AI Engineer Custom YOLO Object Detection Model

@HNMN3

Being an AI engineer with a special focus on Python, Deep Learning, and Machine Learning, I believe I am your best choice for the Custom YOLO Object Detection Model project. My professional company, STR Softwares LLP, ‎houses some of the most senior and dedicated developers who have successfully delivered numerous premium Python solutions over the past ten years. We take pride in providing our clients with fast delivery without compromising on quality code. With our core proficiency ranging from Python Development and Web Development to Data Engineering and Cloud Solutions, we have got the right skill set to meet your project requirements. Our commitment is always towards technical excellence leading to client satisfaction. Hence, we assure you not just a model trained exactly as per your expectations but also a well-documented process that makes it easy for you to reproduce or retrain as per your future requirements. Let's collaborate and build a production-ready model that gives accurate object detection and classification for all people, vehicles, and animals!

@quan9824

EXPERT in(Computer Vision and Real-time Object Detection, Counting and Tracking) Hi, how are you? I checked your detail carefully. I’ve completed the real-time people detection, counting and tracking projects before successfully. Before, using python and YOLOv8, I completed @@Pool Drowning Detection System Implementation@@ project and so on. You can check my works history on my portfolio. I am sure this field and I will do my best. I always thought "It is your job, it is also my job". Awarding me will be the fastest way to complete your task with the best rates possible. THANK YOU.

@hotanloc

Hello, I can easily DO this project using YOLO. I have already completed many similar project successfully. Project review: https://www.freelancer.com/projects/python/people-detection-counting/reviews Please take a look at my previous Freelancer projects and reviews. Your project aligns well with my experience, and I will do my best to meet all your requirements. My core expertise is in object detection, tracking, and counting. I have developed many computer vision projects, including: * People detection and counting * Product detection and counting * Defect detection in manufacturing * Vehicle detection and speed analysis I have strong experience in image processing and CCTV video analysis, where objects are detected and counted from images or video streams. For your project, I will: 1. Train a model using annotated data to generate optimized weights 2. Develop the detection system based on the trained model 3. Analyze detected objects and display the results clearly My technical stack includes YOLO, OpenCV, TensorFlow, PyTorch, Keras, OCR, and other ML/DL frameworks. With my experience in machine learning and deep learning, I can build an accurate detection system and implement post-processing (such as object counting and analysis) using OpenCV. I am confident I can deliver a high-quality solution within a short timeframe. Please feel free to send me a message so we can discuss your project in more detail. I look forward to hearing from you. Thank you.

@vishal1145

Your dataset is ready, but if the model isn't optimized for edge inference from day one, you'll hit 2-3 FPS on embedded hardware instead of the 15-20 FPS you need for real-time detection. I've seen teams waste weeks retraining because they didn't account for quantization loss during the initial build. Before I architect the pipeline, I need clarity on two things: What's your target edge device - Jetson Nano, Coral TPU, or something else? And what's your acceptable mAP drop when converting to TensorRT INT8 versus FP32? Here's the execution plan: - YOLOV8/YOLOV9 TRAINING: Fine-tune on your dataset using mosaic augmentation and auto-anchor optimization, targeting 0.85+ mAP@0.5 before any compression. - TENSORRT OPTIMIZATION: Export to ONNX then TensorRT with INT8 calibration, profiling inference speed on your actual edge hardware to guarantee sub-50ms latency per frame. - EDGE DEPLOYMENT PIPELINE: Build a Python inference wrapper with batching support and frame skipping logic so the model degrades gracefully under CPU load instead of crashing. - REPRODUCIBILITY PACKAGE: Docker container with pinned dependencies, training scripts, and automated retraining hooks so you can retrain on new data without touching hyperparameters. - PERFORMANCE BENCHMARKING: Deliver FPS metrics across Jetson/GPU/CPU plus confusion matrices showing per-class accuracy for people, vehicles, and animals. I've deployed 4 YOLO-based systems that run on edge devices processing 100K+ frames daily. I don't take on projects where the deployment environment is unclear - let's schedule a quick call to align on hardware constraints and acceptable accuracy tradeoffs before training starts.

@MQamar123

Leveraging my 7+ years of experience, I'm confident in delivering your customized YOLO object detection model with unmatched precision and recall. My expertise in Python, TensorFlow RT/TensorRT, and proven track record in AI projects align well with your requirement. I appreciate the opportunity to engage personally with the dataset from the start of our work alongside providing quick feedback throughout the project to ensure class imbalance issues and annotation adjustments are promptly addressed. Furthermore, I recently completed a YOLO variant project with mixed object detection categories which I believe would be relevant and useful for your project - needed deep comprehension of precisely what each task entailed. With me as your AI engineer, expect nothing but top-notch quality, complete thanks to mAP benchmarks on provided test split full report to validate the trained model's performance. Trust me for on-time superior delivery. Let's get started!

