Discover Computer Vision Projects Efficiently

I need a tool to batch process 500+ auction lot photos at once.

THE TASK:
For each image:
1. Extract/detect lot number (or mark as "NOT FOUND")
2. Identify high-value items ($50+ retail value)
3. Generate SEO-optimized title (80 chars) and description (2-4 paragraphs)
4. Export results to CSV

TECHNICAL APPROACH:
You choose the best tech stack, AI tools, and implementation.
I don't care how you build it - just that it works and is user-friendly.

REQUIREMENTS:
- Batch upload interface (accept 500+ images at once)
- Process images and return structured data
- Export to CSV ready for our auction platform
- Handle errors gracefully
- Include progress tracking

PHASES:
1. MVP: Single image → lot number + title + description + CSV export
2. Batch: Handle 500+ images, progress tracking
3. Polish: Documentation, testing, deployment

WHAT I PROVIDE:
- Sample pallet photos
- Example prompts (showing what good output looks like)
- Clear success criteria
- Feedback during development

HOW TO APPLY:
1. Propose your approach (what tools/tech you'll use)
2. Estimated timeline for each phase
3. Any clarifying questions
4. Portfolio link (batch processing, automation, or relevant projects)

ABOUT THE BUSINESS:
Auction house processing 1,000+ lots weekly. Each lot has 5 to 70 photos.
I'm thinking about using Google Sheets + Make.com + Claude but not complete at this time.

This will be a production system used by my team to speed up cataloging
and improve auction descriptions.

START: ASAP
CONTACT: Todd

We're currently focused on building a premier Data Annotation service targeting leading AI players globally. Our strategic approach involves partnering with various partners and established players in the AI annotation ecosystem to achieve rapid, scalable growth.

Position Overview
We are seeking experienced Part-Time Ad Hoc Data Annotation Consultants to join our growing team on a flexible, project-based basis. This role is ideal for professionals who want to contribute their expertise to cutting-edge AI projects while maintaining flexibility in their schedule.

Employment Type: Freelance/Contract (Part-Time, Ad Hoc)
Location: Remote
Compensation: Competitive, commensurate with experience

Key Responsibilities
Consult and Execute: Provide expert consultation on data annotation projects while executing high-quality annotation tasks across various data types (text, image, video, audio, sensor data)
Quality Assurance: Review and validate annotations to ensure accuracy, consistency, and compliance with project guidelines
Process Optimization: Recommend improvements to annotation workflows, tooling, and quality control processes
Strategic Input: Advise on scaling strategies, partner partnership opportunities, and best practices from the annotation industry

Training & Mentorship: Potentially support training initiatives for annotation teams being developed through our partner partnerships.

Project Scoping: Assist in estimating timelines, resource requirements, and deliverables for client projects

Required Qualifications
Application Requirement: Candidates must submit a detailed statement outlining:

Specific Experience: Comprehensive overview of your data annotation background
Value Proposition: Detailed explanation of unique value you bring

Project Examples: Concrete examples of past annotation projects and outcomes

Strategic Insights: Ideas for how we can leverage our planned partnerships and scale rapidly

Minimum Requirements:

Proven experience in data annotation across multiple domains (minimum 1+ years preferred)
Demonstrated expertise in at least two of the following areas:
Computer Vision annotation (bounding boxes, semantic segmentation, polygon annotation, keypoint detection)
Natural Language Processing annotation (entity recognition, sentiment analysis, text classification, intent labeling)
Audio annotation (transcription, speaker diarization, sound classification)
Video annotation (object tracking, action recognition, temporal segmentation)
Familiarity with industry-standard annotation tools (Labelbox, Scale AI, V7, CVAT, Label Studio, etc.)

Strong understanding of annotation quality metrics and quality assurance methodologies
Excellent attention to detail and commitment to accuracy

Strong written and verbal communication skills
Ability to work independently with minimal supervision
Preferred Qualifications
Experience working with major AI/ML companies or annotation service providers
Background in academic research or university collaborations
Experience training or managing annotation teams
Knowledge of GDPR, data privacy, and ethical AI considerations
Familiarity with annotation automation and AI-assisted labeling techniques
Experience with specific verticals (autonomous vehicles, healthcare AI, conversational AI, etc.)
Technical background in machine learning or data science
Multilingual capabilities
What You Must Clearly Demonstrate in Your Application
1. Experience Portfolio:

Specific projects you've worked on (types of data, annotation complexity, scale)
Tools and platforms you've mastered
Industries or domains where you have deep expertise
Any specialized annotation methodologies you've developed or implemented
Metrics showing your accuracy rates, efficiency, or quality improvements
2. Detailed Value Proposition:

You must articulate in detail what specific value you bring to BokotoAi.com:

