Hire the Best Image/Object Recognition Professionals
Karachi, Pakistan
Most machine learning projects fail between the prototype and production. I've shipped 47+ that didn't. 🎯 YOLO Detection | 🧍 Pose Estimation | 🏋️ Sports AI | 🛒 Retail AI | 🛡️ CCTV Analytics | 🔄 Tracking | 🧠 ML Pipelines | 🤖 AI Agents | 💬 LLM Integration You have a working concept — or a clear problem involving cameras, video, or image data. The challenge is making it fast, accurate, and stable under real-world conditions. Wrong framework choices. Inference too slow for live video. Models that break the moment lighting, angle, or environment changes. And systems that detect things but can't reason about them or act on them autonomously. That's exactly where most builds stall. I design and build real-time computer vision pipelines that go all the way — from model training to live deployment — and increasingly, from visual perception to autonomous AI agents that understand, decide, and narrate. Object detection · Machine learning · Pose estimation · Multi-camera tracking · Segmentation · Re-identification · Anomaly detection · OCR & ANPR · Optical flow · Depth estimation · LLM-powered reasoning · Agentic decision pipelines While most CV engineers stop at training the model, I go further: → Accelerated inference with TensorRT, ONNX, OpenVINO, and FP16/INT8 quantization (up to 5× faster) → LLM agents layered over CV pipelines for real-time decisions, alerts, and natural language outputs → Mobile deployment via CoreML (iOS) and TFLite (Android) with 10+ live apps shipped → Edge deployment on Jetson, OpenVINO, Apple Neural Engine, and CUDA/cuDNN → End-to-end pipeline: camera input → training → optimization → real-time actionable output Key Accomplishments: ⭐ Generated $5M+ in client revenue ⭐ Delivered 100+ end-to-end computer vision systems ⭐ Successfully launched my own 2 SaaS products ⭐ Real-time sports AI for 7+ sports, improving analytics for 15+ teams ⭐ Mobile AI on iOS (Core ML) & Android (TFLite), powering 10+ apps ⭐ Surveillance, safety, and industrial AI solutions ⭐ Medical imaging AI for 5+ hospitals: tumor detection, ultrasound, test strips ⭐ Model optimization: up to 5× faster inference using FP16/INT8, ONNX, TensorRT, OpenVINO ⭐ Multi-object tracking, re-identification, Model Training 1M+ labelled Dataset ⭐ Agentic CV systems that perceive, reason, and act without human input in the loop If you have read this far, please note that I appreciate you taking the time to learn about me. Personally, it’s been an amazing journey and knowledge exercise to get to this level of competence in AI and software development. Domain Expertise: - Sports & Fitness: athlete tracking, shot detection, scoring automation, drill analysis, pose estimation - Industrial & Workplace: tire defect inspection, PPE compliance, staff monitoring, meter reading, machine vision inspection, automated quality control - Surveillance & Security: ANPR, crowd monitoring, people counting, animal attack detection, exam cheating detection, perimeter security, intrusion detection - Healthcare & Medical: tumor detection, ultrasound processing, test strip analysis, X-ray/CT scan processing, lesion segmentation, medical image annotation - Traffic & Transport: aerial monitoring, traffic flow AI, license plate recognition, vehicle detection, accident detection, parking management - Retail & Business: customer analytics, receipt extraction, retail intelligence, object recognition, shelf monitoring, inventory management Tech Stack: Machine Learning, Deep Learning, YOLOv5, YOLOv8 - YOLO26, Detectron2, DeepSORT, StrongSORT, MMDetection, MediaPipe, OpenPose, PoseTrack, Action Recognition, Semantic Segmentation, Instance Segmentation, OCR, Anomaly Detection, Motion Detection, Object Counting, License Plate Recognition, PyTorch, TensorFlow, TensorFlow Lite, Keras, OpenCV, FastAPI, Flask, Core ML, TFLite, ONNX, TensorRT, OpenVINO, CUDA, Swift, Kotlin, Flutter, Python, C++, AWS, GCP, Azure, Edge Deployment, Mobile AI, Real-Time Inference, Surveillance AI, Aerial Drone Analytics, Video Stream Analytics, AI Automation, LLM Integration (GPT-4o, Claude, Gemini, Groq), AI Agent Frameworks (LangChain, LangGraph, CrewAI), RAG Pipelines, Streaming LLM Inference license plate recognition, aerial drone analytics, surveillance AI, mobile AI, embedded systems, deep learning pipelines, inference optimization, video stream analytics, AI automation, AI for industry 4.0, computer vision pipelines. If your project involves cameras, video, or images — and you need it fast, accurate, fully deployed, and intelligent enough to reason and act autonomously — I am the engineer you are looking for.
