Development of a Custom YOLO Model for Mussel Detection and Tracking

@shakilan4

Hi, I can train a custom YOLO model for real-time mussel detection and tracking with stable IDs, accurate counting, and ≥15 FPS performance, delivering optimized weights, inference scripts, full training documentation, and detailed evaluation metrics. Best regards, Shakila Naz

@ykarora26

Greetings! I am a skilled developer experienced in training custom YOLO models, specializing in real-time object detection and tracking. Your project requiring a YOLO model for mussel detection and classification into specific classes aligns perfectly with my expertise. I am confident in meeting your project requirements for real-time performance, counting and tracking functionalities, and ensuring high confidence in object identification. With a comprehensive dataset and a focus on delivering final weights, training documentation, and a performance report, I am excited about the opportunity to collaborate on this innovative project. Regards, Yogesh Kumar

@arslanshahid386

Hi there, I see you're looking for a custom YOLO model to detect and track mussels in real-time video feeds, with a focus on distinguishing between individual mussels and clusters. With 4+ years of experience in machine learning and computer vision, I can help you develop a robust model that meets your requirements. My approach will involve using the dataset you provide to train the model effectively, ensuring it achieves high precision and recall for both classes. I’ll prioritize real-time performance to maintain at least 15 FPS on a standard NVIDIA GPU, while also implementing robust tracking to maintain consistent IDs for each mussel. To ensure clarity, may I ask how you envision the integration of this model with your existing camera systems? Best regards, Arslan Shahid

@johnedwardl

Hi, I will develop a custom YOLO model that specializes in detecting and tracking mussels in real-time video. My experience with YOLOv8 and YOLOv11, coupled with a strong background in computer vision, ensures that I can deliver a solution that meets your performance criteria while accurately distinguishing between individual mussels and clusters. I will design the model to maintain at least 15 FPS on a standard NVIDIA GPU, focusing on both accuracy and efficient processing. The system will reliably count lost mussels and track them with consistent IDs across frames, providing the high confidence required for effective monitoring. With your comprehensive dataset of over 5,000 images, I will handle the labeling and training process. You will receive the final weights, an inference script compatible with PyTorch, and thorough training documentation detailing hyperparameters and performance metrics, including Precision, Recall, and mAP. Let’s discuss your timeline and any specific requirements you have for the training process. Thank you.

@ivicab6

As a full-stack engineer with a deep skill set in Machine Learning and Python, I’d like to assert my suitability for your custom YOLO model development project. With six plus years of experience, I have designed and deployed numerous models, empowering businesses through data-driven solutions. My proficiency goes beyond training the models; I also possess data pipelining and ML component experience needed for efficient reporting, dashboards, or even forecasting systems. Real-time performance is at the core of my work philosophy. I consistently prioritize accuracy alongside fluid processing for live monitoring in order to achieve the desired balance between high FPS and precision recall rates – a crucial aspect in your mussel detection and tracking project. Additionally, my history of automating business processes and system-to-system workflows aligns coherently with your need for an inference script compatible with PyTorch or the Ultralytics CLI. Ultimately, I'm committed to delivering top-notch deliverables as required by your project terms - final weights & inference script, training documentation, performance report showcasing metrics such as precision and recall - all documented with exceptional clarity in the README, ensuring that you can replicate the outputs on your end hassle-free. Thanks for considering my candidacy.

@cindyviorinar

Hello, With my extensive experience developing end-to-end applications and a deep knowledge of C, C#, and Python, I am confident in my ability to tackle your mussel detection and tracking project. Over the years, I have developed real-time applications that require high accuracy along with fluid processing, just as your project demands. I have deployed object tracking designs that consistently maintain over 15FPS even on standard NVIDIA GPUs while delivering precise results. Having worked on similar machine learning projects, like the one you have in mind, I have a profound understanding of the unique features and challenges they present. I am well-versed in PyTorch and Ultralytics' CLI, which would ensure seamless delivery of the final weights and inference script compatible with your stack. My analytical approach combined with extensive training data ensures the model will detect mussels with high precision and recall and maintain stable IDs for accurate tracking. Lastly, as a developer who prioritizes open communication, clean documentation, and regular updates throughout the project journey, I guarantee your satisfaction not just with the end product but also with the entire development process. If chosen, I will provide you with detailed training documentation, performance metrics as well as a clear README to reproduce the inference and its results on your end. Together, we will transform your dataset into an efficiently perfo Thanks!

