Multi-Class Object Detection System

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@ahmadayaz

I propose utilizing the YOLOv5 model for efficient detection of vehicles, persons, and animals in images or videos. I will train and fine-tune the model, create a diverse annotated dataset, and develop a FastAPI endpoint for seamless integration. Deployment via a Dockerfile on Azure Container Instances with automatic GPU detection will enhance processing speed. I will provide well-documented source code, trained weights, and reproducible training scripts for future management and extension. Let's collaborate to build a robust computer vision pipeline that exceeds your expectations.

@devendrathakur12

Hi, I recently built a computer vision project where I trained a custom object detection model, improved dataset quality, and deployed it through a FastAPI service for real-time inference. The focus was on reliable predictions, fast response time, and simple deployment. I can handle the full pipeline from dataset preparation and model training to validation, Docker packaging, and a production-ready API with clean documentation. Everything will be easy to retrain and extend as your dataset grows. One question: do you already have an annotated dataset, or should I start by combining public datasets with your own images for better accuracy? Looking forward to building a detector that performs well outside the lab too. Best regards, Dev S.

@corpmember29

Hello, >>>> Multi languages (English and Arabic)Left-To-Right (LTR) and Right-To-Left (RTL) <<<< I have reviewed your requirement for a production-ready multi-class object detection pipeline with FastAPI and Docker deployment. I have 13+ years of experience in computer vision, PyTorch/TensorFlow, YOLO-based detection systems, FastAPI microservices, GPU optimization, and Azure container deployment. I would start with YOLOv8 because it offers an excellent balance of mAP, inference speed, deployment simplicity, and GPU efficiency on NVIDIA T4 hardware. I can take ownership of the complete pipeline including dataset curation and augmentation, model training and fine-tuning, mAP/precision/recall/FPS validation, confusion-matrix analysis, FastAPI inference endpoint development, Docker packaging with GPU auto-detection, and reproducible training scripts. The final delivery will include clean commented source code, trained weights, FastAPI REST service supporting image and base64 input, Dockerfile and docker-compose setup, Azure-ready deployment configuration, and a concise README for setup, retraining, and future extension. I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. WE WILL WORK WITH AGILE METHODOLOGY I am available on desk as per your convenient time zone and will work on your project until you are satisfied with my work. Thanks Christina

@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.

@mobimubasir

Hello there, I can take full ownership of the computer-vision pipeline, from dataset curation and model training to FastAPI, Docker, Azure deployment, benchmarking, and reproducible handover. I would start with YOLOv8 because it offers an excellent balance of detection accuracy, T4 inference speed, mature PyTorch tooling, and straightforward export and deployment options. I will deliver validated weights, mAP/precision/recall/FPS reports, clean source code, GPU auto-detection, and a Docker Compose setup that passes the fresh-clone acceptance test.

@Aaleen110

I understand you need a production-ready multi-class object detection system capable of identifying vehicles, persons, and animals in images and video, exposed via a FastAPI endpoint. I've previously developed and deployed a similar system that achieved 95% precision on a complex traffic scene dataset. My approach involves curating and augmenting your dataset using tools like Albumentations for varied lighting and weather simulation. I'll then train a YOLOv8 model, fine-tuning it on your specific classes and conditions. The trained model will be wrapped in a lightweight FastAPI application, exposing a `/detect` endpoint that accepts image/video frames and returns bounding boxes and class labels in JSON format. What is the preferred output format for the detection results (e.g., JSON with bounding box coordinates, class IDs, and confidence scores, or an image with overlaid boxes)? Ready to start as soon as you confirm scope.

@trm66614

I am excited about the opportunity to take ownership of your multi-class object detection system. The goal of moving from raw data to a production-ready detector aligns perfectly with my expertise in computer vision and deep learning. To begin with, I would curate a comprehensive annotated dataset covering cars, trucks, bikes, pedestrians, and various animals. Data diversity is crucial, especially under varying lighting and weather conditions, to ensure our model performs well in real-world scenarios. For the training phase, I'd recommend starting with YOLOv8 for its impressive balance between accuracy and inference speed. This choice will help us meet your latency target of under 350 ms while achieving the required mAP threshold of 0.85. Validation will involve thorough evaluations like mAP, precision/recall, and more, iterating until our metrics meet your expectations. Additionally, I will ensure smooth endpoint creation via FastAPI, wrapped in a Dockerfile for easy deployment. Finally, I will hand over clean code, trained weights, and the necessary documentation to assist with setup and further extensions. Which model architecture do you feel most comfortable with for initial testing? Looking forward to partnering on this challenge! Best, Talha

@abdulhamidh2

Hello I just read about your project to build a production-ready vehicle, person, and animal detector with FastAPI integration, and it sounds like exactly the kind of challenge I’m equipped for. Creating a reliable pipeline from data curation to deployment is something I’ve successfully done before. I’d start with YOLOv8 due to its excellent balance of accuracy and speed, which fits your latency requirements on an NVIDIA T4 GPU. I’ll curate and expand your dataset carefully to cover all classes in varied conditions, then train and fine-tune the model using PyTorch for flexibility. After validating with mAP, precision/recall, and FPS metrics, I’ll package everything behind a FastAPI endpoint with GPU-aware Docker support for seamless deployment on Azure. Could you share more about your current dataset size or any existing models you’ve tested? Best regards, AbdulHamid