- Computer Vision
- Object Detection & Tracking
- Machine Learning
- Artificial Intelligence
- Sports
- Image Processing
- Python
- OpenCV
- Object Detection
- YOLO
- Computer Vision Software
- AI Model Training
- Edge AI
- AWS Lambda
- SwiftUI
- Retail
- Deep Learning
- Healthcare
- AI Development
- SaaS
Islamabad, Pakistan
✅Data Annotation Expert With over 4 years of dedicated experience in data annotation and image labeling, I have a proven track record of consistently delivering top-tier results. My expertise like automotive, fashion, and social media, equipping me with a versatile skill set. I have strong expertise in data annotation tools including Labelbox, CVAT, and Amazon Mechanical Turk. Proficient in annotation standards like PASCAL VOC and YOLO. As a detail-oriented and motivated professional, I am quick to grasp new techniques, always staying updated with the latest trends in data annotation." ✅Skills: ✔️ Image/video annotation ✔️ Image masking/segmentation ✔️ Categorization ✔️ Fact-checking annotation ✔️ Transcription ✅Awards and Recognition: ✔️ Data Annotation Team of the Year (2022) ✔️ Top 10 Data Annotators on Upwork (2021) ✅Why you should hire me: ✔️ Highly skilled and experienced data annotator with a successful track record. ✔️ Quick learner, staying current with the latest techniques, and committed to going the extra mile for precise results. ✔️ Team player with a creative mindset, offering innovative solutions for high-quality data annotation services. Let me know if you are available to have a quick zoom Video call to see my portfolio or ask questions. I will be looking forward to it. Can't wait to work with you. Syed Fakhr
- Image Recognition
- Object Detection
- Image Annotation
- Facial Recognition
- Image Segmentation
- Data Annotation
- Image Resizing
- Data Labeling
- Image Alt Tags
- Image Compression
- Image File Format
- Video Annotation
- Annotated Screenshot
- Radar Polygon
- Quality Audit
Gujranwala, Pakistan
I build real-time AI systems that detect threats, track objects, and turn video into actionable insights ready for real-world deployment. If you need a reliable Computer Vision solution (not just a demo), I can design, train, and deploy it end-to-end. 🎯 What I Do I specialize in building production-ready Computer Vision systems that work in real environments not just controlled demos. From surveillance and safety to traffic analytics and automation, I help businesses turn video data into practical, usable intelligence. 🔧 Solutions I Build ✔ Real-Time Object Detection (YOLOv8 / YOLO11) ✔ Multi-Object Tracking (ByteTrack, DeepSORT) ✔ Surveillance & Threat Detection Systems ✔ Fire & Smoke Detection ✔ PPE / Safety Compliance Monitoring ✔ Traffic Monitoring & Speed Estimation ✔ OCR & Document AI ✔ End-to-End AI Systems (Data → Training → Deployment) 🧠 Why Clients Choose Me Most freelancers stop at training a model. I go further — I build complete systems that actually work in production. ✔ Optimized for real-time performance (high FPS, low latency) ✔ Designed for real-world conditions (low light, fog, motion blur) ✔ Strong focus on data quality & annotation (where most projects fail) ✔ Clean, scalable, deployment-ready code 💡 Most Computer Vision models fail outside the lab — I make sure yours doesn’t. 📊 Selected Projects 🔫 Real-Time Weapon Detection & Tracking AI-powered surveillance system for detecting and tracking firearms in live video streams → Helps improve security response time and monitoring 🔥 Real-Time Fire & Smoke Detection (107 FPS) Early hazard detection system designed for fast response in critical environments → Detects fire/smoke in real-time to reduce risk and damage 🦺 Construction Safety Monitoring (PPE Detection) Helmet detection system with live violation alerts → Improves worker safety and compliance on-site 🚗 Vehicle Speed Estimation System Tracking-based system for real-time speed analysis from video → Useful for traffic monitoring and smart city solutions ⚙️ Tech Stack AI / Deep Learning: PyTorch, YOLO (v8, v11), TensorFlow Computer Vision: OpenCV, real-time video processing pipelines Tracking: ByteTrack, DeepSORT Deployment: FastAPI, Flask, Docker, GPU acceleration Language: Python 🎬 What You Can Expect ✔ Demo available before starting ✔ Clean, well-documented code ✔ Fast communication & regular updates ✔ Scalable solutions ready for deployment ✔ Support with real-world challenges (lighting, motion, noise) 💡 How I Work I follow a complete pipeline: Data Collection → Annotation → Training → Optimization → Deployment You don’t just get a model you get a working system ready to use. 📩 Let’s Build Something Real Have an idea or project in mind? 👉 Send me a message I’ll break down the best approach and can even share a quick demo or plan before you commit.