@Abutalha5

Hi, I am Abutalha, a machine learning engineer with experience in computer vision and real-time object detection systems. For this task, I will train a custom YOLO model (YOLOv8 or latest stable version) to detect and classify “mussel” and “group” with high accuracy. I will handle dataset preparation, training, and optimization to ensure real-time performance (15+ FPS) on a standard NVIDIA GPU. I will also integrate object tracking (e.g., DeepSORT/ByteTrack) to maintain consistent IDs and accurate counting across frames. You will receive trained weights, an inference script, training notebook with hyperparameters, a performance report (Precision, Recall, mAP), and a demo video showing tracking results. I can start immediately and deliver a working version quickly for testing. Best Regards, Abutalha

@abhiramukk

⭐⭐⭐⭐⭐ Custom YOLO Model Expert Ready to Enhance Mussel Detection and Tracking Hi there, I understand the need for a custom YOLO model to accurately detect and track mussels in real-time video streams. Your focus on real-time performance, counting, tracking, and high confidence levels align perfectly with my expertise. ✅How I will help: - Implement a custom YOLO model optimized for real-time performance - Ensure accurate counting and consistent ID tracking for individual mussels - Achieve high precision and recall for both "mussel" and "group" classes ✅ Work Experience: - Extensive experience in developing custom YOLO models - Proven track record in object detection and tracking - Strong background in optimizing models for real-time applications ⚫Quick Questions: - How critical is it to achieve a specific FPS target for real-time processing? - Are there any specific performance metrics or benchmarks you aim to meet? Looking forward to discussing further how we can achieve your project goals together. Regards, ✍️ AB

@peterj514

Hi there, I understand you need a real-time Mussel detection and tracking solution with two classes: mussel (individual lost mussels) and group (clusters), optimized for live camera feeds on NVIDIA GPUs. I’ve spent the last 4 years solving exactly this type of problem, delivering robust YOLO-based detectors with real-time tracking and precise counts. I will start by curating and augmenting your 5,000-image dataset, then train a YOLOv8/YOLOv11/YOLOv12 family model optimized for your target classes. Core steps include: selecting an efficient backbone and training regime for high mAP and stable 15+ FPS on your hardware; integrating a lightweight tracking module (DeepSORT/ByteTrack) to assign consistent IDs frame-to-frame; implementing per-frame mussel counting with robust ID association; and producing an inference script compatible with PyTorch or Ultralytics CLI, plus clear hyperparameter documentation and a performance report with precision, recall, and mAP. Deliverables will include final weights, an inference script, a training notebook, a tracking-enabled sample video, and a detailed README to reproduce results. I’ll ensure the model distinguishes mussel from group with high precision and recall and includes a reliable counting mechanism across frames.

@dylanr36

Hello, As a seasoned Full-Stack and Mobile App Developer with more than 8 years in the field, I offer a multifaceted skillset for your mussel detection and tracking project. From custom app development to AI integration, my experience traverses the entire spectrum of technologies crucial to completing this project successfully. I am well-versed in the Python programming language you require for training a custom YOLO model. I have an extensive background in building and deploying models using various frameworks including PyTorch. Additionally, my expertise in databases such as SQL and NoSQL ensures that I can seamlessly manage your comprehensive dataset of over 5,000 images for accurate labeling and training. Delivering consistent performance without sacrificing accuracy is always a priority in all of my projects, and your mussel detection system is no exception. With an adept understanding of real-time processing on standard NVIDIA GPUs, I can guarantee at least 15 FPS while maintaining high precision/recall rates and stable ID tracking across consecutive frames. My commitment to timely delivery, excellent communication, and client satisfaction coupled with my broad technical abilities showcases why selecting me is not just a sound decision but a long-lasting partnership for the successful completion of this complex AI-driven project. Thanks!

