know if you are able to train her for me. I have pictures of multiple angles of my model

Iva N.

I want to put intelligence behind my existing IP cameras so they can spot unusual activity on their own and trigger the right security responses without me lifting a finger. The core of the job is to build an AI-driven controller that watches the live feed, detects predefined events (motion in restricted hours, unknown faces, package removal, etc.), and then automatically issues the appropriate security alert—push notification, email, or siren—through a simple dashboard or mobile push gateway.

Scope of work
• Connect to standard ONVIF or RTSP cameras on my local network.
• Run real-time video analytics (OpenCV, TensorFlow, or another proven framework is fine) to classify events I can customise later.
• Expose a lightweight web or REST interface so I can arm/disarm zones and review alert logs.
• Push alerts instantly to my phone and optionally trigger a relay (e.g., a smart siren).
• Package everything so it installs on a small box in my office (Raspberry Pi 4 or Ubuntu server—your call, just outline the resource requirements).

Acceptance criteria
1. At least three event types reliably detected (e.g., motion, person, package).
2. <1 second latency from detection to alert on a local network.
3. Clear setup guide that lets me add or remove cameras and tweak sensitivity without touching code.
4. Source code delivered in a Git repo with comments and a brief README.

If you have prior work with Home Assistant, MQTT, or similar automation stacks, please mention it—plug-and-play is a big plus for me.

Skills: PHP, Mobile App Development, Android, Software Architecture, OpenCV, Tensorflow, Computer Vision, AI Automation

We are an independent technology studio seeking an experienced Machine Learning / Computer Vision Engineer to develop a localized Python-based proof-of-concept (POC) script. The objective is to analyze high-resolution 2D photographs of a flexible, non-rigid texturized surface, map a dynamic unit density baseline, and execute dual-method logical matching algorithms based on structural metrics.

The selected developer will be granted secure access to abstract geometric calibration data and texturized surface profiles.

Core Technical Requirements
• Pipeline: Develop a standalone Python script utilizing OpenCV or a lightweight custom YOLO framework executing entirely on-device.
• Frame Normalization: Automatically map, rectify, and flatten perspective tilts and variable ambient lighting from a smartphone camera capture into a uniform 2D coordinate matrix against a physical template.
• Feature Extraction: Project a dynamic, elastic vector grid-mesh over the textured surface to perform automated density verification and precise unit counts per inch.
• Data-Matching Logic: Develop a backend mathematical processor that cross-references the real-world camera count against target data values to output specific configuration and adjustment metrics to the console log.
• Architecture: Code must be fully modular, cleanly documented, and optimized for future packaging inside a mobile SDK/API deployment framework without cloud dependencies.

Milestone & Interview Timeline
We are operating on an accelerated commercial runway and expect strict adherence to the following schedule:
• October 29-November 1 (Wed-Sun): Proposal Review & Shortlisting
• November 2-November 3 (Mon-Tues): 15-Minute Zoom Technical Discovery Calls
• November 4-November 5 (Wed-Thu): Contract Award & Onboarding
• November 13 (Fri): Delivery of Core Algorithmic Logic Parameters

Application Questions (please answer to be considered):
1. What is your experience with projecting dynamic, elastic vector grid-meshes over flexible, texturized surfaces using OpenCV or lightweight YOLO models?
2. How would you handle image rectification to flatten skewed perspective angles and normalize variable lighting conditions into a uniform 2D matrix?
3. Are you available to deliver a clean, modular Python script executing this image-to-console logic pipeline by the November 13 deadline?

Project Overview

We are looking for an experienced Flutter developer to build a cross-platform mobile application (iOS and Android) that will be used to capture product images from multiple angles and upload them to a cloud-based server for processing by our computer vision models.

The application will ultimately support AI-powered grading and classification workflows, so image quality, consistency, and ease of use are critical.

Scope of Work

The initial version of the application should include:

Image Capture Workflow

User logs into the application
User creates or selects an item to be photographed
App guides user through taking multiple photos of the same item from predefined angles
Ability to require completion of all required angles before submission
Display visual prompts showing which angle should be captured next
Allow user to review and retake photos before submission

Image Upload

Upload images to our server through a REST API
Associate all images with a unique item ID
Secure authentication and API communication
Display upload status and confirmation

Additional Features

Offline image storage if connectivity is unavailable
Automatic upload when connectivity returns
Camera controls for focus and image quality
Basic user management and authentication
Simple dashboard showing previously submitted items

Technical Requirements

Flutter (preferred)
Single codebase for Android and iOS
Experience integrating camera functionality
Experience with REST APIs
Experience with image upload and storage workflows
Familiarity with AWS, Azure, or similar cloud environments
Experience deploying apps to Apple App Store and Google Play is a plus

I need a image classification pipeline to detect placement caps on smd connectors on an PBCA.

I will send you the images (balanced dataset) to train and validate the model.

It is more important to detect all placement caps, because some following process steps will crash if there are some. No placement cap will be the value true, so optimize for precision.

The dataset contains 671 images with placement caps and 671 images without placement caps.

We are looking for an experienced AI / Computer Vision Engineer to develop models for anatomical landmark detection using 2D images and 3D scans, primarily for digital dentistry applications.

The engineer will collaborate with our clinical and software development teams to build accurate, reliable, and practical AI solutions.

Requirements
- Strong Python skills and hands-on experience with PyTorch or TensorFlow.
- Proven experience training computer vision models for landmark detection, segmentation, or keypoint detection.
- Experience processing 2D images and/or 3D meshes and point clouds.
- Ability to prepare datasets, establish annotation guidelines, and evaluate model accuracy.
- Familiarity with relevant tools such as OpenCV, Open3D, PointNet, or similar frameworks.
- Experience delivering working AI models and inference pipelines.

### Responsibilities
- Define annotation requirements with our clinical team.
- Prepare and validate datasets.
- Train, test, and improve AI models.
- Evaluate prediction accuracy and identify failure cases.
- Develop AI-assisted annotation workflows.
- Deliver trained models, documented code, and structured landmark coordinates for software integration.

Preferred Qualifications
- Experience in dental AI, medical imaging, or anatomical landmark detection.
- Dental knowledge or previous collaboration with dental professionals.
- Experience with 3D computer vision and CAD software integration.

How to Apply
Please share relevant projects, models you personally trained, GitHub repositories or demonstrations, and your availability.