Expertise Transfer: How will your experience accelerate our capabilities?
Quality Enhancement: What processes or standards can you implement to ensure world-class annotation quality?
Scaling Insights: What strategies do you recommend for rapidly scaling through partnerships?
Client Acquisition: Can you help us understand what leading AI companies look for in annotation partners?
Operational Excellence: What systems or workflows can you introduce to increase efficiency?
Network & Connections: Do you have relationships with universities, student groups, or industry players that could benefit our partnerships?
Problem-Solving: What unique challenges have you solved in previous annotation projects?
Innovation: What emerging trends or technologies in data annotation can you bring to our projects?
3. Strategic Recommendations:

Suggestions for quality control at scale
Thoughts on competitive positioning in the annotation market
Recommendations for tools, technologies, or methodologies we should adopt

Senior Embedded Vision / Data Capture Engineer (NOT a model training role)

🔍 Summary

We are building a road inspection device that records high-quality video + GNSS/IMU data for offline AI analysis.

We are not looking for a PyTorch/TensorFlow researcher.
We need an engineer who can design and implement a robust embedded video + sensor logging pipeline on an edge device.

If your primary experience is training YOLO models in Python, this role is not for you.


---

🎯 What this role is really about

Your main mission:

Take an industrial camera + GNSS + IMU + embedded board,

And turn it into a reliable “black box” recorder:

1080p video at stable 25–30 FPS,

synchronized GNSS/IMU timestamps,

data safely logged to SSD,

no frame drops, no random exposure jumps.



AI will run offline later.
Your focus is data capture, synchronization, and robustness, not model training.


---

✅ Required Core Skills (must-have, no exceptions)

Embedded Linux & C++

Strong C++ on Linux (not just Python/OpenCV scripts).

GStreamer and/or V4L2 for building low-latency camera pipelines.

Experience with:

multi-threading,

ring buffers,

back-pressure handling,

handling dropped frames, etc.



Industrial Camera Integration

Hands-on experience with USB3 / MIPI / GMSL industrial cameras.

Ability to configure:

exposure,

gain,

shutter,

resolution,

frame rate programmatically.


Understanding of rolling vs global shutter and impact on motion.


Sensor & Timestamp Handling

Reading GNSS (NMEA/UBX or similar) and IMU data streams.

Attaching accurate timestamps to each sensor sample.

Aligning sensor data with video frames (same clock / known latency).


Edge Device Engineering

Experience with Jetson Orin / Xavier / similar ARM-based embedded devices.

Designing video logging systems that:

write to SSD,

survive reboots / power glitches,

recover gracefully from errors.


Comfortable with Linux services, logging, debugging tools.


System-level Thinking

You have built real systems in the field, not just lab demos.

Can define clean interfaces for a hardware manufacturer:

“This is the API/connector/protocol we need”.


Can explain trade-offs: bitrate vs quality vs storage vs CPU load.



---

👍 Nice-to-have (NOT required, but helpful)

Experience with:

automotive or mobile robotics perception,

road / infrastructure imaging,

Shenzhen-based camera or box-build manufacturers.


Basic familiarity with TensorRT/ONNXRuntime only to the extent needed to run a pre-trained model (we can keep this minimal for MVP).



---

❌ This role is NOT for you if…

Please do NOT apply if:

Your main skill is:

PyTorch/TensorFlow model training,

computer vision research,

Kaggle-style experimentation.


You mostly work in:

Jupyter notebooks,

Google Colab,

cloud-only pipelines.


You have never:

built a real-time camera pipeline,

debugged dropped frames or sync issues,

worked directly with industrial cameras.



We respect ML/AI researchers — but this is an embedded systems & data pipeline role.


---

📦 Key Deliverables (January MVP)

For the first MVP (by end of January), we expect:

1. Working prototype on dev kit

1080p video capture at stable 25–30 FPS.

Known and measured latency from sensor to disk.



2. Synchronized logging

Video + GNSS (+ IMU if available) written with usable timestamps.

Simple format (e.g. MP4 + JSON/CSV metadata or similar).



3. Basic configuration tooling

Ability to adjust key camera params (exposure, gain, FPS).

Simple CLI or minimal UI for debugging & verification.



4. Documentation

Clear description of pipeline architecture.

Requirements for the hardware manufacturer (camera/box builder).




You will work closely with a Chinese camera manufacturer (hardware/firmware) and the product owner. Good written communication is important.

I need an automation tool in Python that works on Windows and connects directly to DXCAM (DirectX-based screen capture) to monitor a 'Hold & Win' slot machine in real time. (The game will be run in the browser).

