Custom Image Recognition AI System -- 2

@vinod00k

Hello Good Morning, i have 5 years of experience in openCV, ML, and image processing, I have worked before for image recognition and generation projects, i can create the tool for recognition of the custom images, please come over the chat, thanks Vinod

@devanshkhandekar

Hi,I am a Applied ML Engineer with more than 6 years of experience & I can work on your project end-to-end & make it production-ready. My Approach based on the Attached Image: Phase 1 (Scope Lock): I’ll review 50–200 representative samples & your targets (FPS/latency, CPU/GPU/on-device, acceptable FP/FN). We’ll choose the highest-value scope: (A) OCR (text detect+read), (B) Face detect/recognize, (C) Object/template detection, or a hybrid (common for poster/thumbnail-like images: face + headline text). Phase 2 (Data + Baseline):Dataset spec + labeling guide (CVAT). For OCR: polygon/rotated boxes + transcripts; for face: detect + enrollment images per ID; for objects: bbox per class. Strong augments (blur, compression, perspective, occlusion) to match real conditions Phase 3 (Training/Tuning): Best-fit architectures: * OCR: DBNet/CRAFT (detect) + CRNN/SVTR (recognize), multilingual if needed * Face: SCRFD/RetinaFace + ArcFace/AdaFace embeddings + cosine/FAISS * Object: YOLOv8/YOLOv10 optimized for real-time Metrics: mAP/F1 (detect), CER/WER (OCR), FAR/FRR + Top-1 (face) Phase 4 (Deployment): Export to ONNX (TensorRT), ship FastAPI (REST/WebSocket) + batch inference script, Docker CPU/GPU builds, warmup, batching, per-stage latency logs & a short setup guide + commented code. The days range mentioned here is not exact, since I have the script & on device models for OCR & Object detecttion ready so it wont take more than 2 days end to end once the scope is decided.

@hassam945

With a solid background in Software Engineering, and extensive knowledge, and experience in Artificial Intelligence, Machine Learning, Deep Learning, and Computer Vision, I am uniquely positioned to bring value to your image recognition project. I have successfully implemented systems exactly like you're looking for - production-ready solutions that combine accuracy and real-time processing using technologies like Python, TensorFlow, PyTorch and OpenCV. One of the things that makes me a valuable asset is my strong understanding of the entire pipeline. From dataset preparation to model architecture selection to training/tuning and finally deployment via REST/GraphQL API or on-device model. My skills across supervised, unsupervised, and reinforcement learning ensure that your solution will be intelligently built for real-world implementation. In terms of the milestones we can expect in this project, my process involves iterative improvement. I would start with robust data preprocessing and dataset augmentation techniques before exploring several model backbones (CNNs, RNNs or LSTMs) to obtain the best performance. The finalization phase would include optimizing storage and processing demands while delivering on an agreed-upon accuracy/F1 metric. At every stage of the project, I prioritize efficiency, clean coding practices and attention to the smallest details to make sure we don’t just get it working but also achieve a high-quality AI system.

@samyak8840

Hi, I am Samyak, I have 7+ years experience in Python Backend Development. I worked with 8+ clients. I will deliver the project in 2 days. We can connect on chatbox. You can also see my profile. I have certifications in Deep learning and agentic AI from Udacity, developed lot of projects. Thanks

@bhartimalu

Hello, I can develop your Custom Image Recognition AI System exactly as outlined. I’ll help you refine the exact use-case (object/face/text recognition), prepare and preprocess your dataset, choose the most suitable model (CNN, transformer, etc.), train and optimize it for both speed and accuracy, then deliver a working solution with a lightweight API or on-device inference ready for integration. I’ll also provide clear documentation and a tested demo so you can seamlessly deploy it. Regards, Bharti

@InjectAI

Hi, I’d love to help you build a production-ready, real-time image recognition solution. The first step will be a focused evaluation of object detection, facial recognition, and OCR/text detection based on your dataset size, image quality, latency targets, and deployment constraints. I’ll help you choose the use-case that delivers maximum business value with achievable accuracy within your timeline. From there, I’ll handle the complete pipeline: data cleaning and annotation strategy, model selection (CNN, Vision Transformers, YOLO, or hybrid architectures), training and hyperparameter tuning, performance benchmarking (Accuracy, Precision, Recall, F1), and deployment as a lightweight REST/GraphQL API or optimized on-device model. I prioritize scalable, production-grade systems—clean code, modular structure, versioned experiments, and reproducible training workflows. .......... Deliverables .......... • Use-case evaluation report with recommendation • Trained and validated model meeting agreed benchmarks • Inference script or REST/GraphQL API • Deployment-ready package (Docker optional) • Setup guide + fully commented code .......... Tech Stack .......... • Python • PyTorch / TensorFlow • OpenCV • YOLO / CNN / Vision Transformers • FastAPI / Flask • Docker (if required) Please visit my profile to see similar AI and computer vision projects. I focus on delivering 10/10 production-quality solutions with measurable performance.