Dental experience is a plus, not a requirement.
We value strong technical expertise and practical model-development experience.

Work Location: This is an on-site position at our office in Giza, Egypt.

We are looking for an experienced computer-vision engineer to develop a proof-of-concept system for virtual advertising in recorded football broadcast footage.

The initial prototype should replace advertising shown on physical LED perimeter boards with new supplied creative, while keeping the replacement correctly positioned as the broadcast camera moves.

This is an early-stage technical feasibility project. The first version will work with recorded footage rather than a live broadcast feed but we eventually want to get to that.

Initial prototype requirements

Using a short sample of football broadcast footage, the system should:

Identify and track the visible LED perimeter boards.
Replace the existing board content with supplied video or animated graphics.
Maintain correct scale, perspective and positioning during camera movement.
Mask players, officials and other foreground objects when they pass in front of the advertising.
Produce a clean exported 1080p video.
Use automated or semi-automated computer vision rather than manual frame-by-frame editing.
Be capable of being adapted to additional footage and stadium configurations.

The successful freelancer must provide the complete source code, documentation and setup instructions.

Required experience

Applicants should have strong practical experience in several of the following areas:

OpenCV
Python and/or C++
PyTorch, TensorFlow or similar frameworks
Camera calibration
Homography and perspective transformation
Semantic or instance segmentation
Object detection and tracking
Video compositing
FFmpeg or GStreamer
CUDA or GPU optimisation
Real-time or near-real-time video processing

Experience with broadcast graphics, augmented reality, virtual production, sports analytics, Unreal Engine, NDI or SDI workflows would be useful but is not essential.

Initial deliverable

The first paid milestone will involve approximately 30–60 seconds of supplied football footage.

The expected deliverable is:

Replacement of the visible LED advertising.
Stable tracking during camera pan, tilt and zoom.
Foreground masking when players cross the advertising area.
An exported demonstration video.
Complete source code.
Instructions explaining how to run the system with replacement creative.

This is not a video-editing or manual rotoscoping project. We are looking for a technically reusable computer-vision solution.

Potential future work

If the prototype is successful, further work may include:

Longer football broadcasts.
Automated stadium and board calibration.
Virtual 3D advertising carpets.
Support for different stadiums and camera positions.
An operator interface.
GPU optimisation.
Near-live or live video processing.
Multiple regional advertising outputs.

There is potential for a longer-term technical relationship for the right person or team.

Application questions

Please answer the following questions in your proposal:

Describe a relevant project where you anchored graphics or virtual objects to footage from a moving camera.
How would you track the geometry of LED perimeter boards during camera movement?
How would you mask players passing in front of the replacement advertising?
Would you use classical computer vision, machine learning or a combination of both?
How would you minimise manual calibration or rotoscoping?
What processing speed would you expect to achieve at 1080p?
What additional development would be required to make the system operate live?
Which parts of your previous work can you demonstrate on a video call?
Are you personally completing the work, or would any part be subcontracted?

Generic AI proposals will not be considered. Please explain your proposed technical approach and include examples of directly relevant work.

Commercial terms

The project will be structured through fixed-price milestones.

The client must receive:

Ownership of all bespoke source code following payment.
Access to the code throughout development through a client-controlled repository.
A list of all third-party libraries, models and licences.
Full disclosure of any existing proprietary components.
Documentation sufficient for another engineer to run and review the system.

The supplied footage and commercial information must remain confidential and may not be used in portfolios or demonstrations without written permission.

Please include:

Your proposed budget for the initial prototype and full development.
Estimated delivery schedule.
Recommended technology stack.
Any assumptions or technical limitations.

Project Domain / Category
Artificial Intelligence / Computer Vision / Medical Image Processing / Deep Learning
Abstract / Introduction
Brain tumor detection from Magnetic Resonance Imaging (MRI) is an important medical image
analysis task. Deep learning techniques can assist in identifying abnormal patterns in brain MRI
images. Capsule Networks (CapsNet) offer an alternative to conventional Convolutional Neural
Networks (CNNs) by learning spatial relationships among image features. Integrating a trained
deep learning model into a web-based application can make MRI image analysis more
accessible and convenient for demonstration and educational purposes.
This project aims to develop a web-based brain tumor detection system using a Capsule
Network (CapsNet). The publicly available Br35H Brain MRI dataset, containing Tumor and NoTumor images, will be used for model training and testing. The developed web application will
allow users to upload MRI images, perform basic image-quality assessment, and obtain Tumor
or No Tumor predictions using the trained CapsNet model.
Functional Requirements: In this project, we will leverage image processing and deep learning
techniques to address the following objectives:
1. Data Collection and Preprocessing: Gather a diverse dataset of brain MRI scans,
ensuring data quality and integrity. Preprocess the images to enhance their suitability
for further analysis. Apply image normalization, resizing, and suitable data
augmentation.
2. Dataset Splitting: To facilitate model training and evaluation, we will split the dataset
into distinct sets for training, validation, and testing. This step ensures that the deep
learning model's performance is rigorously assessed and prevents overfitting.
3. Deep Learning Model Development: Implement and train a conventional Convolutional
Neural Network (CNN) as a baseline model and a Capsule Network (CapsNet) as the
primary model for classifying brain MRI images into Tumor and No Tumor categories.
Conduct a comprehensive comparative performance analysis of both models using
Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Confusion Matrix. The comparison
shall evaluate their classification effectiveness, ability to correctly identify tumor cases,
and computational efficiency to determine the relative strengths and limitations of
CapsNet compared with the conventional CNN.
4. Model Evaluation: We will establish robust evaluation metrics to assess the
performance of the deep learning models, ensuring that the segmentation results are
precise and reliable.
Evaluate the model using Accuracy, Precision, Recall, F1-Score, ROC-AUC, and
Confusion Matrix.
Compare CapsNet with a basic CNN model to determine whether Capsule Networks
provide any performance advantage. Analyze the performance drop of each model
under different image-quality degradations.
5. DataSet: For this project you need to use BR35H Brain MRI dataset.
6. Model Deployment: Students shall develop a user-friendly web interface that allows
users to upload brain MRI images. The uploaded MRI image shall be processed using the trained CapsNet model. The system shall classify the image as Tumor or No Tumor and
display the predicted class and model score

Skills: Python, Image Processing, OpenCV, Flask, Tensorflow, Keras, Pytorch, Computer Vision, Deep Learning, Web Application

I'm looking to develop LPR and/or Video Analytics, preferably in C++. I'm looking for someone with hands-on experience who has already developed these kinds of apps. I'm looking for actual application functionality, not just object detection models.
Please send me a demo if you have any.
Please start your msg with the words "I have experience with LPR / Video Analytics!"

