Hire the Best Image/Object Recognition Professionals
Longjumeau, France
Many computer vision models work in a notebook but fail in production. I build OCR, detection, and tracking systems that actually run in real environments so your team can automate workflows and extract real business value from visual data. I work with companies and startups who need robust AI pipelines for document automation, traffic monitoring, retail analytics, or custom visual AI applications. If you want a low-budget experiment with no clear success criteria, Iโm probably not the best fit. ๐ ๐๐ผ๐ ๐ ๐ช๐ผ๐ฟ๐ธ ๐ญ. ๐๐ป-๐๐ฒ๐ฝ๐๐ต ๐๐ถ๐๐ฐ๐ผ๐๐ฒ๐ฟ๐ I begin each project by understanding your goals, constraints, and success metrics to ensure the solution meets your unique needs. ๐ฎ. ๐ ๐ผ๐ฑ๐๐น๐ฎ๐ฟ ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ-๐ฆ๐ผ๐น๐๐ถ๐ป๐ด I break complex tasks into smaller parts and test multiple approaches to find the most effective solution. This ensures reliable results you can trust. ๐ฏ. ๐๐ฒ๐๐ฒ๐ฟ๐ฎ๐ด๐ถ๐ป๐ด ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ & ๐๐๐๐๐ผ๐บ ๐ ๐ฒ๐๐ต๐ผ๐ฑ๐ My toolkit includes all major computer vision tasks: Classification, detection, tracking, and segmentation. I combine off-the-shelf models with custom-built methods to achieve top-notch performance. ๐ฐ. ๐๐น๐ฒ๐ฎ๐ฟ, ๐๐ฟ๐ฒ๐พ๐๐ฒ๐ป๐ ๐๐ผ๐บ๐บ๐๐ป๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป I keep you updated at every step, provide realistic timelines, and immediately address any hurdles. Even if youโre not technical, Iโll explain everything in plain language so you always know where your project stands. ๐ฑ. ๐๐น๐๐ฎ๐๐ ๐ฃ๐๐๐๐ถ๐ป๐ด ๐ฌ๐ผ๐ ๐๐ถ๐ฟ๐๐ My clients consistently give me 5-star ratings and glowing feedback. If your requirements stretch beyond my skill set, Iโll be transparent and let you know right away. ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐ฉ๐ถ๐๐ถ๐ผ๐ป & ๐ฏ๐ ๐ฃ๐ฒ๐ฟ๐ฐ๐ฒ๐ฝ๐๐ถ๐ผ๐ป For projects involving robotics, autonomous systems, or spatial analytics, I also build 3D perception pipelines using LiDAR, stereo cameras, and point clouds. This includes 3D object detection, Birdโs Eye View (BEV) transformations, and point cloud processing using deep learning. ๐ฅ๐ฒ๐ฐ๐ฒ๐ป๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐ญ. ๐ ๐๐น๐๐ถ-๐๐ฃ๐ ๐ข๐๐ฅ ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ: Combined Google Vision, OpenAI, and AWS Rekognition to increase document extraction accuracy across noisy images. ๐ฎ. ๐ฆ๐ฐ๐ฎ๐ป๐ป๐ฒ๐ฑ ๐๐ผ๐ฐ๐๐บ๐ฒ๐ป๐ ๐๐ผ ๐๐ ๐ฐ๐ฒ๐น: Parsed key fields using Python + QwenVL and auto-generated Excel reports. ๐ฏ. ๐๐ถ๐ฐ๐ฒ๐ป๐๐ฒ ๐ฃ๐น๐ฎ๐๐ฒ ๐๐ฒ๐๐ฒ๐ฐ๐๐ถ๐ผ๐ป: End-to-end system with YOLOv8 trained on custom dataset and deployed for inference ๐ฐ. ๐ฅ๐ฒ๐ฎ๐น-๐ง๐ถ๐บ๐ฒ ๐๐ถ๐๐๐ฅ ๐ข๐ฏ๐ท๐ฒ๐ฐ๐ ๐๐ฒ๐๐ฒ๐ฐ๐๐ถ๐ผ๐ป & ๐ง๐ฟ๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด Built a 3D detection and tracking pipeline using LiDAR and camera data with 3D bounding boxes, frame-to-frame association, and 2D/3D visualizations for autonomous navigation. ๐ฑ. ๐๐ฒ๐ฒ๐ฝ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ผ๐ป ๐ฃ๐ผ๐ถ๐ป๐ ๐๐น๐ผ๐๐ฑ๐ Trained PointNet and voxel-based 3D CNNs on ShapeNet Core for point cloud segmentation and classification, including full preprocessing and model visualization. ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐๐ฎ๐ฐ๐ธ I work with Python, PyTorch, TensorFlow, OpenCV, YOLO, Open3D, and modern vision APIs (Google, AWS, OpenAI) to build detection, tracking, and OCR systems. ๐ช๐ต๐ฎ๐ ๐๐น๐ถ๐ฒ๐ป๐๐ ๐ฆ๐ฎ๐ "Yacine is reliable, very good at his job, and very informative. He was able to set up a POC, identify the main pitfalls, and propose solutions independently." "Yacine is committed to provide high quality work. He knows what he's doing. It's a pleasure to work together. I recommend him for data mining and vision work." "Yacine always does a great job on any computer vision related task, he delivered the project very quickly. I will definitely rehire him again whenever needed." ๐ฌ Letโs Talk Send a message describing your computer vision problem and the data youโre working with. If itโs a good fit, weโll discuss the next steps.