@zahidhasta

Hi, This fits perfectly with my experience. I’ve trained and deployed multiple YOLO-based detection systems on real-world datasets, including multi-class setups and edge deployments. I can build a complete training pipeline with proper augmentation, class balancing, and fine-tuning to achieve strong precision and recall. I’ll optimize the model for low-latency inference using TensorRT, ensuring it runs efficiently on both edge devices and GPUs. You’ll get clean exported weights, a fast Python inference script, and clear documentation so you can easily retrain or extend the model. I’ll also provide a performance report covering mAP and FPS on your test data. I’ve worked on similar detection systems (including real-time deployments), so I can move quickly once I review your dataset. Ready to start. Best regards Zahid Hassan

@chiragardeshna

Hi there, Strong alignment with this project comes from experience delivering custom YOLO-based object detection models with optimized inference for edge and cloud environments. Clear understanding of the requirement to build a full training pipeline, fine-tune with your dataset, and deliver fast, reliable inference with TensorRT optimization. Hands-on expertise with computer vision, transfer learning, and performance benchmarking ensures accurate detection and low-latency deployment. Risk is minimized through structured experimentation, validation, and clear reproducibility with documented environments and scripts. Available to start immediately happy to discuss dataset and share relevant work. Recent work: https://www.freelancer.com/u/chiragardeshna Regards Chirag

@altaireon

This aligns well with my experience in computer vision and building end-to-end ML pipelines. I have 8+ years of experience developing and deploying ML systems, with hands-on work in segmentation and classification tasks. I’ve built defect detection models using U-Net variants with ResNet/SE-ResNeXt encoders and worked on segmentation pipelines with EfficientNet backbones - giving me a strong foundation for training and optimising detection models like YOLO. I’m comfortable setting up the full pipeline you’re looking for: * Data augmentation and handling class imbalance * Transfer learning and fine-tuning YOLO models * Exporting optimised models and building fast inference scripts * Benchmarking performance (FPS, mAP) and documenting results I also have experience building*production-ready pipelines with clean, reproducible setups, ensuring models can be retrained and deployed easily. Optimising for low-latency inference (including TensorRT/TF-TRT) is something I’m confident I can handle. I can take ownership of the full workflow and deliver a robust, well-documented solution.

@jaroslavcaprata

Hi, there. I will build your custom YOLO object detection model using your curated dataset, implementing transfer learning, data augmentation, and fine-tuning to achieve high precision and recall across people, vehicles, and animals. The model will support low-latency inference under 50ms per frame on edge devices and GPUs, processing 20+ FPS reliably. I have trained 5+ YOLO models for multi-class detection with measurable performance gains. I will provide an end-to-end training pipeline, exported weights, and a clean Python inference script that returns bounding boxes, class labels, and confidences in under 1 second. The system will be optimized with TensorRT/TensorFlow RT for efficient edge deployment while maintaining accuracy above 90% mAP on your test split. Documentation will cover environment setup, commands, and retraining steps. Milestone delivery will include the trained YOLO model, inference script, and a short benchmark report detailing FPS, mAP, and resource usage. All components will be tested for reproducibility and edge deployment readiness. If this sounds good, connect in chat and we can start. Thanks, Jaroslav Caprata

@ffulb

Your YOLO detection project needs a solid training pipeline with TensorRT optimization for edge deployment. I'll build you an end-to-end system using YOLOv8 as the base, implement progressive data augmentation during training, and export optimized weights for sub-second inference on both GPU and edge hardware. My algorithmic trading bot handles real-time data processing with similar latency requirements, and I've built systems that need reliable performance under tight constraints. You can see my technical work at ffulb.com. Can start immediately and deliver the trained model, inference script, and performance benchmarks within 2-3 weeks once I have dataset access.

@yacinebsd

Hello, I understand your goal is to build a production-ready YOLO object detection model with strong performance and low-latency inference. This aligns well with my experience. I have hands-on experience training and fine-tuning YOLO models, including keypoint detection projects (spine and leg keypoints) using custom datasets with strong results. In addition, I have broader computer vision experience: Safety Elements Detection project: Built a multi-label classification system using ResNet50 (PyTorch) to detect helmets, vests, and gloves. Handled dataset preparation, augmentation, class imbalance, and generalization challenges. These projects show my ability to work with real datasets and improve model performance iteratively. My approach for your project: Apply transfer learning on a recent YOLO version and fine-tune on your dataset Use data augmentation and class balancing Optimize precision/recall for stable detection Deliver a fast, clean inference pipeline Provide clear documentation and reproducible training steps Benchmark mAP and FPS on your test data I’m comfortable adapting to deployment needs and working in fast feedback loops. I focus on delivering reliable, production-ready solutions. Looking forward to working with you. Yacine

@mrresearcher99

Hello Sir, I have 5 years of experience working with Python development. Let's discuss this further. Thanks, Bhargav.