Project Overview

I’m developing a beauty technology startup focused on making professional-looking makeup accessible and affordable at home.

The concept is an app that allows users to upload a photo of themselves, preview different makeup looks virtually, and eventually receive a personalised makeup kit with the products and a tutorial needed to recreate their chosen look.

I’m looking for an experienced developer to build an initial working prototype of a custom virtual makeup try-on tool featuring lipstick, blush and eyeshadow.

This is the first stage of a larger project. I want to establish a reliable, realistic makeup-rendering foundation that can be expanded into a full AI-powered beauty application.

I’m open to adapting suitable existing technology or open-source projects, provided the solution is suitable for commercial use and can be customised to meet my requirements.

Scope of Work

1. Photo Upload

* Allow users to upload a portrait photo in JPG, PNG or WEBP format.
* Automatically detect the face and relevant facial features.
* Display the original photo and the edited result.
* Include basic error handling for unsuitable images.

2. Virtual Lipstick Try-On

* Apply different lipstick shades, including nude pink, rose, classic red, berry and brown nude.
* Include an adjustable intensity slider.
* Preserve natural lip texture, highlights, shadows and lip shape.
* Accurately identify the outer and inner lip boundaries.
* Avoid colouring teeth or the inside of the mouth.

3. Virtual Blush Try-On

* Apply blush naturally to the cheeks.
* Offer several colours, such as soft pink, peach and rose.
* Allow users to adjust the intensity and placement.
* Blend the colour naturally into the original skin while preserving skin texture and facial features.

4. Virtual Eyeshadow Try-On

* Offer a selection of eyeshadow colours and preset styles, such as natural brown, soft pink and evening glam.
* Accurately detect the eyelids and apply colour within the appropriate areas.
* Allow users to adjust the intensity.
* Preserve the natural appearance of the eyes and surrounding skin.

5. Complete Makeup Looks

Users should be able to combine lipstick, blush and eyeshadow to create a complete makeup look.

The prototype should include a few preset combinations, allow users to switch between them, and provide a before-and-after comparison.

6. User Interface

* Clean, modern and beauty-brand-appropriate design.
* Responsive layout for desktop and mobile browsers.
* Simple controls for selecting colours, styles and intensity.
* Ability to reset the image and download the finished result.

Technical Requirements

I’m open to your recommendations on the best technical approach. Potential technologies include:

* React and TypeScript.
* MediaPipe or another suitable facial landmark detection solution.
* Canvas, WebGL or other appropriate image-rendering techniques.
* Image segmentation or additional computer vision techniques where necessary.

You may propose adapting an existing open-source project or SDK if it offers a suitable foundation.

However, please disclose any third-party dependencies, licensing restrictions, commercial-use permissions, ongoing costs and technical limitations before implementation.

The main priority is realistic makeup rendering, not simply applying flat blocks of colour to the face.

The finished result should blend naturally with the user’s features and preserve the original person’s identity and appearance as much as possible.

Privacy and Data Handling

Where practical, uploaded photos should be processed locally in the browser. If external processing or third-party APIs are required, please explain what data is transmitted, where it is processed, and any associated costs.

Deliverables

1. A working web-based prototype featuring lipstick, blush and eyeshadow.
2. Several preset combinations demonstrating complete makeup looks.
3. Complete source code and project files.
4. Instructions for running, testing and maintaining the application.
5. A demonstration using a variety of suitable portrait photos.
6. Documentation of third-party libraries, licences, ongoing fees and limitations.
7. Confirmation of ownership and usage rights for custom code and project-specific deliverables, subject to any disclosed third-party licence terms.

Future Development

If the prototype performs well, I intend to explore additional features, including:

* AI-generated personalised makeup looks.
* Recommendations based on occasion, time of day, outfit, preferred style and skin concerns.
* More makeup categories and realistic product-specific shades.
* Live camera try-on.
* Integration with a product recommendation and makeup-kit ordering system.
* A future mobile application for iOS and Android.

These features are not required for the initial prototype, but the solution should be reasonably extensible.

Ideal Candidate

I’m looking for someone with demonstrable experience in one or more of the following:

* Computer vision and facial landmark detection.
* Image processing and realistic cosmetic rendering.
* Canvas or WebGL-based image manipulation.
* Virtual makeup try-on or beauty technology.
* Building functional prototypes with clean, maintainable code.

Please include examples of relevant projects you have personally worked on.

Budget and Proposal

My initial budget is for the prototype, depending on the proposed approach, existing technology that can be reused, and the quality of the results.

I understand that a highly polished commercial product may require a larger budget. I am therefore open to a staged approach, beginning with a clearly defined milestone and expanding the scope once the initial results have been evaluated.

In your proposal, please explain:

1. How you would approach building this prototype.
2. Whether you would use existing technology or develop the rendering functionality from scratch.
3. How you would achieve realistic blending for lips, cheeks and eyelids.
4. Examples of similar projects you have completed.
5. Your estimated timeline and fixed-price quote.
6. What is included in your price and what would incur additional costs.
7. Any limitations or compromises you anticipate within the proposed budget.

I value technical quality, realistic results and a maintainable foundation over simply delivering a large number of features.

I would prefer to start with a small milestone to validate the rendering quality before proceeding with the full prototype.

I need a custom virtual camera that can join a Zoom call and replace my face in real time with a chosen subject’s face. Before each meeting I want to load one or more reference photos (or short video clips) of that person; once the call starts, the software should track my head and mouth movements and render a realistic, latency-free face swap that other participants see as my camera feed.

Zoom is the first platform I must support, but I eventually want to point the same virtual camera at Google Meet and Microsoft Teams, so please keep cross-platform thinking in mind. Whatever stack you prefer—OpenCV, MediaPipe, TensorFlow, Unreal’s LiveLink, or your own pipeline—you’ll need to handle live video I/O, facial-landmark tracking, texture blending, and create a signed driver or plugin so the operating system recognises the feed as a standard webcam.