- Computer Vision
- Object Detection
- AI Agent Development
- Automation
- OCR Algorithm
- Object Detection & Tracking
- Deep Learning
- Python
- PyTorch
- Image Segmentation
- OpenCV
- TensorFlow
- Image Processing
- Image Recognition
- CUDA
- Machine Learning
- Image Classification
Islamabad, Pakistan
โ 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." โ Skills: โ๏ธ Image/video annotation โ๏ธ Image masking/segmentation โ๏ธ Categorization โ๏ธ Fact-checking annotation โ๏ธ Transcription โ Awards and Recognition: โ๏ธ Data Annotation Team of the Year (2022) โ๏ธ Top 10 Data Annotators on Upwork (2021) โ Why you should hire me: โ๏ธ Highly skilled and experienced data annotator with a successful track record. โ๏ธ Quick learner, staying current with the latest techniques, and committed to going the extra mile for precise results. โ๏ธ 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
Jizzax, Uzbekistan
Hi! I work with image and video data annotation for computer vision projects. I focus on clean, accurate labels and always follow project guidelines carefully. I have experience with bounding boxes, polygons, semantic segmentation, and image masking. I understand how annotation quality affects model performance, so I pay close attention to details and edge cases. Tools I use: CVAT | Roboflow | LabelMe | MakeSense.ai and any other I can quickly adapt to new annotation platforms if needed. If you need a reliable annotator who delivers consistent and well-structured datasets, Iโm ready to help. Skills: -Image & Video Annotation -Bounding Boxes -Polygon Annotation -Semantic Segmentation -Image Masking As a competitive and quick learner, I ensure top-notch outputs. Your project deserves the best start, and Iโm here to provide it through precise and reliable data annotation.
- Computer Vision
- PyTorch
- YOLO
- CVAT
- Python
- Roboflow
- Data Scraping
- OCR Algorithm
- OpenCV
- Data Collection
- Object Detection & Tracking
- Image Annotation
- Deep Learning
- Robotics
Bac Ninh, Vietnam
I specialize in data annotation with a focus on enhancing AI and computer vision projects. With hands-on experience in 2D and 4D labeling for autonomous driving datasets, I am adept at traffic sign annotation, object tracking, and processing LiDAR data. My background in Information Technology equips me with strong analytical skills that ensure accuracy and efficiency in every task. I am eager to apply my expertise in AI data operations to contribute to innovative projects. If you're looking for a detail-oriented professional who can elevate your data quality and enhance your machine learning models, I would love to discuss how I can help drive your project forward. Let's connect and turn your vision into reality.