The program must:
• Capture the game window at full speed through DXCAM, without noticeable impact on gameplay.
• Detect the MINI, MINOR, MAJOR, and GRAND symbols the moment they appear and track them as they move across the reels.
• Calculate when the token is at the center of the reel and send a precise automatic 'Stop' command.
• The program must be able to stop the MINI, MINOR, MAJOR, and GRAND tokens.

Key points to consider for the program:
• Performance is critical: the symbols fall quickly, so latency from capture to the stop action must be minimized. • Multithreading to avoid latency and freezes.
• Use any support library you want (for example, OpenCV, NumPy, Torch) as long as the setup remains a single command through requirements.txt.

Deliverables
1. Fully documented Python source code.
2. A README with setup steps, configurable parameters (for example, grid size, hotkey assignments), and troubleshooting tips.
3. Demo video and live session showing that the tool stops in time.

Acceptance criteria will be a live test session in the target game; the script must achieve at least 90% effectiveness.

Skills: Python, Image Processing, OpenCV, Computer Vision, Automation, Machine Learning Algorithms

I need an automation tool in Python that works on Windows and connects directly to DXCAM (DirectX-based screen capture) to monitor a 'Hold & Win' slot machine in real time. (The game will be run in the browser).

The program must:
• Capture the game window at full speed through DXCAM, without noticeable impact on gameplay.
• Detect the MINI, MINOR, MAJOR, and GRAND symbols the moment they appear and track them as they move across the reels.
• Calculate when the token is at the center of the reel and send a precise automatic 'Stop' command.
• The program must be able to stop the MINI, MINOR, MAJOR, and GRAND tokens.

Key points to consider for the program:
• Performance is critical: the symbols fall quickly, so latency from capture to the stop action must be minimized. • Multithreading to avoid latency and freezes.
• Use any support library you want (for example, OpenCV, NumPy, Torch) as long as the setup remains a single command through requirements.txt.

Deliverables
1. Fully documented Python source code.
2. A README with setup steps, configurable parameters (for example, grid size, hotkey assignments), and troubleshooting tips.
3. Short demo video or GIF showing that the tool stops in time.

Acceptance criteria will be a live test session in the target game; the script must achieve at least 90% effectiveness.

Skills: C Programming, Python, Software Architecture, C++ Programming, Image Processing, OpenCV, Computer Vision, Automation

I need a machine learning system to recognize packaged goods in point of sales images. The system should identify:
- Product name
- Product category
- Price
- Number of faces of each product that are available on shelf
- Quality of exposition (based on height)


Ideal Skills:
- Expertise in computer vision and ML
- Experience with image recognition tasks
- Familiarity with retail and shelf analysis
- Strong programming skills in Python or similar

Please provide relevant experience in your bids.

Skills: Python, Machine Learning (ML), React.js, Artificial Intelligence, Image Processing, Computer Vision, Deep Learning, AutoML, Image Recognition, YOLO

Project: AI Object Detection Prototype for Hardware Counter Device (Phase 1 Demo)
I’m looking for an experienced Computer Vision + Embedded Systems developer to create a working prototype demo for an AI-powered counting device. The system must accurately detect and count small physical items (e.g., screws, bolts, connectors) laid out on a flat surface or tray.
This prototype will be used to pitch investors and secure funding for full product development.
Phase 1 Deliverables (What You Will Build Now)
1. AI Counting Demo (Video Output)
Build a computer vision model (YOLOv5/YOLOv8/YOLO-NAS, Detectron2, or similar)
Demonstrate real-time counting of small items placed on a table
Video output must show bounding boxes + total count
Accuracy should be reasonably high for demo purposes
2. Light Prototype of the Future Hardware Workflow
Recommend ideal hardware for Phase 2 (Jetson Nano, Raspberry Pi + camera, edge TPU, etc.)
Produce a simple pipeline showing:
Input from camera or mobile device
AI inference
Output count
3. Technical Summary Document
Which model you used and why
Limitations + what is needed for commercial accuracy
Recommended hardware path for real-world deployment
Ideal Skills
Python
Computer Vision / Object Detection
YOLO frameworks (any version)
Deep Learning (PyTorch, TensorFlow)
Embedded systems or edge AI
Ability to produce a clean demo video
About This Project
This technology will be used in warehouse and industrial environments where workers currently count items by hand — a process that slows down operations and creates errors.
This AI device will eliminate manual counting and allow companies to process orders faster.
We are starting with a simple proof-of-concept. If the prototype is successful, there will be long-term work, including:
Hardware integration
Full mobile/desktop interface
Industrial optimization
On-device processing (no cloud)
Productization
Who I Want to Work With
Clear communicator
Able to demonstrate similar projects or object detection work
Familiar with edge-AI constraints
Comfortable advising on hardware selection
Deliver fast, clean prototypes
This is Phase 1 only, and simply requires a working visual demo + technical report. Future paid phases are planned once investors review the prototype.