@mfilipovacfl

Hi, I see you're looking to develop a custom image-recognition AI system, with a focus on real-time accuracy and performance. The first step will be selecting the right task (object, facial, or text detection) based on your workflow and performance targets. Once we finalize the scope, I can help you build the end-to-end pipeline. Here's my approach: 1. Scope Evaluation: I’ll work with you to determine which task (object, facial, or text detection) best fits your data, timeline, and accuracy goals. This phase will involve a review of your sample images and domain knowledge. 2. Model Selection & Training: Based on the task, I’ll choose the most appropriate model architecture (e.g., CNNs for object detection, transformers for text recognition, etc.) and handle the dataset preparation, training, and fine-tuning. I will use Python, TensorFlow, PyTorch, and OpenCV—depending on which provides the best balance of speed and accuracy. 3. Deployment: Once we’ve achieved the agreed accuracy benchmarks, I’ll deploy the model either as an inference script or as an API (REST/GraphQL) for easy integration with your system. I’m ready to start quickly and provide prototypes as soon as we settle on the scope. Let me know if you’d like to move forward or discuss milestones and data requirements in more detail. Best regards, Mihailo

@simar2176

Hello, Greetings of the day. I can help you design and deliver a production-ready image recognition solution from strategy to deployment. The first step will be evaluating your use case—object detection, facial recognition, or text detection—based on your dataset size, latency requirements, hardware target (cloud vs edge), and desired accuracy/F1 benchmarks. I’ll provide a clear technical comparison so we choose the highest-impact, most feasible path. Once scope is defined, I will handle the full pipeline: • Dataset cleaning, labeling review, and augmentation • Model selection (YOLO/ViT/CNN/OCR stack as appropriate) • Transfer learning and hyperparameter tuning • Performance benchmarking and validation • Optimization for real-time inference For deployment, I can provide: • Lightweight FastAPI-based REST API or GraphQL endpoint • Optimized inference script (ONNX/TorchScript if needed) • Clean, well-commented production-ready code • Brief setup and deployment guide I have experience building computer vision systems in Python using PyTorch, TensorFlow, and OpenCV, focusing on both accuracy and runtime efficiency. Happy to review sample images and define milestones immediately. Best regards, Mohit

@gayathria42

Hello, I am a Machine Learning engineer with hands-on experience in Python, Deep Learning, and Computer Vision. I have worked on AI/ML projects during my Google for Developers AI/ML Virtual Internship, where I built and evaluated ML models end-to-end. For your production-ready image recognition solution, I can help you: • Analyze and finalize the best use case (object, facial, or text detection) • Prepare and clean the dataset properly • Select the right model architecture (CNN/Transformer-based) • Train, fine-tune, and evaluate performance • Deploy the solution as a lightweight API (Flask/FastAPI) or optimized on-device model I am comfortable with PyTorch, TensorFlow, and OpenCV, and I focus on building solutions that balance accuracy, speed, and scalability. I ensure clear communication, structured milestones, and timely delivery. I would be happy to discuss your data and performance targets in detail before starting. Looking forward to working with you. Thank you.

@supunchinthana

I propose a structured, production-focused approach to ensure both high accuracy and real-time performance. 1. Project scope identification. Here we will decide the use case (object recognition, character recognition or face recognition) and the target metrics. 2. Development. After obtaining the sample dataset I will begin to process the images. For this purpose I will be using Open CV. Depending on the type of images and the use case I will evaluate the best real-time object detection architectures such as YOLO v8 and Efficientdet. I will be using Python to develop the solution. I hope to develop the system in a couple of stages. Phase 1 -: Preparing the dataset. ++ This includes using Opencv and python to create a pipeline that automatically processes the images to be model ready. Phase 2 -: Choosing and developing the model. ++ Object detection → YOLOv8 or EfficientDet ++ Facial recognition → Embedding-based models (ArcFace/FaceNet) ++ Text detection → OCR frameworks (PaddleOCR or transformer-based) Phase 3 -: Training and fine tuning the model. ++ Developing the model with a chosen architecture. ++ Performance evaluation using Precision, Recall, F1 and mAP Phase 4 -: Making the solution production ready. ++ Export model in an optimized format (ONNX or other format). ++ Docker Container for easier deployment. I am ready to review the dataset and define the technical scope so we can establish milestones and can begin development.

@vhoc7

Jumping straight into training without defining the best use-case often wastes resources. As an AI Engineer specializing in Computer Vision, my first goal is to help you maximize ROI. Here is my approach: 1. Scoping (Consulting): I’ll review your workflow/images and compare Object Detection (YOLO), OCR, and Facial Recognition. We’ll lock the scope that brings the most immediate value. 2. Prototyping: I’ll handle data preprocessing and train a baseline model (PyTorch/TensorFlow), iterating to hit your accuracy targets without losing speed. 3. Production Deployment: A great model needs easy integration. I’ll wrap the optimized model in a lightweight REST API (FastAPI/TorchServe) or provide an on-device inference script. 4. Delivery: You get the trained model, API scripts, and a clear setup guide. Pricing Strategy: I offer a highly competitive bid of ₹13,000 INR for this entire pipeline. As I'm building my reputation here, my priority is delivering a production-ready, 5-star solution. You get enterprise-level AI at a fraction of the cost, and I get a great portfolio piece. (Note: Assumes a manageable initial dataset size). I’m ready to review your sample images today. Let's build something fast and accurate!