Deliverables
• Desktop application (Windows or macOS) that appears in Zoom as a selectable camera
• Interface to import photos or videos that train/drive the swap model on the fly
• Real-time, high-resolution face replacement that matches lighting and expressions
• Installation package and brief usage guide for non-technical users

When you reply, just show examples of past work that prove you have already built—or heavily contributed to—similar real-time video or AR projects. A short demo clip or repo link is perfect.

Skills: C Programming, C# Programming, C++ Programming, OpenGL, Augmented Reality, Artificial Intelligence, OpenCV, Tensorflow, Computer Vision, Facial Recognition

I need an end-to-end computer-vision pipeline that spots every Choy Sum plant in my greenhouse, decides whether it is Immature, Mature, or Overmature, pinpoints its X-Y-Z position, and flags only the harvest-ready plants for a robotic arm.

Camera & environment
• A stereo camera will be mounted above the crop beds; depth or single-lens alternatives are not planned at this stage.
• All images will come from my indoor farm, but none are yet captured or annotated, so the project starts with data acquisition and labelling.

Core objectives
• Train a YOLO-based detector in PyTorch that recognises individual plants and assigns the three growth-stage classes with high precision.
• Derive 3-D coordinates from the stereo pair, fuse them with the class prediction, and output a clean list of mature-plant positions.
• Package the results in a straightforward API or message format that my harvesting robot can consume.

What I expect to receive
- A documented data-collection protocol plus labelled dataset.
- All training scripts, inference code, and trained weights.
- A short technical report covering model architecture, evaluation metrics, and integration instructions.
- A live demo (video or remote session) that shows the system detecting, classifying, and localising plants in real greenhouse footage.

Please reply with your proposed timeline, a brief outline of your approach to stereo depth and YOLO fine-tuning, and any previous work you have done on agricultural vision systems or 3-D localisation.

Skills: C Programming, Python, Software Architecture, C++ Programming, Artificial Intelligence, Pytorch, Computer Vision, YOLO

Need Help on telephonic guidance on WhatsApp call to Navigate an unfamiliar Python repository like (Mako, Requests, or Colander), reproduce bugs, identify root causes, implement fixes, run tests, and explain online guide debugging.

Hello, we have developed a pipeline to create scaled human 3D models from an iOS device. We have our own 3DGS, meshing and ARKit scaling solution, however we need help refining the process to match 3rd party system performance. Are you the right person to help? We would love to chat and explore the project. Regards, James

We are looking for a detail-oriented Data Annotation Specialist with experience in image annotation, object detection, and bounding box labeling to support an AI training data project involving drone and maritime imagery.

The primary responsibility is to review and refine AI-generated annotations, ensuring that objects are accurately identified, labeled, and positioned. The project includes both daylight and thermal imagery, with the potential to expand into video annotation.

Key Responsibilities:

Review, correct, and refine machine-generated bounding boxes.

Identify and label missed objects, including vehicles, boats, people, and other relevant objects.

Annotate daylight and thermal sensor imagery with high precision.

Ensure consistent object classification and accurate bounding box placement.

Flag unclear images, ambiguous objects, and annotation guideline inconsistencies.

Maintain consistent productivity while meeting quality standards.

Support video-frame annotation and object tracking if the project expands.

Requirements:

Previous experience in Data Annotation, Image Labeling, or Computer Vision projects.

Strong understanding of bounding box annotation and object detection.

Experience using annotation tools such as CVAT, Labelbox, Label Studio, Roboflow, or similar platforms.

Excellent attention to detail and ability to follow annotation guidelines.

Ability to work independently and communicate clearly.

Experience with drone, aerial, maritime, or thermal imagery is a strong advantage.

Ideal Candidate:

We are seeking someone who values accuracy, consistency, and reliable delivery. You should be comfortable reviewing AI-generated labels, identifying errors, and maintaining high-quality annotations across different image types.

How to Apply:

Please include the following in your proposal:

A brief overview of your image annotation experience.

The annotation tools you have worked with.

Any experience with drone, aerial, maritime, or thermal imagery.

Your availability and expected hourly rate.

We look forward to working with a skilled annotation specialist who can help us build accurate, high-quality AI training datasets.

I’m developing an AI-powered loss-prevention system for delis and convenience stores.

I own a deli in Brooklyn, NY, and want to build an MVP using our existing security cameras. The goal is to use computer vision to identify potentially suspicious activities involving cashiers and customers, such as unusual register activity, potential employee theft, and shoplifting.

The system should ideally:

* Connect to existing IP cameras and DVR/NVR systems using RTSP or ONVIF where supported.
* Analyze video footage using AI.
* Detect and flag specific suspicious events.
* Save short video clips with timestamps for review.
* Provide a simple dashboard and alerts for business owners.
* Eventually support multiple cameras and multiple business locations.

I’m looking for an experienced computer vision engineer with a background in CCTV, real-time video analytics, and AI surveillance systems.

I want to start with a small technical feasibility project using my own store’s cameras before investing in a full commercial platform.

I’m interested in your recommended technical approach, estimated timeline, and budget for an initial MVP.

The goal is to build a reliable, scalable product that can eventually be offered to other small businesses through a monthly subscription.

I’m looking for an experienced AI computer vision developer to help build an MVP for an AI-powered loss-prevention service for independent grocery stores and super markets.

The concept:

* Analyze footage from existing CCTV cameras and DVR/NVR systems.
* Detect potentially suspicious cashier behavior, such as concealing cash, suspicious handoffs, or unusual product handling.
* Send timestamped alerts and video clips to the store owner.
* Work without requiring a POS system.
* Avoid replacing existing cameras wherever technically possible.

Required experience:

* Computer vision and video analytics.
* Python, OpenCV, and relevant AI video models.
* RTSP, IP cameras, and DVR/NVR integrations.
* Real-time video processing and alert systems.
* Deploying AI locally on an edge device or in the cloud.

First milestone:
I want a technical feasibility assessment and a small proof of concept using sample deli footage before committing to a full product.

Please share examples of similar systems you’ve built, your proposed technical approach, estimated cost, and timeline.

We're looking for an experienced, hands-on Machine Learning Engineer to join our team on a part-time (1-2 days/week), freelance basis.

You'll mostly work on existing ML applications, including computer vision algorithms that power our at-home hormone tests, predictive models for menstrual cycles and symptoms, and LLM-powered features that personalize the user experience.