- Computer Vision
- Object Detection & Tracking
- YOLO
- OpenCV
- Deep Learning
- Image Annotation
- Convolutional Neural Network
- Image Segmentation
- Semantic Segmentation
- Anomaly Detection
- AI Model Integration
- Generative AI
- OCR Algorithm
- Large Language Model
- Retrieval Augmented Generation
- Artificial Intelligence
- Data Annotation
- Python
- Machine Learning
Islamabad, Pakistan
Imagine spending thousands of dollars training an AI model—only to realize the labels were flawed. That’s where I come in. I’m 𝐁𝐞𝐡𝐳𝐚𝐝 𝐀𝐥𝐢 𝐊𝐡𝐚𝐧 and co-founder of 𝐒𝐖𝐀𝐓𝐀𝐢, a data annotation and AI development platform trusted by AI startups and enterprises worldwide. With 4+ years of experience and a team of 178+ skilled annotators, I’ve personally led projects that helped train over 100 production-grade AI models from medical image segmentation, agriculture dataset annotations, AI-based electric poles condition monitoring system to autonomous vehicle perception systems. 🌐𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗧𝗿𝘂𝘀𝘁 𝗠𝗲: 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗟𝗲𝘃𝗲𝗹 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: I specialize in polygonal, pixel-wise and semantic segmentation with an obsessive attention to detail. 𝗔𝗜-𝗔𝘄𝗮𝗿𝗲 𝗔𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻: Unlike generic labelers, I understand the AI pipeline. I don’t just label, I label for performance. Every dataset is annotated with model training, accuracy and edge-case handling in mind. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗦𝗰𝗮𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Delivered $500,000+ worth of labeled data to top AI companies (like Moonvalley, SaharLabs etc) with proven systems for scalability, security and deadlines. 𝗖𝘂𝘀𝘁𝗼𝗺 𝗧𝗼𝗼𝗹𝘀, 𝗥𝗲𝗮𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Worked across CVAT, Labelbox, SuperAnnotate, Roboflow and even client-specific proprietary tools. 🌐𝗦𝘁𝗼𝗿𝘆 𝗼𝗳 𝗚𝗿𝗼𝘄𝘁𝗵: What began as a solo freelancing gig on Fiverr has now become a global agency delivering AI-ready data for some of the most innovative companies on Earth. Now offering our services on Upwork as well. 🛠️𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗜 𝗢𝗳𝗳𝗲𝗿: ✦ Image & Video Segmentation (polygon, semantic, instance-based) ✦ Bounding Box, Keypoint & Landmark Annotation ✦ Text & Audio Annotation (multilingual available) ✦ Dataset Cleaning, Structuring & Preprocessing ✦ Consultancy on AI Dataset Design and Labeling Strategies ✦ AI Model Training and development Image annotation, Bounding boxes, 3D boxes, Video annotation, instance and semantic, Object labeling/tagging,Segmentation, Polygons masks, Text annotation, Line annotation, Key Points annotation, Cuboids, Image classification and categorization.