YOLO Expert: Crowd Counting from Handheld Video (Stabilization Required)

Project Overview
I am a Data Artist looking for a technical partner to extract crowd data from a video recording of a protest march. I have a handheld video taken from a single location of a continuous stream of people walking past a fixed point.

My goal is to objectively measure the number of attendees, as information on recent protester numbers is hard to attain. I need to convert this visual evidence into a rigorous dataset that I will later sonify (turn into sound).

The Assets
* Input Media: 3 minutes long, 1080p, MP4 file.
* Conditions: Handheld shot from sidewalk.
* CRITICAL NOTE: This footage was NOT shot on a tripod. It contains natural camera drift. Your solution must include stabilization/registration logic (e.g. Optical Flow) to ensure the counting line remains fixed relative to the street.

The Task
Use an object detection model (e.g., YOLO v8/v10 or similar) to analyze my video file and provide the following:

1. Total Unique Headcount: An estimated total count of individuals who passed the camera during the duration of the clip. (Note: Since this is a continuous stream of people walking past, the solution likely requires "line crossing" logic or object tracking/Re-ID to avoid double-counting the same people across frames).

2. Time-Series Data (Crucial): I need the data export to be granular so I can map it to sound parameters.

Required Deliverables
1. The Dataset (.CSV): A spreadsheet with high-resolution temporal data. I specifically need these three columns to separate Crowd Size from Crowd Speed:
* Timestamp (or Frame Number)
* People_In_Frame (Crucial: The count of people visible on screen at that moment. This measures the Density).
* Flow_Rate (People crossing the line per second. This measures the Speed/Movement).
* Cumulative_Count (Total unique people counted).

2. Visual Verification (.MP4): A rendered clip (at least 30 seconds) showing the bounding boxes and the stabilized counting line. This is essential for me to verify the rigour of the count against the camera motion.

Questions for You
1. Stabilization Strategy: Given the footage is handheld, how will you ensure the counting line stays registered to the background/street to prevent counting errors? (e.g., Optical Flow, feature matching, or stabilization pre-processing?)
2. Occlusion: How will your approach handle people blocking each other in a dense crowd?
3. Data Rate: Can you output the CSV data at a frequency of 10 readings per second (or per frame)? I need high-resolution control data for granular audio synthesis.

Budget & Proposal Structure
I am looking for a quick turnaround (48-72 hours).

Quote: Please provide a fixed-price quote for the full deliverable (Stabilization + Count + CSV Export).

Itemization: In your technical workflow, the "Total Count" usually requires tracking individuals over time anyway. However, if generating the detailed granular CSV adds significant manual effort or compute cost to your specific workflow, please specify this in your proposal so we can discuss.

• Have you worked with Optical Flow or camera stabilization for object counting before?
• Please paste a link to a similar project or video verification you have created.
• Can you deliver the specific CSV columns (Flow Rate and Density) requested?

Description:

We are a Canadian travel company that receives scanned and handwritten PDF surveys from travellers after their tours. Each survey has a consistent 7 page layout and contains traveller names, tour metadata, multiple rating grids with checkboxes 1 to 5, several open text sections, and a final page with two handwritten questions about destinations and future travel plans.

We need an experienced AI, OCR, or Computer Vision developer to build a reliable and accurate system that extracts all survey data into structured JSON or CSV. This requires someone familiar with layout detection, checkbox mark detection, image processing, and handling noisy or inconsistent scans.

Project Requirements:

Input
A PDF that may contain one or more survey responses. Each survey consists of 7 pages with the same structure.

Fields to Extract
Identity and metadata such as traveller names, tour code, guide names, and start or end dates.
Multiple rating grids where each row is scored from 1 to 5. You must detect which checkbox is marked even if the mark is messy, faint, or not recognized as text.
Open text answers spread across several pages.
Final page contains two text questions about other destinations and confirmed travel plans for future years such as 2025 to 2027.

Output
A structured JSON object or CSV row containing all extracted fields, including ratings, text answers, and identity fields.

Technical Approach
You may use any tools that ensure accuracy, such as Google Document AI, Google Vision plus OpenCV, Tesseract plus segmentation, LayoutParser, or a custom approach. The key goal is reliability.

Required Capabilities
Accurate checkbox or tick detection for 1 to 5 rating scales.
Consistent layout mapping so each extracted value maps to the correct field.
Separation of multiple surveys in a single PDF.
Clear extraction of open text fields.
Error handling and optional debug output such as bounding box or segmentation logs.
Clean code and documentation.