This is a highly practical role for someone who enjoys solving complex problems, writing production-ready code, and taking ownership of projects from development through deployment.

You'll work closely with our product and engineering teams, with the flexibility to manage your own time and workload.

Ideally you have experience with medical imaging, diagnostic technologies, or quantitative image analysis. A background in healthcare or digital health is a bonus but not required. Experience with health-related time-series data and personalized recommendation systems is preferred.

We are a European custom LED neon sign company. Every day we receive about 300 customer logos (PNG/JPG/PDF). Today a design team traces each logo by hand in CorelDRAW to make a vector file for production and pricing. This is slow and the quality varies a lot.

We want to automate this as far as possible.

INPUT
A customer logo (raster image, often busy: small text, icons, gradients, photos).

OUTPUT WE NEED
1. Neon layer: clean vector paths that follow the path of the LED neon tube. Two styles are used, depending on the element:
- Single line (centerline): one tube through the middle of a stroke. Used for thin or script letters and fine lines.
- Double line (outline): the tube follows the outer contour of a bold letter or shape, so you get two parallel lines.
The system should choose the right style per element (and allow us to force one style). Smooth curves, no tiny segments, sensible minimum distance between lines.
2. Cut line: a smooth contour around the whole design for the acrylic backboard (offset outline).
3. Optional UV print layer: parts that cannot be made in neon (small text, detailed illustrations) placed on a separate layer as an image.
4. Export as PDF and/or SVG (CorelDRAW compatible), with fixed layer names, so our existing script can read size and price.

IMPORTANT
- The result must stay faithful to the original logo. Never change letters or invent shapes.
- If the system is not confident, it must say so, so that a human designer takes over.
- It's fine to combine tools (e.g. AI segmentation, vectorization, centerline tracing, OCR + font matching, LLM / vision models). Please explain your approach.

PHASE 1 (paid trial, fixed price)
We send 20 example logos with our current hand-made files. You deliver your automatic output for these 20, plus a short report: how many are usable without changes, with small fixes, or not usable.

PHASE 2 (if phase 1 works)
Turn it into an API or script we can run on our server, with documentation.

IN YOUR PROPOSAL, PLEASE INCLUDE
- Similar work you did (vectorization, centerline tracing, CAD/CNC/laser/sign making, computer vision).
- Which approach and tools you would use, and why.
- Estimated time and price for phase 1.

NDA required before we share customer logos.

We are seeking an experienced VR developer or security specialist to research, design, and develop a solution that ensures the correct student is taking an examination in a Virtual Reality (VR) environment.

Consider a scenario in which a teacher assigns an examination to a student using a VR headset. The system must establish that the person wearing the headset and completing the examination is the registered student, rather than another individual taking the test on their behalf.

We are looking for an innovative, technically sound approach to identity verification and examination integrity in VR.

This is a research and development project. We are open to different approaches and would like the successful applicant to assess the available technologies, identify their limitations, and recommend the most reliable and practical solution.

Project Objectives

The primary objective is to establish a reliable link between a student’s verified identity and their examination session.

Potential capabilities include:

* Identity verification before an examination: Verify the student’s identity before granting access to the test.
* Continuous or periodic identity checks: Investigate whether the system can detect when the original student leaves and another person takes over.
* Biometric authentication: Assess the feasibility of technologies such as iris recognition, facial recognition, or other biometric methods compatible with the selected VR hardware.
* Hardware integration: Investigate whether additional cameras, sensors, fingerprint readers, eye-tracking hardware, or external peripherals could support identity verification.
* Session integrity: Detect or flag suspicious interruptions, headset removal, changes in the user, or other potential attempts to bypass the verification process.
* Exam security: Explore ways to prevent unauthorised access, account sharing, impersonation, and other forms of examination fraud.
* Instructor interface: Provide teachers or administrators with verification results, session logs, and relevant alerts.
* Privacy and security: Protect biometric information and personal data, with appropriate consent, access controls, and secure storage.

Important Technical Consideration

We recognise that VR headsets have different hardware capabilities and that not every device supports iris scanning, facial recognition, or other biometric methods.

The successful applicant will be expected to evaluate what is possible with existing VR hardware and whether additional sensors or hardware modifications would be necessary.

We are particularly interested in understanding whether a reliable solution can be implemented on commercially available VR headsets, such as the Meta Quest series, or whether a custom hardware configuration would be required.

We do not expect applicants to assume that any single biometric technique will solve the problem. We want a developer who can compare alternatives and recommend a practical, secure, and scalable approach.

Scope of Work

Phase 1 — Technical Research and Feasibility Study

* Evaluate available VR headsets, built-in sensors, and external peripherals.
* Compare potential identity verification methods.
* Assess the feasibility of detecting user substitution during an examination.
* Identify technical limitations, privacy considerations, and potential vulnerabilities.
* Recommend the most suitable architecture and implementation strategy.

Phase 2 — Proof of Concept

* Develop a prototype demonstrating the proposed identity verification method.
* Integrate the prototype with a VR examination environment or a representative test application.
* Demonstrate identity verification at the start of a session and, where technically feasible, detection of a change in the person taking the examination.
* Document the prototype’s accuracy, limitations, false acceptance and rejection risks, and resistance to straightforward bypass attempts.

Phase 3 — Potential Full Development

Subject to the feasibility study and prototype results, the project may be expanded to include a production-ready verification system, administrator dashboard, reporting, and integration with a wider examination platform.

Ideal Candidate

We welcome applications from developers and teams with experience in:

* VR development using Unity, Unreal Engine, or comparable technologies.
* Meta Quest development and VR hardware integration.
* Biometric authentication and identity verification.
* Computer vision, eye tracking, and facial recognition.
* Cybersecurity, authentication protocols, and anti-spoofing techniques.
* Secure software architecture and privacy-conscious data handling.
* Prototyping novel hardware/software solutions.

Experience with online examination security, remote proctoring, or identity verification systems would be particularly valuable.

Deliverables

1. A technical feasibility report comparing potential approaches.
2. A recommended solution architecture and hardware specification.
3. A working proof of concept demonstrating the proposed verification process.
4. A demonstration of how the system handles identity mismatches and suspected user substitution, where feasible.
5. Testing results, known limitations, and security considerations.
6. An estimated budget and timeline for full-scale implementation.