@parthiban2006

I can help you design and deploy a production-grade, real-time image recognition solution tailored precisely to your workflow. First, I propose a short discovery phase to evaluate your data, business objectives, and latency constraints. Based on typical performance and deployment trade-offs: Object Detection (recommended for most workflows) delivers high business value when identifying multiple items per image. Facial Recognition is ideal for identity-based systems but requires strict privacy handling. Text Detection (OCR) works best for automation tasks involving labels, IDs, or documents. Once we finalize scope, I will implement an end-to-end pipeline: Dataset Preparation – Cleaning, augmentation, and annotation validation. Model Selection – Optimized architecture (e.g., YOLOv8 for detection, EfficientNet for classification, or transformer-based models if beneficial). Training & Tuning – GPU-accelerated training with accuracy/F1 benchmarking and hyperparameter optimization. Optimization – ONNX/TorchScript export, quantization if required for real-time performance. Deployment – Lightweight FastAPI REST service or on-device model (TensorFlow Lite / ONNX Runtime). Deliverables include a tested model meeting agreed benchmarks, an inference API, well-documented code, and a clear setup guide. ---Parthiban M

@lokdeshwark

With an extensive background in Java and Python programming languages along with a broad range of software architecture skills, I'm confident in my ability to develop and implement your efficient image recognition solution. Having continuously worked with digital products from inception to delivery, I am well-versed in creating scalable systems, crucial for leveraging large datasets like the one you've described. Additionally, my experience in data-driven marketing and SEO would make a valuable contribution to your project by ensuring not only its technical excellence but also its commercial success. By understanding precisely how your audience interacts with data-focused solutions, I can make sure the final product meets both performance targets and user expectations. The ability to adapt in response to variable project sizes and work under tight timelines has been essential to my career success thus far. You can rely on me to quickly evaluate the different use-cases (object, facial or text detection) specific to your dataset and provide timely progress updates. Avec approach centers on clear communication and generating outputs that genuinely impact business growth. That is precisely what you'll get if you entrust me with your image recognition needs.

@BrainstackTech

Hello there, We bring 8 years of experience in production AI/ML — computer vision, deep learning pipelines, and model deployment. Your approach of evaluating object, facial, and text detection before committing is smart and will define everything downstream. Pipeline structure: image preprocessing via OpenCV → model inference → structured JSON output with confidence thresholds. We'd use PyTorch for faster prototyping during evaluation, with ONNX export for lightweight deployment. If object detection wins, YOLOv8. Text detection, CRAFT + TrOCR. Facial recognition, ArcFace embeddings. We'd start with pretrained models and fine-tune on your samples — cutting training time by 70-80%. Deployment via FastAPI with batch inference keeps latency under 200ms. INT8 quantization if you need on-device. We've built AI classification and extraction systems processing 60,000+ records in production — closely mirroring your end-to-end pipeline requirement. For reliability, confidence score filtering catches low-quality predictions before they hit your integration layer, with fallback to a lighter model on timeouts. Delivery: Week 1 — use-case evaluation and data audit. Weeks 2-3 — model training and tuning to agreed accuracy/F1 benchmarks. Week 4 — API deployment plus setup guide. Daily async updates, weekly video check-ins. Looking forward to hearing from you. Naveen Brainstack Technologies

@danny4skill

Im not a specialist in AI but im good at Deep Learning. I completed my Deep learning course last semester and now im about to complete my 3rd project in DL. Im currently in need of money for college expenses. So I'm willing to work in this project and complete it before the given time. If the bidding ends within tomorrow then i have 3 days holiday. I will finish the project earlier.

@soumyadip72

Hi, I’m Soumyadip Bhattacharyya, and I’m a strong fit for this because I’ve built and deployed production-grade image recognition systems before—not just prototypes. I specialize in taking computer-vision models from “works in a demo” to real-time, reliable, integration-ready solutions with measurable accuracy and stable performance. What you’ll get working with me: • Proven delivery: I’ve completed similar end-to-end projects (data → training → tuning → optimization → deployment) where latency, robustness, and clean handoff mattered. • Fast scope validation: I’ll review your sample images and recommend the highest-value path (object vs face vs text) based on your data, labeling effort, timeline, and real-time constraints—so you don’t lose time on the wrong use-case. • Benchmark-driven results: Clear acceptance metrics (F1/precision/recall + runtime), a realistic test set, and iteration until targets are met. • Integration-ready outputs: A tested inference script or REST/GraphQL API, plus commented code and a brief setup guide. Share your deployment target (cloud/edge/mobile), your real-time requirement (latency/fps), and a small sample set, and I’ll reply with the best use-case recommendation and benchmark plan. Best, Soumyadip Bhattacharyya