- Video Annotation
- Data Annotation
- Image Annotation
- Image Segmentation
- CVAT
- Data Labeling
- Computer Vision
- Data Entry
- Data Collection
- Roboflow
- LabelMe
- Labelbox
- LabelImg
- SuperAnnotate
- Computer Vision Software
Cebu City, Philippines
With over 5 years of experience I specialize in annotating and labeling images for machine learning and AI applications. With a proven track record in creating accurate high-quality datasets across diverse domains.
- Data Labeling
- Image Classification
- Image Segmentation
- Data Annotation
- Image Annotation
- Autonomous Vehicles
- Satellite Image
- Computer Vision
- Machine Learning
- Data Processing
- Data Cleaning
- Data Curation
- CVAT
- LabelImg
- Affiliate Marketing
Tashkent, Uzbekistan
Data Annotation Specialist | Image, Video & Audio Labeling I provide accurate, consistent data annotation for machine learning projects. You get clean, well-structured datasets ready for training and evaluation. I work with images, videos, and audio across different domains. I follow strict labeling guidelines and deliver on time. 🚀 Experience - I have supported projects that required: - Image annotation for object detection and classification - Video annotation with bounding boxes and tracking - Keypoint labeling for pose and object parts - OCR text transcription and region labeling - Audio transcription and tagging I understand how high-quality data improves model accuracy. 📌 Annotation Types - Bounding boxes - Polygons and segmentation masks - Keypoints and landmarks - Classification labels - Object tracking across frames - OCR text labeling Audio transcription and labeling ⚙️ Tools - CVAT - LabelImg - LabelMe - Roboflow - Supervisely - Custom annotation tools if needed 💡 How I Work - Follow your labeling guidelines carefully - Double-check annotations for consistency - Maintain naming and folder structure - Communicate clearly and respond fast - Deliver clean datasets ready for training You get reliable, scalable annotation support for your ML pipeline. Send your sample task or guidelines. I will start quickly and deliver fast.
- Computer Vision
- YOLO
- CVAT
- Image Annotation
- Data Annotation
- PyTorch
- Machine Learning
- Python
- Image Processing
- Data Labeling
- OpenCV
- Research & Development
- OCR Software
- Artificial Intelligence
- Deep Learning
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How Image Recognition Works
Interpreting the visual world is one of those things that’s so easy for humans we’re hardly even conscious we’re doing it. When we see something, whether it’s car, or a tree, or our grandma, we don’t (usually) have to consciously study it before we can tell what it is. For a computer, however, identifying a human being at all (as opposed to a dog or a chair or a clock, let alone your grandmother) represents an amazingly difficult problem.
And the stakes for solving that problem are extremely high. Image recognition, and computer vision more broadly, is integral to a number of emerging technologies, from high-profile advances like driverless cars and facial recognition software to more prosaic but no less important developments, like building smart factories that can spot defects and irregularities on the assembly line, or developing software to allow insurance companies to process and categorize photographs of claims automatically.
We’re going to explore the challenge of image recognition and how data scientists are using a special type of neural network to address it.
Learning to see is hard (and expensive)
A good way to think about this problem is of applying metadata to unstructured data. In our article on content-based recommendations, we looked at some of the challenges of categorizing and searching content in cases where that metadata is sparse or nonexistent. Hiring human experts to manually tag libraries of movies and music may be a daunting task, but it’s an impossible one when it comes to challenges like teaching the navigation system in a driverless car to distinguish pedestrians crossing the road from other vehicles, or tagging, categorizing, and filtering the millions of user-uploaded pictures and videos that appear daily on social media.
One way to solve this would be through neural networks. While in theory we could use conventional neural networks to analyze images, in practice this turns out to prohibitively expensive from a computational perspective. For instance, a conventional neural network attempting to process even a relatively small image (let’s say 30×30 pixels) would still require 900 inputs and more than half a million parameters. While that might be manageable for a reasonably powerful machine, once the images become larger (say 500×500 pixels), the number of inputs and parameters required increases to truly absurd levels.
What’s more, applying neural networks to image recognition can lead to another problem: overfitting. Simply put, overfitting is what happens when a model tailors itself too closely to the data it’s been trained on. Not only does this generally lead to added parameters (and thus, further computational expense), it actually results in a loss in general performance when it’s exposed to new data.
The solution? Convolution!
Fortunately, a relatively straightforward change to the way a neural network is structured can make even large images more manageable. The result is what we call convolutional neural networks (also called CNNs or ConvNets).