To Apply

Please answer the following questions in your proposal:

1. Have you developed VR applications involving identity verification, biometrics, computer vision, or security? Please provide examples.
2. Which VR headsets and hardware platforms would you recommend for this project, and why?
3. Which technologies would you investigate to verify a student’s identity and detect a change of user during an examination?
4. What limitations or vulnerabilities do you anticipate, and how would you address them?
5. Would you recommend beginning with a feasibility study and proof of concept before committing to full development?
6. What would be your estimated cost and timeline for the initial research and prototype?

We are looking for a technically capable partner who can evaluate an open-ended problem, propose innovative solutions, and demonstrate feasibility through a working prototype.

Please focus your proposal on the technical approach, relevant experience, and how you would establish that the person completing the examination is the registered student.

We are looking for an experienced Computer Vision & AI Software Engineer to develop, fine-tune, and deploy a custom real-time object detection system using YOLOv8.
The goal of this project is to build an end-to-end solution—from fine-tuning a YOLOv8 model on custom datasets to deploying a lightweight backend API and Web interface for live inference (image/video/webcam streams).
Key Responsibilities
• Data Preparation & Fine-Tuning: Preprocess, augment, and split custom image/video datasets to fine-tune a YOLOv8 model (e.g., target classes such as PPE compliance: Hardhat, No-Hardhat, Safety Vest, No-Safety Vest, Mask, Person, etc.).
• Model Training & Evaluation: Train and evaluate the model using PyTorch and Ultralytics YOLOv8. Monitor loss curves and report metrics including [email protected], [email protected]:0.95, Precision, and Recall.
• Backend API Development: Build a clean FastAPI backend endpoint (/predict) to handle image upload / video frame stream requests and return inference results with bounding boxes (e.g., encoded as Base64 or JSON bounding box formats).
• Frontend Web & Live Stream Integration: Create a simple HTML/JavaScript frontend supporting image uploads, local video file processing, and live webcam stream detection.
• Model Optimization & Export: Export the trained model to production-ready formats (e.g., ONNX, TensorRT, or .pt) and document deployment steps.
Required Skills & Qualifications
• Machine Learning / CV: 2+ years experience in Object Detection, Deep Learning, and Computer Vision (OpenCV, PyTorch, Ultralytics YOLOv8).
• Backend Development: Strong proficiency in Python and FastAPI.
• Frontend Basics: Basic understanding of JavaScript/HTML for handling web assets and canvas bounding box rendering.
• Model Evaluation: Ability to deliver detailed evaluation reports (Confusion Matrix, PR curves, F1 curves).
Deliverables
1. Trained Weights & Model Checkpoints: Optimized .pt and ONNX weights hosted or uploaded to Hugging Face / GitHub releases.
2. Training Artifacts & Notebook: Annotated Jupyter notebook (YOLO_Training.ipynb) showing dataset prep, loss curves, confusion matrix, and evaluation metrics.
3. End-to-End Application Codebase:
o Backend (backend/app.py): FastAPI inference server.
o Frontend (index.html, webcam.html): Web interface for upload and real-time webcam monitoring.
o Local Script (local/webcam_detection.py): Python script for local live webcam stream testing.

Hello,

I am interested in supporting your Senior Design Project on the Automated Intraoral Full-Mouth Imaging and Tooth Surface Scanning System.

I understand that this project requires a combination of mechanical design, electronics, embedded systems, motor control, and computer vision. I can help you approach Phase 1 systematically by developing practical engineering concepts and supporting the technical design process.

My proposed support includes:

* Engineering Research: Evaluating existing dental scanning technologies and identifying suitable design approaches.
* Conceptual Design: Developing and comparing alternative mechanical and system configurations.
* Mechanical Design: Supporting CAD modeling, component layout, and motion mechanism planning.
* Electronics and Automation: Assisting with system architecture, motor selection, control concepts, and embedded system integration.
* Technical Calculations: Helping evaluate motion requirements, component specifications, and design feasibility.
* Documentation: Preparing clear technical reports, design specifications, and documentation to support your project presentation.

My priority would be to help your team establish a realistic, well-documented design for Phase 1, while keeping future prototype development in mind. I would also be interested in discussing continued collaboration during Phase 2, including assembly, integration, programming, and testing.

I believe a structured and practical engineering approach can help your team develop a reliable foundation for this project.

I would welcome the opportunity to discuss your current progress, project requirements, expected deliverables, and timeline.

Best regards,
Engineering Freelancer

Skills: Engineering, Electronics, Mechanical Engineering, Electrical Engineering, Product Design, Embedded Systems, Computer Vision, Mechanical Design

Looking for an Experienced Engineering Freelancer – Senior Design Project

Electrical and Computer Engineering students working on our senior design project: Automated Intraoral Full-Mouth Imaging and Tooth Surface Scanning System.

The project involves developing an automated dental scanning system using mechanical motion, miniature cameras, embedded systems, motor control, and computer vision/AI.
looking for an experienced freelancer with expertise in Mechatronics, Mechanical Design (CAD), Electronics, Embedded Systems, Automation, and Computer Vision.

Our project consists of two phases:

Phase 1 – Current Semester: Engineering research, conceptual design alternatives, technical calculations, component selection, CAD modeling, system architecture, and technical documentation.

Phase 2 – Next Semester: Physical prototype development, mechanical assembly, electronics integration, programming, and testing.
immediate priority is completing Phase 1, with the possibility of continuing the collaboration through prototype development.

seeking someone capable of providing practical engineering guidance, technical design support, and implementation assistance.

Please contact me with your relevant experience, previous projects, and availability.