@amaank66

You’ve already done the hardest part—curating the dataset. Now it’s about turning that into a fast, reliable detector that performs well in real-world conditions, especially on edge hardware. That’s exactly how I approach YOLO pipelines. I’ve worked on object detection systems optimized for both GPU and edge deployment, focusing on latency, accuracy, and reproducibility. How I’ll build your pipeline: • Train using latest YOLO (v8/v9) with transfer learning + targeted augmentation • Handle class balance, anchor tuning, and hyperparameter optimization for strong precision/recall • Validate with proper splits and iterative tuning based on your feedback Deployment & optimization: • Export model to TensorRT / TF-TRT for low-latency inference • Python inference script (fast load, returns boxes, labels, confidences) • Optimized for edge + cloud environments What you’ll receive: • Trained weights + reproducible training pipeline • Clean inference script (production-ready) • Environment setup + commands documented • Performance report (mAP, FPS benchmarks on your test data) Timeline: 5–8 days depending on tuning cycles Goal is not just a trained model—but one that’s fast, stable, and ready for deployment at scale. Best regards, Amaan Khan L. (CUBEMOONS PVT.)

@InjectAI

Hi, This is exactly the kind of project I work on. I can take your curated dataset and build a production-ready YOLO-based detection system optimized for both edge (TensorRT) and cloud GPU inference. I’ll handle the full pipeline: • Training: data augmentation, transfer learning on latest YOLO, and fine-tuning for strong precision/recall • Optimization: export to TensorRT / TF-TRT for low-latency inference • Inference: clean Python script (fast load, returns boxes, labels, confidence) • Evaluation: mAP, FPS benchmarks with a clear performance report • Documentation: reproducible setup + retraining steps I also have an RTX 5060 GPU available for training, which allows faster experimentation and tuning to reach optimal results quickly.

@guptakishan805

Hello, I have 5+ years of experience in computer vision and deep learning, specializing in YOLO-based object detection and optimized inference pipelines. I can build an end-to-end training system using the latest YOLO variant with data augmentation, transfer learning, and fine-tuning to achieve strong precision/recall. My approach includes exporting optimized weights, implementing a fast Python inference script, and deploying with TensorRT/TF-TRT for low-latency performance on edge and GPU environments. I also provide reproducible training pipelines, clear documentation, and performance benchmarking (mAP, FPS). I’ve worked on multi-class detection systems with real-time inference and edge deployment. Let’s connect to train and deploy a high-performance detection model.

@codifyx9shah

Hi, I have understood your requirement clearly. You want to build a production-ready object detection model using YOLO, optimized for low-latency inference on both edge devices and cloud GPUs. I have experience working with YOLO-based models, computer vision pipelines, and optimizing inference using TensorRT / TensorFlow. I can help you build a complete, reliable solution from training to deployment. My Approach - Set up full training pipeline with data augmentation and preprocessing - Use latest YOLO version (YOLOv8 or similar) with transfer learning - Fine-tune model for best precision/recall based on your dataset - Handle class imbalance and improve annotations if needed - Export optimized weights for deployment Inference & Optimization - Build fast Python inference script (load <1 sec) - Optimize using TensorRT / TF for low latency and high FPS - Ensure compatibility with edge devices and GPUs Deliverables - Trained model + weights - Clean inference code - Full documentation (setup + retraining steps) - Performance report (mAP, FPS benchmarks) I will focus on accuracy, speed, and reproducibility. Let’s discuss and start.. Best regards, Mahammad Shah Alam

@mennahassan10

I have hands-on experience in Computer Vision and Machine Learning, and I enjoy working on real-world problems using data and images. I’ve built and trained models using tools like Scikit-learn, and I’m comfortable with key concepts such as supervised learning, feature engineering, and model evaluation. In computer vision, I’ve worked with image datasets, done preprocessing, and applied techniques like image classification and basic object detection using OpenCV. I also focus on understanding how models perform and improving them when needed. I’m always motivated to learn more and apply my skills to build practical and impactful solutions.

@sushantchavan56

Hello, I’m excited to work on building your production-ready object detection model. I have experience with computer vision, YOLO-based models, and Python, including training pipelines, data preprocessing, and model optimization. I can develop an end-to-end pipeline with data augmentation, transfer learning, and fine-tuning to achieve strong precision and recall. I will deliver optimized model weights, a fast-loading inference script, and clear documentation for reproducibility. I also have experience working with performance tuning and can provide benchmarking (FPS, mAP) on your dataset. I’m comfortable collaborating closely, identifying data issues, and improving model performance iteratively. Looking forward to collaborating. Best regards, Sushant Chavan

@ahmad9331

Hi, This is very close to a system I recently built—essentially a full custom pipeline inspired by Google Teachable Machine, but extended beyond it. My version handled image, audio, and pose models in one unified workflow, with production-ready training and inference. On the vision side specifically, I’ve already implemented YOLO-based detection pipelines with transfer learning, augmentation strategies, and TensorRT optimization for low-latency deployment on both edge and GPU environments. For your project, I’ll: Train and fine-tune a YOLO model tailored to your dataset Optimize inference with TensorRT for fast, reliable performance Deliver clean Python inference code (fast load + structured outputs) Provide a fully reproducible pipeline with clear documentation Benchmark mAP and FPS on your test split I’m also used to catching dataset issues early (imbalance, labeling inconsistencies) and iterating quickly. Happy to share a demo of my previous system, it’ll give you a clear idea of how I structure and deploy these models. Best, Ahmad