One of the advantages of neural networks is their general applicability, but as we’ve seen when dealing with images, this advantage turns into a liability. CNNs make a conscious tradeoff: By designing a network specifically to handle images, we sacrifice some generalizability for a much more feasible solution.
Specifically, CNNs take advantage of the fact that, in any given image, proximity is strongly correlated with similarity. That is, two pixels that are near one another in a given image are more likely to be related than two pixels that are further apart. However, in a typical neural network, every pixel gets connected to every single neuron. In this case, the added computational load actually makes our network less rather than more accurate.
Convolution solves this by simply killing a lot of these less important connections. In more technical terms, CNNs make image processing computationally manageable by filtering connections by proximity. Rather than connecting every input to every neuron in a given layer, CNNs intentionally restrict connections so that any one neuron only accepts inputs from a small subsection of the layer before it (like, say, 3×3 or 5×5 pixels). Thus, each neuron is only responsible for processing a certain part of an image. (Incidentally, this is more or less how the individual cortical neurons in your brain work: Each neuron responds to only a small part of your overall visual field.)
Inside a convolutional neural network
But how does this filtering work? The secret is in the addition of two new types of layers: convolutional and pooling layers. We’ll break the process down below, using the example of a network designed to do just one thing: determine whether a picture contains a grandma or not.
The first step is the convolution layer, which actually consists of several steps in itself:
- First, we’ll break down a picture of grandma into a series of overlapping tiles 3×3 pixel tiles.
- Next, we’ll run each of these tiles through a simple, single-layer neural network, leaving the weights unchanged. This will turn our collection of tiles into an array. Because we kept each of the images small (in this case, 3×3), the neural network required to process them stays small and manageable.
- Then, we’ll take those output values and arrange them in an array that numerically represents the content of each area of our photograph, with the axes representing height, width, and color channels. So in our case, we’d have a 3x3x3 representation for each tile. (If we were talking about videos of grandma, we’d throw in a fourth dimension for time.)
Then comes the pooling layer, which takes these three-(or four-)dimensional arrays and applies a downsampling function alongside the spatial dimensions. The result is a pooled array containing only those parts of the image that are more important while discarding the rest, which both minimizes the computations we’ll need to do while also avoiding the problem of overfitting.
Lastly, we’ll take our downsampled array and use it as the input for a regular, fully connected neural network. Since we’ve dramatically reduced the size of the input using convolution and pooling, we should now have something a normal network can handle while still preserving the most important parts of the data. The output of this final step will represent how confident the system is that we have a picture of a grandma.
Note that this is a simplified explanation of how a convolutional neural network works. In real life, the process is (excuse the pun) more convoluted, involving multiple convolutional, pooling, and hidden layers. Additionally, real CNNs typically involve hundreds or thousands of labels, rather than just one.
Implementing convolutional neural networks
Building a Convolutional Neural Network from scratch can be a time-consuming and expensive undertaking. That said, a number of APIs have recently been developed that aim to allow organizations to glean insights from images without requiring in-house computer vision or machine learning expertise.
- Google Cloud Vision is Google’s visual recognition API, based on the open-source TensorFlow framework and using a REST API. It detects individual objects and faces and contains a pretty comprehensive set of labels. It also comes with a few bells and whistles, including OCR and integration with Google Image Search to find related entities and similar images from the web.
- IBM Watson Visual Recognition, part of the Watson Developer Cloud, comes with a large set of built-in classes, but is really built for training custom classes based on images you supply. Like Google Cloud Vision, it also supports a number of nifty features, including OCR and NSFW detection.
- Clarif.ai is an upstart image recognition service that also uses a REST API. One interesting aspect is that it comes with a number of modules that help tailor its algorithm to particular subjects, like weddings, travel, and food.
While the above APIs may be suitable for some general applications, for specific tasks you might still be better off building a custom solution. Luckily, there are a number of libraries available that make the lives of data scientists and developers a little easier by handling the computational and optimization aspects, allowing them to focus on training models. Many of these libraries, including TensorFlow, DeepLearning4J, Torch, and Theano, have been used successfully in a wide variety of applications.
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Similar Image/Object Recognition Freelancer Skills
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