Skills: Engineering, Research, CAD/CAM, Electrical Engineering, PCB Layout, Product Design, Embedded Systems, Computer Vision, Mechanical Design, Automation

​We’ve built the foundation for an AI/ML and Computer Vision product and are looking for a senior technical leader/advisor to help us scale it up.
​We need guidance and hands-on architecture design around:
​Optimizing Computer Vision pipelines for lower GPU load and efficient edge/cloud inference
​High-level system security, database scaling, and API integration
​Technical evaluation and documentation support for patent filing/IP strategy

We are seeking an experienced Computer Vision Engineer to build an end-to-end crack detection and mensuration pipeline. The objective is to produce a model that can identify, segment, and quantitatively measure crack dimensions across the following structural surfaces: concrete, brickwork, mortar joints, and varied masonry.
Expected job functions are:
1. Scrape publicly available datasets containing cracks representative of what is seen on masonry structures
2. Combine publicly available datasets with proprietary datasets from aRoboticsCompany
3. Label and segment relevant cracks in those datasets (relevance to be informed by aRoboticsCompany team)
4. Select an appropriate pre-trained model to conduct transfer training on
5. Conduct transfer training, applying labeled crack dataset to that model
Model expected functions:
1. Generate a semantically segmented mask over cracks in an image
2. Generate estimated length and width for cracks in mask
3. Generate JSON’s or XML’s that list each crack location and metrics in each image

Dataset curation (expected 30 hours of work)
Identify, scrape, and clean publicly available datasets containing masonry/brick/mortar cracks (e.g., open academic datasets, structural inspection archives, web sources) while ensuring quality control and licensing compliance.
Merge curated public data with our internal proprietary crack datasets. Establish standardized annotation formats (COCO-stuff , binary masks, or polygon GeoJSON).
Implement tiling/slicing workfl ows (e.g., SAHI / patch-based inference) to preserve micro-crack details in high-resolution images, along with robust data augmentation (lighting shifts, mortar texture noise, weathered brick surfaces).

Data labeling requirements (expected 200 hours of work)
Label cracks in dataset in accordance with aRoboticsCompany requirements. Labels must consist of manually generated shading or masks over cracks in the training images with pixel level accuracy.

Model training (expect 20 hours of work)
Train, validate, and benchmark high-resolution segmentation models specialized in thin, elongated topologies (e.g., SegFormer, HRNet, UNet with attention/transformers, or YOLOv8/v11-seg).
Ensure the model generalizes across surface variations: differentiating actual structural cracks from natural mortar lines, surface spalling, shadows, effl orescence, and vegetative growth.
Implement post-processing algorithms (e.g., medial-axis skeletonization, Distance Transform, Voronoi/graph-based centerline extraction) to calculate:
Crack Length: Continuous path tracking along the crack skeleton.
Crack Width (Aperture): Local orthogonal width profi les along the skeleton (mean, median, and maximum width).
Model backtesting and documentation (expect 24 hours of work)
Provide clean, well-documented training and data-scraping codebase (PyTorch preferred)
Provide backtesting data quantifying model accuracy

Final Deliverables:
Standalone inference pipeline accepting an input image and returning:
High-resolution semantic crack mask.

Overlay visualization highlighting detected cracks.

Structured output (JSON) reporting crack instance ID, coordinates, estimated length, and estimated width profi le for each named image.

Brief technical report summarizing validation metrics (mIoU, F1/Dice score, width/length error margins against reference test sets).

Development of a computer-vision AI model for football match video analysis, focused on tracking a single selected player and extracting performance metrics.

Scope of work:
- Player detection model (YOLO) trained/fine-tuned on football broadcast footage
- Multi-object tracking (ByteTrack) with single-player lock-on and ID-switch handling
- Two-stage processing: fast preview for player selection, then full GPU analysis
- Pitch calibration to convert video coordinates into real-world positions
- Metrics extraction: distance covered, speed, sprints, activity levels (standing to sprinting), positional heatmap and zone occupancy
- Structured JSON output ready to feed reporting and AI-generated analysis
- GPU inference pipeline (serverless) with testing and accuracy tuning

This contract covers the AI model and analysis pipeline only. The web platform, user interface and report design are not included.

O que contratar primeiro

**Especialista em visão computacional para futebol, rastreamento de múltiplos jogadores e reconhecimento temporal de ações.** Procurar experiência demonstrável em Python, PyTorch, OpenCV, detecção de objetos, associação de trajetórias e avaliação de vídeo. Conhecimento de YOLO/BoT-SORT e reidentificação por aparência pode facilitar o aproveitamento da base; trocar componentes exige comparação e justificativa.

Não exigir uma LLM própria ou um modelo generativo como solução inicial. O problema atual envolve detecção, associação temporal, identidade, bola e classificação. A arquitetura pode combinar modelos especializados. O produto deve executar a análise sem chamadas ao Gemini/OpenAI ou a outro serviço de compreensão visual; hospedagem em GPU é infraestrutura substituível.

## Base que deve ser reaproveitada

| Ativo existente | O que oferece | O que ainda falta |
|---|---|---|
| Detector de pessoas, rastreador e pose | Observações de vários jogadores, caixas e IDs locais | Continuidade confiável em sobreposição, saída/retorno e cortes |
| Detector próprio de bola refinado por 42 épocas | Caixas e observações da bola em vídeos de celular | Cobertura em bola pequena, borramento, oclusão e bola aérea; bola detectada não comprova posse |
| Classificador de ações e refinamentos anteriores | Propostas de passe, condução, finalização e outras classes | Reduzir falsos positivos e ações perdidas; ligar ação ao jogador correto |
| JSONs auxiliares e revisões humanas | Supervisão já aproveitada em treinos próprios | Não são trajetórias densas revisadas de todos os jogadores nem de remetente/receptor |
| Corpus histórico de ações | Dez fontes, 49 positivos e 15 negativos específicos; versão agrupada com 70 grupos | Poucos exemplos e fontes por classe; 70 grupos não são 70 vídeos independentes |
| Contexto e índice de participantes | Evidências por frame e propostas ligadas a atores possíveis | Tracklet não equivale a pessoa global ou perfil; autoria e recepção ainda ambíguas |
| Worker Python e contrato TypeScript | Processamento de pesquisa e JSON estruturado | Integração durável ao upload e aceite de produto são entregas posteriores |
| Backup local | Pesos, corpus, revisões, relatórios e configurações preservados | Fornecer pacote mínimo selecionado; não repassar todo o backup |

Os 11.327 rótulos de bola registrados no intake pertencem à tarefa de bola, não são 11.327 ações. As revisões de identidade disponíveis são esparsas e não substituem anotação de trajetórias. Quantidades finais elegíveis e linhagem devem ser conferidas no inventário do primeiro marco.

