Python Object Detection Prototype

@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 Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .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

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@toriquldev123

Hello There! I’m Md Toriqul Islam, an experienced Python and computer vision developer with 10+ years of experience, and I’m excited to partner with you. I understand you need a reliable Python object-detection prototype that processes images, detects objects with bounding boxes, saves annotated results, and provides structured detection summaries. I am skilled in Python, OpenCV, PyTorch, TensorFlow, YOLO, computer vision, image processing, model integration, and reproducible environments. I can build the detection script with a suitable model, generate the annotated images and JSON/console output, provide requirements and setup instructions, and structure the code for later integration into your larger pipeline. 1. Which objects should the detection model identify in the three sample images? 2. Will the free three-image test be used only for evaluation, with the full implementation funded separately? Looking forward to hearing from you. Best regards, Md Toriqul Islam

@TheWorkingGroup

Hi! This is my area of interest and expertise. Happy to deliver the result on the 3 test images and then we can take the conversation from there. Best wishes, Salaar Khan The Working Group

@vishal1145

Your detection pipeline will fail in production if the model inference isn't optimized for batch processing and GPU acceleration from day one. Running frame-by-frame without CUDA will bottleneck at 2-3 FPS when you scale beyond sample images. Quick questions - are you planning real-time video processing or batch analysis of static images? And what's your target environment: edge device, cloud GPU instance, or local workstation? Here is the architectural approach: - OBJECT DETECTION: Deploy YOLOv8 with PyTorch backend for 60+ FPS inference on CUDA-enabled hardware, outputting bounding boxes with confidence scores above 0.5 threshold. - COMPUTER VISION PIPELINE: Build modular preprocessing layer using OpenCV for image normalization and augmentation, ensuring consistent input dimensions across varied image sources. - SOFTWARE ARCHITECTURE: Structure the codebase with separate modules for model loading, inference, post-processing, and export so you can swap detection models without rewriting integration logic. I've built similar CV pipelines for 2 manufacturing clients that process 10K images daily with sub-200ms latency per frame. Let's run the proof-of-concept this week and discuss scaling strategy before you commit to a full build.

@gattes

Hi there, I have over 10 years work experience with Linux and python backend development as well as working with OpenCV/TF. Ping me and I'll help you out .Cheers!

@melopgabriel

Hi, Your test request is clear: a small Python proof-of-concept that can load three sample images, run object detection, draw bounding boxes, and save both the annotated images and a compact detection summary. I’ve built similar Python 3.x image-processing scripts using OpenCV and PyTorch, with clean setup files and reproducible local runs. For this, I’d keep the first pass simple and reliable: package the environment, implement a script that processes each image one by one, and return both visual outputs and a JSON or console report so you can verify results quickly. I focus on clean code, readable documentation, and making sure the project is easy to run on your machine. If the proof-of-concept performs well, I can help extend it into a stronger pipeline with model refinement and speed improvements. Best regards, Gabriel

@billybryan98

Hello there. I hope you are donig well. I have successfully developed similar object detection prototypes, utilizing frameworks like OpenCV and TensorFlow to deliver accurate results. My experience includes optimizing models for performance and ensuring clean documentation for reproducibility, which aligns perfectly with your project needs. I understand you require a proof-of-concept for object detection that includes loading images, applying detection models, and summarizing results. I will efficiently implement a script that meets these requirements, ensuring clarity and accuracy in the output. I will provide a high-quality, well-documented script that includes setup instructions, resulting images with bounding boxes, and a summary of detections. My approach focuses on clean code and reproducibility to ensure a seamless transition to further development. Please feel free to reach out to me. I look forward to working with you. Best regards, Billy Bryan

@engruhulajom

Dear Client, I’m a full-stack developer with 10+ years of experience in Python, web applications, APIs, databases, and automation, with a strong focus on clean, maintainable, and well-documented code. I understand you need a small Python 3.x object-detection proof of concept using three sample images, including model inference, bounding-box visualization, saved outputs, concise JSON/console detection summaries, and reproducible environment instructions. If the test meets your requirements, I can extend the pipeline for accuracy, performance, and repository integration. My experience includes Python, REST APIs, database design, Linux, Docker, Git, and structured development workflows. I have a few questions: i. What object categories should the model detect? ii. Are GPU resources available for testing? iii. What accuracy or speed target should we meet? I can keep the POC lightweight, reproducible, clearly documented, and ready for further development. Best regards,

@jayantkumar314

I can build the Python object-detection proof-of-concept and test it against your three sample images, including bounding-box outputs, detection summaries, and reproducible setup instructions. I’ll keep the code clean and structured so it can easily be extended later for better accuracy, performance, and integration into your larger project.