Success Criteria
All rating fields produce correct numeric values.
All open text fields are extracted reliably.
The system works across multiple scans of the same form template.
The tool can process more than one survey per PDF.
Low error rate and stable output.

Ideal Candidate
Experience building OCR or computer vision systems.
Comfortable with form recognition, checkbox detection, and image processing.
Experience with OpenCV, Tesseract, DocAI, or similar tools.
Understands bounding boxes, segmentation, and dealing with low quality scans.
Able to design a reliable extraction workflow suitable for production use.

Deliverables
A working survey processing tool or module.
Source code and documentation.
Instructions for adjusting or adding field mappings.
Optional assistance deploying into a Google Cloud Function.

We’re an early-stage beauty-tech startup building a computer-vision powered product. This project is a short, tightly scoped pilot build to support a January integration phase (Jan 2–20).

We’re looking for a frontend/UI developer to build a simple, reliable photo capture and upload flow that integrates with an existing backend API. This is a real pilot — not a concept, redesign, or long-term product build.

Scope

You will build a web-based capture flow that includes:
Instruction screens to guide users through photo capture
Image upload UI (4 photos, enforced order)
Client-side validation (file type, size, count)
Integration with backend API endpoints (/upload, /process)
Handling of success, error, and retake states
Light logging to support debugging during pilot testing

This project prioritizes clarity, reliability, and speed over polish.

Out of Scope
Design systems or branding
Auth, user accounts, dashboards
App store builds
Advanced result visualization
Long-term product ownership

Structure & Budget
Fixed-price project, ~$4,000 USD total
Paid in milestones
Scope is intentionally bounded
This is not an open-ended engagement

Timeline
Start: Jan 2
Target completion: Jan 20

This phase will move quickly and require same-day responsiveness during U.S. Eastern business hours

Tech Stack
React or Next.js (preferred)
REST API integration
Lightweight flow guidance provided (not design-heavy)

Who This Is a Good Fit For
You’ve shipped real MVPs or pilot-stage products
You’re comfortable integrating with evolving backend APIs
You work independently and don’t need heavy hand-holding
You’re pragmatic and delivery-focused
You’re available and responsive during the January window

To Apply

Please include:
1–2 examples of relevant frontend work (live product or repo)
Your primary time zone
Confirmation of availability and responsiveness during Jan 2–20

We want an api development and AI automation expert to help us construct a website add on. We want this add on to be implemented by the click of a button, to then pop up the program we need you to make. This program will be an avatar in a blank space, standing still, wearing clothes of choosing. We need it to be possible to rotate this avatar by the click of a mouse and add on the function of having it walk in place.

We are seeking an experienced developer to build a computer vision system that analyzes foot orthotics for quality control purposes. The ideal candidate will have a robust understanding of computer vision techniques and be able to implement a solution that identifies defects and ensures compliance with quality standards. This project requires creativity and expertise in machine learning algorithms, image processing, and data analysis to effectively evaluate orthotic products. If you have a passion for improving healthcare technology and a strong background in this field, we invite you to apply. Please see attached video and pdf.

We are seeking a skilled developer to create an AI-based vehicle detection system capable of recognizing a specific car model and sending real-time alerts. The system should be highly accurate and efficient, integrating with existing infrastructure. Familiarity with machine learning algorithms and image processing techniques is essential. If you have experience in developing similar systems, we would love to hear from you.

We are seeking a skilled developer to create a web application that captures streaming video input, such as from a camera. The application should include functionality to detect individuals in the video feed and overlay virtual accessories, like glasses or other items. The ideal candidate will have experience with video processing, computer vision, and real-time application development. If you're passionate about creating engaging applications and have a strong understanding of the necessary technologies, we want to hear from you!

a perfect example of the solution would be that the user is taking an ipad or similar tablet and he/she sees herself with a (different) pair of glasses or earrings or any other improvement of her face straight away.