- Data Annotation
- Data Labeling
- Image Annotation
- Photo Editing
- Adobe Photoshop
- Adobe Premiere Pro
- Adobe Lightroom
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
Gujranwala, Pakistan
With 5+ years of experience and 150+ successful projects, I help businesses build high-performance Computer Vision and AI Agent systems that work in production โ not just in theory. ๐ What I Build โ AI Agents & Automation Pipelines (OpenClaw, LangChain, CrewAI, AutoGen) โ Semantic Search & RAG Systems using vector databases (FAISS, pgvector, OpenSearch) โ Personal AI Assistants with persistent memory & full system access โ Object Detection & Multi-Object Tracking (YOLO26, YOLOv12, YOLO11, YOLOv8, DeepSORT, ByteTrack, BOT-SORT) โ Real-Time Video Analytics & Surveillance Systems โ Face Recognition & Liveness Detection โ Image Segmentation (U-Net, DeepLabV3+, Semantic & Instance) โ OCR & Document AI (Tesseract, Google Document AI, PaddleOCR) โ Industrial Defect Detection & Quality Control โ Medical Image Analysis โ Traffic & Vehicle Detection Systems โ Retail Analytics & Customer Behavior Tracking โ Edge AI Deployment (Jetson, TensorRT, CUDA, Docker, AWS) โ Model Optimization (FPS, latency, memory efficiency) โก What I Deliver โ End-to-end AI systems (data pipelines โ model serving โ deployment โ monitoring) โ LLM and AI agent architectures (RAG, tool use, function calling, multi-agent workflows) โ Semantic search and vector database solutions (OpenSearch, FAISS, pgvector) โ Real-time computer vision systems (detection, classification, tracking, segmentation) โ Custom YOLO model training on your own dataset (YOLOv8, YOLO11, YOLO26) โ Multi-camera surveillance & smart monitoring systems โ Video analytics pipelines with real-time alerting & reporting โ Scalable AI infrastructure on AWS (SageMaker, EKS, Lambda, EC2) โ Production-grade APIs and backend services โ Optimization of existing AI systems (lower latency, reduced cloud costs, improved reliability) ๐ง Core Expertise Computer Vision ยท AI Agents ยท OpenClaw ยท Deep Learning ยท Machine Learning ยท Object Detection ยท Multi-Object Tracking ยท Image Segmentation ยท Real-Time AI ยท Video Analytics ยท OCR ยท Data Annotation ยท Edge AI ยท Generative AI ยท LLM Integration ยท RAG Systems ๐ Tech Stack AI & Vision: PyTorch ยท TensorFlow ยท Keras ยท OpenCV ยท MediaPipe ยท YOLO variants ยท Faster R-CNN ยท Vision Transformers AI Agents: OpenClaw ยท LangChain ยท CrewAI ยท AutoGen ยท RAG ยท LLMs ยท GPT-4 ยท Gemini Tracking & Optimization: DeepSORT ยท ByteTrack ยท BOT-SORT ยท TensorRT ยท CUDA Backend & Deployment: FastAPI ยท Flask ยท Docker ยท AWS ยท Jetson ยท REST APIs ๐ Industries I Serve Retail ยท Security & Surveillance ยท Healthcare & Medical ยท Industrial & Manufacturing ยท Traffic Management ยท Smart Cities ยท Agriculture ยท Sports Analytics ๐ก Why 150+ Clients Chose Me โ 100% Job Success Score โ Top Rated on Upwork โ 5+ years delivering real-world AI systems โ Production-ready, scalable solutions โ Strong optimization โ high FPS, low latency โ Clear communication & on-time delivery ๐ฉ Let's Work Together Looking to build a Computer Vision system, AI Agent, Object Detection model, or Real-Time AI solution? ๐ Message me now โ I'll help you design the best approach and deliver a scalable, production-ready solution fast.
- Computer Vision
- Object Detection & Tracking
- YOLO
- OpenCV
- Deep Learning
- Convolutional Neural Network
- Image Segmentation
- Anomaly Detection
- AI Model Integration
- NVIDIA Jetson
- Generative AI
- Large Language Model
- Retrieval Augmented Generation
- OCR Algorithm
- Python
- Artificial Intelligence
- Machine Learning
- AI Chatbot
- AI Agent Development
- AI Development
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How Image Recognition Works
Interpreting the visual world is one of those things thatโs so easy for humans weโre hardly even conscious weโre doing it. When we see something, whether itโs car, or a tree, or our grandma, we donโt (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.
Weโre 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 itโs 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 (letโs say 30ร30 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 500ร500 pixels), the number of inputs and parameters required increases to truly absurd levels.
Whatโs 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 itโs 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 itโs 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 weโve 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, 3ร3 or 5ร5 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. Weโll 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, weโll break down a picture of grandma into a series of overlapping tiles 3ร3 pixel tiles.
- Next, weโll 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, 3ร3), the neural network required to process them stays small and manageable.
- Then, weโll 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, weโd have a 3x3x3 representation for each tile. (If we were talking about videos of grandma, weโd 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 weโll need to do while also avoiding the problem of overfitting.
Lastly, weโll take our downsampled array and use it as the input for a regular, fully connected neural network. Since weโve 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 Googleโs 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
- Object Detection Specialists
- Pattern Recognition Specialists
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- Generative Model Specialists
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- Transfer Learning Specialists
- Deep Neural Networks Developers
- PyTorch Specialists
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