@Badhan685

Hi, I have read the job description and now ready to start. I will provide 100% quality. Please send me a message for discussion. Let's discuss the project. Thanks

@manishashree12

Hello there, I understand you need assistance with setting up reliable object detection code for your image-processing project in Python. For the proof-of-concept test, I will create a Python 3.x script using TensorFlow to detect objects in the sample images you provide. The script will draw bounding boxes around the detected objects, save the resulting images, and provide a concise summary of the detections. I will also include clear setup instructions for easy replication on your machine. Once the accuracy and clarity standards are met, we can proceed with refining the model and integrating it into your repository. I look forward to discussing this further with you. Thanks, Manisha

@codeandcommand

Reliable object detection at 10 to 30 dollars usually means YOLOv8 or v5, since training a custom model from scratch would blow past that budget fast. I can set up the pipeline in OpenCV, get you a working detector on your images within 3 days, and note where CUDA would speed things up if you need real time later. The budget and timeline here are starting points based on the post. Once we go over your image set and accuracy needs, I will confirm the real numbers, and they could move either way. I can start today. Want me to send a quick plan?

@azwa04

Hi, Could you please share the three sample images you’d like me to work with? I can set up a reliable object-detection code using TensorFlow or OpenCV. For the test, I’ll create a script that loads each image, detects objects, and draws bounding boxes. The resulting images will be saved locally, and I’ll provide a concise summary of the detections in JSON format, along with clear setup instructions. With over 4 years of experience in Python and a strong background in image processing, I can ensure that the results are accurate and the code is well-documented. If the test meets your standards, I’d love to discuss refining the model and expanding the pipeline further.

@ABDOKAMA

السلام عليكم ورحمة الله وبركاته، معك عبد الرحمن كمال، مهندس متخصص في الرؤية الحاسوبية (Computer Vision) والذكاء الاصطناعي، ولدي خبرة واسعة تتلخص في تنفيذ أكثر من 20 مشروعاً بمستويات صعوبة مختلفة في هذا المجال. لقد اطلعت على متطلبات مشروعك بعناية، وأنا على أتم الاستعداد لتنفيذ الاختبار المبدئي (Proof of Concept) المجاني للصور الثلاث التي ستوفرها، لضمان جودة ودقة العمل قبل إسناد المشروع بالكامل.

@AnjosGustavo

Hi, I have experienced in several projects like face detection, object tracking, image segmentation, style conversion in CV domain and this strongly matches my CV/ml background, and production image-processing pipelines. Beyond producing detections, I’ll explain the model choice, confidence thresholds, preprocessing, outputs, loss function, NMS, and how accuracy/speed can be improved for your specific neural arch. You’ll receive reproducible code, requirements, annotated images, and clear documentation. If expanded, I can support fine-tuning, evaluation, optimization, and repository integration. I choose that : model : Yolo framework : tensorflow or Pytorch. CV library : OpenCV Best, Gustavo

@robertb297

A free test run before award is a fair filter, but worth flagging that a strong pretrained model like YOLO already handles general object detection well out of the box, so the real question for the full pipeline is whether your objects are common classes or something custom that'd need fine-tuning later. I'd use YOLOv8 via ultralytics for the test since it's fast to set up and gives clean bounding boxes with confidence scores out of the box. Send over the three sample images and I'll run the test.

@bojans9

Hello, I can build the Python object-detection prototype using OpenCV and a suitable ML/deep-learning model, with bounding boxes, detection summaries, saved output images, and reproducible environment/setup instructions. My experience includes Python, machine learning with Scikit-learn, AI solutions, image/data processing, and building clean, reproducible pipelines. I can structure the prototype so it can later be extended for model refinement, performance improvements, and repository integration. I’m happy to demonstrate the approach using your sample images, but I would prefer to perform the prototype as part of the funded milestone rather than an unpaid test. Bid: $120 fixed Timeline: 1–2 days Milestone funded before development.

@avijitdey239

As a seasoned Computer Science graduate, I have dabbled extensively in Python and associated frameworks such as OpenCV, TensorFlow and PyTorch. My nine-year experience in the field of Lead Generation & Data Research has fine-tuned my abilities to create clean, efficient and solution-oriented code, which is essential for your project. I am confident that my skills will match the requirements of your image-processing project. To test the waters, I will deliver a brief proof-of-concept script that loads each image, runs the most appropriate detection model based on your needs, and draws bounding boxes - all documented and explained for your ease. I'll also provide concise summaries of detections as you requested. These initial results - accurate and well-documented - will no doubt propel us to proceed towards refining the model, improving speed and integrating with larger repository seamlessly. Throughout my career, I have prioritized clear communication, fast turnarounds and maintaining attention to detail. Pair these qualities with my adaptability to projects of any size, my aim for delivering reliable data and actionable solutions, and you have yourself a promising collaboration option. Let's work together to build an object-detection pipeline that meets your clarity and accuracy standards beyond just the initial stage!