I am hiring an expert-level ComfyUI engineer with deep experience in high-fidelity image-to-image replication.
The task is to take an ultrasound image as input and produce an output that is a direct replica of the same image, rendered as a highly realistic, photo-like image.
This is not a reinterpretation, redesign, enhancement, or “inspired by” result. The output must clearly and consistently match the original image’s structure, proportions, facial features, and positioning, with realism being the only transformation.
I will be uploading before-and-after images to verify accuracy. If the output does not closely replicate the input, it will be rejected.
Deliverables
A production-ready API JSON pipeline compatible with RunPod deployment
Not a standard ComfyUI workflow file
The pipeline must be designed for programmatic execution (API-driven, not manual)
Reliability & Success Criteria (Mandatory)
The image generation process must be fully repeatable and deterministic
Across varied ultrasound inputs, the pipeline must achieve a minimum 90% success rate
“Success” is defined as producing a clearly recognizable, high-fidelity replica of the input image
No manual prompt tuning, node adjustment, or intervention per run
One-off results or pipelines that only work under ideal conditions will not be accepted
Required Expertise (Do Not Apply If You Don’t Meet These)
Expert-level ComfyUI knowledge
Proven experience with image-to-image replication, not creative or stylized generation
Strong understanding of model conditioning, control, and consistency
Experience deploying API-based pipelines (RunPod or similar)
Timeline & Availability
This is a 24–48 hour task
You must be able to work diligently and focus exclusively on this project
Applicants who are juggling multiple projects and cannot commit focused time should not apply
Contract Terms
I will not start the contract until you can:
Demonstrate a completed, working pipeline
Show actual before-and-after results
If you cannot already execute this at a high level, do not apply
Proposal Requirements
Include:
Examples of high-accuracy image-to-image replication work
A brief explanation of how you ensure repeatability and reliability

Hi, I’m reaching out because I’m looking for an automation specialist who’s comfortable with Make.com, webhooks, and API integrations.
I’m building AI receptionist workflows for real businesses and I’m looking for someone to help design and maintain the backend systems.
I think there’s an untapped market here and I’m open to discussing ongoing work or a partnership, not just one-off tasks.
If you’re interested, let’s schedule a Zoom call and talk through the idea.

@everyone ┇ BIG REQUEST ┇ NO UPFRONT ┇ PAY ON FULL DELIVERY ONLY

Helios 2 + Titan Two │ Complete Private Discord Dashboard + Full AI Suite + ALL Scripts From Scratch │ NBA 2K26 & Universal FPS

Need ONE real top-tier dev (or small duo) who can deliver the entire package 100% finished. Focus on the best AI tools like YOLO/OpenCV/TensorFlow for god-tier detection, with dead-simple beginner modes (plug-and-play presets, auto-setup wizards, tooltips) and advanced customization (raw variable tweaking, custom model training, script editing IDE in Discord).

Everything included from zero:
- Private Discord bot + pro-level interactive dashboard (embeds that look like Atlas/Phoenix but better – responsive, no lag)
- ALL Titan Two GPC scripts written fresh (anti-recoil patterns, rapid fire mods, perfect release combos, aim injection logic, toggle macros – everything optimized for low detection)
- Helios 2 handling all CV/AI processing
- Full integration: Discord controls Helios → Helios commands Titan Two in real time
- Beginner-friendly: One-tap setups, guided tutorials in bot; Advanced: Unlock Python hooks, threshold sliders, dataset uploads for retraining

Discord Dashboard Details (Scalable UI)
- Clean, modern embeds: sliders for timings/sensitivity, buttons for modes/toggles, dropdowns for profiles/shot types, live graphs for stats (green %, aim drift, recoil curves)
- One-click save/load/share named profiles (export as .gpc or .json)
- Real-time feedback: Detection confidence %, hit/miss logs, visual overlays (preview aim lock or shot meter in Discord)
- Password-locked tester channels + auto-update notifier for new models/scripts
- Beginner mode: Auto-optimize everything with presets; Advanced mode: Full raw access to variables, GPC code editor, AI hyperparams

NBA 2K26 (Current + Next Gen) – Ultimate AI Shot God
- 99-100% green release every single shot with meter AND no-meter modes
- Triple+ detection: Shot meter zones (green/white/yellow), player animations (release point prediction), audio cues (swish/net sounds for feedback), ball trajectory tracking
- Every shot type covered: Jumpers, fades, hops, post spins, stepbacks, dribble pull-ups, catch-and-shoot, off-dribble, leaners, floaters, dunks/layups timing
- Adaptive AI: Detect defender proximity/contest levels (heavy/light/open), auto-adjust release based on stamina/fatigue, badge activation (e.g., Deadeye, Limitless Range)
- Game mode support: MyCareer, Pro-Am, Rec Center, Park – auto-switch profiles based on detected mode
- Patch-proof: One-click retrain button with easy dataset upload (beginners: Bot guides you; pros: Full PyTorch/TensorFlow access for custom models)
- Extra features: Auto-dribble combos for space creation, stamina management (slow down when low), green window expansion via AI prediction
- Zero input lag on HOF difficulty – integrate best tools like PegasusVision CV for pixel-perfect timing