## O que precisa de treino e o que precisa de engenharia

| Frente | Trabalho necessário | Prioridade |
|---|---|---|
| Identidade e rastreamento | Diagnosticar associação, estado durante oclusão e recuperação; avaliar se embeddings de aparência precisam de adaptação com pares/identidades anotados | Primeira contratação |
| Bola | Avaliar o checkpoint existente por tipo de erro; se necessário, refinar com caixas revisadas de bola pequena/aérea/oculta e negativos explicitamente anotados | Condicionada ao erro medido |
| Contato e transição entre jogadores | Anotar/evaluar jogador que toca, instante, bola livre, receptor e incerteza; produzir candidatos sem depender apenas do atleta inicial | Segunda etapa |
| Ações temporais | Refinar passe, condução e finalização com clipes/intervalos revisados, máscaras de desconhecido e negativos por classe; avaliar gol separadamente | Segunda etapa |
| Autoria e contagens | Associar evento, autor e receptor a IDs reconciliados; separar tentativa, conclusão e resultado desconhecido | Após evidência de identidade e ação |
| Backend e melhoria contínua | Versionar dataset/modelo, registrar inferência, preservar revisões e avaliar candidatas antes de promoção | Entrega de reprodução; integração do app orçada à parte |

Falta, desarme, drible contra adversário e gol não devem herdar automaticamente o resultado de uma classe parecida. Condução não prova drible bem-sucedido. Bola perto do jogador não prova domínio. Finalização não prova gol. Um gol fora de quadro pode ficar desconhecido.

Skills: Python, Data Processing, OpenCV, Pytorch, Computer Vision, Deep Learning, Object Detection, YOLO, Data Annotation, Model Evaluation

Project Overview:

Skillforce-Ready 2.0 is a workforce re-engagement and capacity-building program funded by the U.S. Department of State. We upskill displaced Sudanese professionals and prepare them to re-enter the competitive Gulf Cooperation Council (GCC) labor market. We are currently in our recruitment phase and need a strategic data miner to map the digital community footprint of our exact target demographic.

The Mission:
We do not need individual candidate lead generation or cold outreach. Your mission is to identify the high-trust digital spaces and community gatekeepers where our target demographic gathers. You will build a rigorously filtered database of localized community hubs and professional micro-influencers who can act as megaphones for our program application.

Target Demographic & Specific Job Breakdowns:
You must identify hubs and influencers that cater to displaced Sudanese professionals who relocated to the UAE, Saudi Arabia (KSA), Qatar, Oman, or Bahrain on or after April 2023.

The communities and influencers must align with our four high-yield target industries and their specific functional breakdowns:

***AI, Data & Cybersecurity

Artificial Intelligence (AI) / Machine Learning (ML) Engineers (NLP, Computer Vision, LLM fine-tuning)

Cyber Security Architects & Engineers (Cloud Security, Threat Intelligence, SOC Analysis, Penetration Testing)

Data Scientists & Quantitative Analysts (Big Data, Predictive Modeling, Business Intelligence)

Mid to Senior Software Engineers (Cloud Native, Full-Stack Python/Java/Go, DevOps/SRE)

***Green Energy & Advanced Built Environment

Green Energy Project Managers (Solar PV, Wind, Green Hydrogen, Carbon Capture/CCUS)

Senior Architects & Project Directors (Mega/Giga-Project Delivery, Master Planning, BIM Specialists)

Structural & Environmental Engineers (Sustainable Infrastructure, LEED/Estidama/Mostadam Compliance)

Smart City & Urban Planners

***Healthcare & Life Sciences

Medical Specialists & Consultants (Specialized Physicians, Surgeons licensed/registered with SCFHS, DHA, or DOH)

Biotech Researchers & Clinical Scientists (Genomics, Bioinformatics, Life Sciences R&D)

Specialized Healthcare Professionals (Health Informatics Specialists, Senior Medical Technologists, Specialized ICU/Critical Care Nurses)

***Specialized Finance & Quantitative Wealth Management

Wealth Managers & Private Banking Heads (Ultra-High-Net-Worth Advisory, Asset Management, Portfolio Structuring)

FinTech Product Managers (Digital Banking, Alternative Payments, RegTech)

Quantitative Analysts & Risk Modelers (Algorithmic Trading, Market/Credit Risk Valuation)

Corporate Tax & International M&A Specialists (GCC Corporate Tax, Transfer Pricing, Cross-Border Advisory)

Specific Deliverables & Google Sheet Structure:
For a fixed contract, you will deliver a clean, properly formatted Google Sheet containing two tabs with the exact columns specified below:

Tab 1: Community Hubs (25 to 30 Hubs)
A mapped list of localized diaspora Telegram channels/groups, WhatsApp groups, Facebook communities, or LinkedIn hubs (e.g., "تجمع مهندسي البرمجيات السودانيين بالخليج", "رابطة الأطباء السودانيين في السعودية").

Column A: Platform (Telegram / WhatsApp / Facebook / LinkedIn)

Column B: Group/Channel Name

Column C: Group URL / Invite Link

Column D: Admin Name

Column E: Admin Profile Link / Contact Info (VERY IMPORTANT: I must gain a direct contact the admin, preferably: a phone number)

Column F: Approximate Member Count

Tab 2: Strategic Diaspora Influencers (15 to 20 Profiles)
Identify high-trust community leaders, career mentors, or professional micro-influencers who actively create content for the Sudanese diaspora in the GCC across platforms like LinkedIn, X (Twitter), YouTube, Instagram, or Telegram.

Column A: Platform (LinkedIn / X / Instagram / YouTube / Telegram)

Column B: Influencer Name / Handle

Column C: Profile URL

Column D: Primary Content Niche (e.g., GCC Tech Careers, Healthcare Licensing, Relocation/Job Strategy)

Column E: Follower/Subscriber Count

Column F: Engagement Quality (High / Medium / Low)

Column G: Direct Contact Method (Email, Business Contact Link, or DM)VERY IMPORTANT: I must gain a direct contact the influencer , preferably: a ***phone number***)

Required Skills:

Native or Professional Working Proficiency in Arabic: You must be able to conduct deep-dive searches using Arabic terms and professional syndicate markers (e.g., تجمع, رابطة, نقابة, مهندسين, أطباء) to locate authentic, Arabic-first diaspora channels.

Expertise in Boolean search and platform discovery across Telegram, LinkedIn, Facebook, and X.

Deep contextual understanding of Sudanese diaspora professional networks within the GCC.

How to Apply:
To prove you have read this brief, please start your proposal with the phrase "April 2023." Briefly outline the 1st three steps you will take to deliver this job. Your application should not be more than 5 bullet lines.