@ska234

Hi. To build this cleanly, I’d start with YOLOv8 in PyTorch, then fine-tune on a curated dataset split by vehicles, persons, and animals, with augmentation for weather, lighting, and motion blur. That gives you strong accuracy with fast inference, which matters for your <350 ms target on T4. I’d validate with mAP, precision/recall, FPS, and confusion matrix, then tune input size, confidence thresholds, and NMS for the best speed/quality balance. The FastAPI layer will accept image uploads or base64, run GPU-first inference, and return structured JSON boxes, labels, and scores. I’d package everything with Docker and a docker compose setup so the repo boots cleanly on a fresh clone and deploys to Azure Container Instances without friction. As a Senior AI Engineer, I have mastered PyTorch, YOLOv8, FastAPI, Docker, and model optimization, and have strong experience in computer-vision pipelines, object detection, and production inference services. I am sure I can deliver high-quality results within 14 days. Let’s get in touch and discuss more. Thanks.

@toriquldev123

Hello There! I’m Md. Toriqul Islam, and I’m excited to partner with you. I can dive into your project immediately. I’m an experienced AI/ML & Computer Vision Engineer with 10+ years of experience building production-grade deep learning, object detection, and FastAPI-based AI solutions. I understand you need a production-ready computer vision pipeline for detecting vehicles, people, and animals, exposed through a FastAPI service with Docker deployment. I would start with YOLOv8 because it offers an excellent balance of detection accuracy, inference speed, and deployment simplicity, making it ideal for achieving your latency and mAP targets while integrating seamlessly with FastAPI and GPU acceleration. I’m skilled in Python, PyTorch, YOLOv8, TensorFlow, FastAPI, OpenCV, Docker, Azure, CUDA, and Computer Vision. I’m ready to start immediately and would be happy to discuss your dataset, target performance, and deployment requirements. Looking forward to hearing from you. Best regards, Md. Toriqul Islam

@demivision

As an amalgamation of decades of experience in software development and cutting-edge technological know-how, I have the requisite skills and knowledge to deliver impeccable results on your Multi-Class Object Detection System project. My wealth of knowledge encompasses everything from C and C++ programming to key areas of your project like Computer Vision, FastAPI, and Machine Learning. In particular, my extensive knowledge of Python offers me a distinct advantage when it comes to curating, expanding, and annotating datasets for object detection models. Additionally, I have a strong command over TensorFlow and PyTorch frameworks and possess the finesse to train and fine-tune state-of-the-art architectures such as YOLOv8 or Faster-RCNN that prioritize both accuracy and inference speed – which is exactly what you need for a production-ready detector. I also understand the importance of documentation, reproducibility, and long-term support. Thus, rest assured, I will provide you with everything you need - clean, thoroughly-commented source code; trained weights; reproducible training scripts; a Dockerfile for effortless deployment; and a comprehensive README file ensuring easy setup, retraining and extension steps. With solid expertise in end-to-end project management combined with my ability to consistently deliver high-quality work that aligns with clients' goals, I believe I am the best fit for this job.

@kend27

Hi, I’d start with YOLOv8 or YOLO11 in PyTorch, because they offer the best practical balance of accuracy, training speed, deployment simplicity, and low-latency inference. For a T4 target under 350 ms, I’d benchmark the small and medium variants first, then export to ONNX or TensorRT if needed. I’ve built production computer-vision pipelines covering dataset preparation, augmentation, model training, evaluation, inference optimization, and FastAPI deployment. My approach would be: Audit and expand the dataset across lighting, weather, camera angles, and object sizes Train with class-balanced sampling and targeted augmentations Track per-class mAP, precision, recall, confusion matrices, and FPS Optimize inference with mixed precision, batching where appropriate, and GPU auto-detection Expose image and base64 inputs through a clean FastAPI endpoint Package everything with Docker Compose, reproducible scripts, weights, tests, and documentation One important point: achieving mAP@0.5 ≥ 0.85 for every class depends heavily on annotation quality and whether “animals” is treated as one broad class or several specific classes. Could you share the current dataset size, annotation format, and whether animal species should be grouped into a single class?

@mahads31

In approaching this project, my key strengths in computer vision, machine learning and development would enable me to curate an extensive image dataset that will adequately address your needs for object detection. My experiences span designing and executing entire software architectures - a skillset which is crucial for implementing the entire pipeline from data curation to setting up the FastAPI endpoint as you require. Moreover, I have experience working extensively with TensorFlow and PyTorch - both common platforms for object detection. Complementing these competencies are my deep understanding of AI Automation tools that will be crucial in ensuring optimal algorithm training and evaluation based on the agreed thresholds like mAP, precision/recall, FPS, and confusion matrix reports. Further strengthening my suitability for this project is my proficiency in Docker-containerization as requested. This skill is key to delivering an easily deployable project to your specified environment of using Azure Container Instance with provisions to auto-detect GPU-acceleration when available. Extra convenience comes with a clearly documented README that I'll provide explaining not just setup and retraining steps but also highlighting potential extensions. Trust me to give you clean, well-documented code along with the trained weights and scripts.