Universal FPS (Warzone, Apex, Valorant, Xdefiant, etc.) – Elite AI Aim Lock Suite
- YOLOv8/v9 (or better) running on Helios 2 for enemy/head/body detection – pre-trained models for instant start, custom training for pros
- All aim modes: Legit (subtle smoothing), Rage (instant snap), Hybrid (toggleable), Silent aim, Triggerbot, Memory/Pixel aim (safest per game)
- Advanced tracking: Wall prediction (track through smoke/walls via movement patterns), aimbot with humanized curves (random offsets, bezier smoothing)
- Live editable recoil tables for every weapon (drag-drop patterns for beginners, script raw values for pros), anti-recoil compensation
- FOV circle customization, priority targeting (closest/low-health/head-first), bone selection (head/neck/chest toggle)
- Extra features: Auto-fire on lock, no-scope assist, quickscope timing, movement prediction for moving targets, anti-ban randomization (variable delays, miss % sliders)
- Stays undetected every season: Auto-update hooks for new anti-cheat patches, mode switcher based on game meta
- Game-specific tweaks: CoD bloom control, Apex legend ability detection, Valorant agent outlines, Xdefiant recoil variance

Tech Requirements (Best-in-Class AI)
- All computer vision/AI on Helios 2 (zero lag, high FPS capture via Elgato/Remote Play)
- You write and deliver ALL Titan Two GPC scripts from scratch (I have nothing – make them modular and easy to flash via Gtuner IV)
- 100% local & encrypted, no cloud dependency – advanced: OpenCV/Python/TensorFlow hooks for custom builds
- Compatibility: PS5/Xbox/PC, future-proof for 2K27+ and new FPS titles

Payment
- ZERO upfront, zero milestones
- Full payment ONLY when I flash the scripts, join the Discord, and everything works perfectly in-game on my console (test beginner + advanced modes)

DM me with:
- Short clip or screenshots of your previous full Helios 2 + Titan Two packages (must show dashboard + working scripts + AI features)
- How long until I get the complete, ready-to-use version
- Your price (paid only on delivery)

New talented devs are 100% welcome — if you’re good with Python/OpenCV, can write clean GPC, and know how to make things easy for beginners while keeping it powerful for pros, hit my DMs. I’ll give you a shot if your proof looks legit.

Let’s build the cleanest, most user-friendly, and strongest 2026 setup ever made.

Skills: Python, User Interface / IA, Software Testing, Machine Learning (ML), Software Development, Computer Vision, Game Testing, Discord API, YOLO, AI Development

Hello,
CCTV-based vehicle tracking system for construction sites. Cameras at entry/exit gates will log vehicles, identify number plates, detect material type (sand, debris, etc.), and estimate whether the vehicle is empty, half, or fully loaded.



There are two scopes—CCTV integration (live feed setup) and AI analytics (vehicle detection & reporting). You can handle either or both based on your expertise. Let’s connect to discuss.

We are seeking an experienced AI/Deep Learning developer to help improve our machine learning model for super-resolution image reconstruction. The goal is to enhance the quality and resolution of microscopy images, enabling more precise analysis and visualization.

Responsibilities:
- Capable of suggesting a proper ML model for the task of transforming fluorescence microscopy images to super-resolution.
- Capable of collecting and preparing the necessary training sets (links to public repositories will be provided).
- Convert and optimize the model for Core ML deployment on Apple devices.
- Document methodology and suggest future improvements.

Requirements:
- Strong experience in Python and deep learning frameworks (PyTorch, TensorFlow, or similar).
- Proven background in image processing, super-resolution, or computer vision.
- Experience converting ML models to Core ML format and optimizing for Apple devices.
- Familiarity with microscopy or biomedical imaging is a plus, but not mandatory.
- Ability to optimize models for performance, accuracy, and cross-device compatibility.
- Clear problem-solving skills and efficient communication.

We are building an Event Intelligence Platform that connects to existing venue CCTV systems and provides real-time operational insights for events (sports, venues, live events).

We are looking for a Computer Vision Engineer to implement and configure camera-based AI modules, working closely with our Head of R&D.

Scope of work (MVP – 30 days)

You will work on practical, deployable AI, not research.

Primary modules:
• People counting without double-count
• Directional line crossing
• Entry/exit counts at gates
• Zone footfall
• Queue estimation
• Queue length
• Approximate wait time
• Crowd density
• Zone heatmaps
• Threshold alerts
• Basic presence / completion detection
• “Object present / missing” (for install verification)

Technical requirements
• Strong experience with computer vision in production
• Experience with:
• Python
• OpenCV
• YOLO (v5/v8 or similar)
• Object tracking (ByteTrack, DeepSORT, etc.)
• Experience handling video streams (RTSP / CCTV / IP cameras)
• Ability to tune models for real environments (lighting, occlusion, crowds)
• Focus on accuracy + stability, not over-engineering

What you will NOT do
• No facial recognition
• No identity tracking
• No academic research
• No building dashboards (handled by another developer)

Deliverables
• Working AI modules that expose:
• Counts
• Densities
• Queue metrics
• Clear API outputs for integration into our platform
• Configuration instructions for different camera angles

Nice to have
• Retail analytics, smart city, or transport analytics experience
• Experience deploying on edge or lightweight servers

Engagement
• Remote
• Fast-paced MVP
• Immediate start
• High chance of long-term collaboration as platform grows

I am looking to hire an experienced Titan Two developer who can create high-quality, reliable scripts compatible with every new NBA 2K release as well as all major FPS games. The goal is to have scripts that work seamlessly across multiple game titles, with consistent performance and full support for modern gameplay mechanics. I also need these scripts to integrate smoothly with Helios 2, whether through computer vision or Python-based input systems, so experience with AI-driven automation is a major plus.

The ideal candidate should have strong knowledge of GPC scripting, controller input logic, anti-recoil systems, timing optimization, and cross-game compatibility. If you have worked on advanced integrations, automated input systems, or multi-game solutions before, I would love to hear from you.

This is a paid opportunity, and compensation will reflect skill, reliability, and the quality of your work. Please reach out with your experience, portfolio, or examples of previous Titan Two projects.
Hiring a Titan Two developer to build scripts for all new 2K titles and all FPS games, fully compatible with Helios 2/Python, plus a customizable in-game menu to adjust settings, controls, and features for each game. Paid project for reliable, expert-level work.

We are a drone software company with a fully built 2D & 3D web viewer frontend. We are now looking for an expert Computer Vision Engineer to architect a custom, high-performance 3D reconstruction engine to power the backend.
The Goal: We want to build a proprietary processing pipeline that surpasses OpenDroneMap (ODM/WebODM) in quality. While ODM is a great starting point, our goal is "commercial-grade" output: sharper edges on buildings, denser point clouds, and superior texture blending without the "fuzziness" often seen in open-source wrappers.
The Challenge (Backend Only): You will architect a custom pipeline (likely leveraging COLMAP + OpenMVS or similar high-end libraries) to achieve:

Superior Dense Matching: Higher density point clouds than standard OpenSfM.

Engineering-Grade Accuracy: Robust bundle adjustment and camera calibration handling (self-calibration).
High-Quality Texturing: Advanced seam leveling and color correction to avoid texture artifacts.
Performance: GPU-accelerated processing (CUDA) to handle 1000+ image datasets efficiently.

Technical Requirements:

Deep expertise in Photogrammetry: Structure from Motion (SfM) and Multi-View Stereo (MVS).
C++ & Python: Ability to write performant C++ code and wrap it for a Python backend.
Libraries: Extensive experience with COLMAP, OpenMVS, AliceVision, or Ceres Solver.
Math: Understanding of Bundle Adjustment, Camera Intrinsic/Extrinsic calibration, and coordinate system transformations (EPSG).

Nice to Have:
Experience with NeRF (Neural Radiance Fields) or Gaussian Splatting (as a future R&D path).
Experience optimizing large LAS/LAZ files for web streaming (Entwine/Potree).

We are seeking a skilled developer to create Titan Two scripts compatible with computer vision or Helios 2. The ideal candidate should have experience in programming scripts specifically for Titan Two devices and a strong understanding of vision processing systems. Communication will be conducted through Upwork, and we do not accept payments or discussions via Discord. If you have a passion for gaming technology and can deliver high-quality scripts, we encourage you to apply!

**Relevant Skills: **
- Titan Two Scripting
- Computer Vision
- Helios 2 Integration
- Programming (C, C++)
- Problem-Solving
- Attention to Detail
I want to give you an example it a discord name input sense I want to learn this and be successful I just want my own willing to learn and be friends https://discord.gg/6TRJ95k3

• Describe your recent experience with similar projects
• Please list any certifications related to this project

We are looking for an experienced Native Mobile App Developer (iOS and Android) with strong expertise in AR-based measurement tools. The goal is to build an application that can measure real-world objects accurately without relying on flat surfaces or controlled backgrounds—similar in functionality to the Apple Measure app.
Project Requirements:
Develop native iOS and Android applications (Swift for iOS and Kotlin/Java for Android).
Ability to measure objects in the real world using AR technology, computer vision, and device sensors.
Measurements should work without constraints such as plain surfaces or clean background conditions.
Accuracy and stability must be comparable to the Apple Measure app.
Experience working with ARKit, ARCore, or similar frameworks is essential.
Clean, maintainable, and well-documented code.
Provide guidance on technical feasibility, edge cases, and measurement accuracy.
Ideal Candidate:
Proven experience building AR measurement or similar CV-based apps.
Strong understanding of 3D space mapping, depth estimation, and object tracking.
Experience with sensor fusion, LiDAR , and spatial computing.
Ability to deliver a reliable and user-friendly UI/UX.