@emilesd617

Hi, I am a full-stack AI developer with 8 years of rich experience in software development. I am familiar with Python, PyTorch, TensorFlow, YOLOv8, FastAPI, Computer Vision, Object Detection, Machine Learning, Docker, and REST APIs. I have experience building AI-powered computer vision applications and deploying production-ready inference services. I would start with YOLOv8 because it provides an excellent balance of accuracy and inference speed. I can curate and fine-tune the dataset, validate the model using mAP and precision/recall metrics, package it behind a FastAPI endpoint with Docker support, and deliver a scalable solution optimized for both GPU and CPU environments. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@invoketech

Hey ! I WILL develop a production-ready computer vision detection pipeline with optimized model training, FastAPI deployment, and Dockerized delivery. will provide you AI-powered object detection system using YOLOv8/PyTorch with a FastAPI REST API, Docker deployment, GPU acceleration, and Azure-ready infrastructure. Key features: Dataset preparation & annotation YOLOv8 model training and fine-tuning mAP, Precision/Recall, FPS & confusion matrix evaluation FastAPI endpoint for image/base64 inference Docker & Docker Compose with GPU auto-detection Azure Container Instance deployment support Clean source code, trained weights & reproducible training scripts We would start with YOLOv8 because it offers an excellent balance of accuracy, inference speed, and deployment simplicity, making it ideal for achieving your latency and mAP targets. With 9+ years of experience in AI/ML, PyTorch, FastAPI, Docker, and cloud deployments, we can deliver a scalable, production-ready solution with complete documentation and post-deployment support. Thanks, InvokeTech

@PrateekP3003

Hello! As per your project post, you are looking to build a production-ready computer vision service that detects vehicles, people, and animals with high accuracy and low latency. My focus will be on curating and validating the dataset, fine-tuning a state-of-the-art object detection model, evaluating it against defined performance metrics, and deploying it behind a scalable FastAPI service with Docker support for seamless Azure deployment. My focus will be on delivering reproducible training pipelines, comprehensive evaluation reports, GPU-optimized inference, clean API design, Dockerized deployment, and well-documented source code that makes future retraining and model extensions straightforward. For this use case, I would start with **YOLOv8** because it provides an excellent balance of mAP, inference speed, deployment simplicity, and strong support for real-time production workloads. I specialize in Python, PyTorch, TensorFlow, YOLO, FastAPI, OpenCV, Docker, Azure, REST APIs, GPU acceleration, and scalable AI/ML deployment. My focus will be on building a reliable, high-performance detection pipeline that is production-ready, maintainable, and easy to integrate into your existing infrastructure. I would be happy to discuss your dataset, target metrics, and deployment environment to ensure the solution meets your performance goals. Best regards, Prateek

@carlosd178

Hello, After a detailed review of your project requirements, I understand that you need a production-ready computer vision pipeline, not just a trained model. I have experience building AI/ML solutions using Python, PyTorch, TensorFlow, Computer Vision, Object Detection, FastAPI, and Docker, with a focus on deploying reliable inference services. The main challenge in projects like this is balancing detection accuracy with real-time performance. I would start with YOLOv8 because it provides an excellent balance between mAP accuracy, inference speed, GPU optimization, and easy deployment. I can handle the complete workflow including dataset preparation, model fine-tuning, evaluation (mAP, precision/recall, FPS), FastAPI API development, Docker packaging, and reproducible training setup. A couple of quick questions: • Do you already have an annotated dataset, or should I handle the complete data collection and labeling process? • Are the three acceptance classes the final target, or should the model support all requested categories (vehicles, persons, and animals)? I would be glad to discuss your expected thresholds and can start working on the pipeline immediately. Best regards, Carlos.

@abdf2010

Hi there, I've taken a close look at your project for a Multi-Class Object Detection System, and I'm confident I can help you build a reliable computer-vision pipeline. You need a system that can accurately detect vehicles, persons, and animals in images or short video clips, and I understand that you're looking for a production-ready detector that can be easily integrated into your existing stack through a FastAPI endpoint. With my background in machine learning and experience with C, Python, and C++ programming, I'm well-equipped to tackle this project. I've worked on similar projects that involved curating annotated datasets and developing object detection models, so I'm familiar with the challenges and opportunities that come with this type of work. To get started, I'd like to discuss your specific requirements and explore how we can expand or curate an annotated dataset that covers the classes you're interested in, such as cars, trucks, bikes, pedestrians, and common domestic or wild animals. Let's chat about how we can move forward with this project and see if we're a good fit.