@alirashdi26

Hey , I just finished reading the job description and I see you are looking for someone experienced in YOLO, C Programming, Computer Vision, C++ Programming, C# Programming, Python, Deep Learning and Machine Learning (ML). This is something I can do. Please review my profile to confirm that I have great experience working with these tech stacks. While I have few questions: 1. These are all the requirements? If not, Please share more detailed requirements. 2. Do you currently have anything done for the job or it has to be done from scratch? 3. What is the timeline to get this done? Why Choose Me? Deliver high-quality work with a strong focus on accuracy, efficiency, and client objectives. Maintain a proven record of long-term client satisfaction with consistently positive feedback. Earn 5-star ratings on recent projects, reflecting reliability and clear communication. Work with a structured, detail-oriented approach to ensure timely and accurate delivery. Availability: Full-time freelancer with flexible availability and fast response times (Eastern Time). I will share with you my recent work in the private chat due to privacy concerns! Please start the chat to discuss it further. Regards, Ali

@shafeeqm6

⚠️Zero risk on your side. If it's not right, you don't pay⚠️ I understand you need a high-precision, real-time YOLO model specialized in distinguishing individual mussels and clusters with seamless tracking and counting. Having successfully developed custom object detection and tracking solutions with YOLOv8 and YOLOv12 for similar real-time video projects, I bring expertise in model training, optimization, and delivering reliable, quality-focused outcomes. I will provide examples of past projects that reflect this experience. My approach is client-centered, ensuring clear communication with regular updates and calls, focused on integration without friction and trusted results. Message me — worst case, you lose nothing; best case, enjoy a free consultation and real clarity. Looking forward to discussing your project in detail. Kind Regards Shafeeq

@donm82

⭐ I can train a custom YOLO model to detect and track mussels in real-time video with high accuracy and stable object IDs. I’ll use YOLOv8 or the latest YOLOv12 with PyTorch / Ultralytics framework, optimizing for both speed and precision to maintain 15+ FPS on standard NVIDIA GPUs. The dataset of 5,000+ images will be split for training, validation, and testing, and I’ll apply data augmentation to improve detection of individual mussels and clusters. Tracking will use ByteTrack or DeepSORT integration to assign persistent IDs across frames and count mussels accurately. Hyperparameters (learning rate, batch size, image size, augmentation settings) will be documented in a training notebook. Deliverables include: • Final weights & inference script compatible with Ultralytics CLI/PyTorch • Training documentation with all hyperparameters • Performance report: Precision, Recall, mAP, and sample tracking video • README with clear instructions to reproduce results Estimated timeline: 7–10 days from dataset delivery to validated results. Quick question: do you want the inference optimized for GPU only, or also CPU fallback for low-power deployments? ✅ This approach ensures reliable, real-time mussel detection, tracking, and counting with reproducible performance.

@rosmarflores

As an experienced Full Stack Developer, I bring a unique blend of skills to your project. My expertise in Python, specifically using the PyTorch framework, will be paramount for training your custom YOLO model. I have a deep understanding of object detection and tracking algorithms, and I am confident in providing you the flawless final weights and inference script you need. Not only that, my competence extends beyond just the development stage -- I'll ensure to furnish you with a thorough documentation explaining all the hyperparameters used, alongside clear instructions to reproduce the inference and results on your end. Having worked on high-stakes projects throughout my career, I understand the significance of real-time performance. I'm well-placed to deliver a model that simultaneously provides accurate detection of lost mussels while maintaining at least 15 FPS on a standard NVIDIA GPU. Lastly, the quality of my work is rooted in comprehensive testing and transparent communication; thus you can expect more than just diligently annotated code. My robust performance report, complete with key metrics like precision, recall and mAP plus an explanatory video depicting object tracking across frames will instill complete confidence in my deliverables. Let us partner together to not only build a model that counts every mussel accurately but also offers long term stability and performance. After all, what's an exceptional software without flawless deployment?

@GenePauly

Hi, This is Gene from Luxembourg. You need a custom YOLO model trained to detect and track individual mussels and clusters in real-time with stable IDs and accurate counting. I’ll train and fine-tune a YOLOv8/11 model on your dataset, then integrate tracking and optimize it to reliably hit the FPS target while keeping detection accuracy solid. I’ve built similar object detection and tracking pipelines using YOLO with custom datasets, including real-time inference setups and ID tracking across frames. Will your dataset already include bounding box annotations for both “mussel” and “group” classes, or do you need help preparing labels as well? Happy to get this running smoothly for you.

@kabilank80

Hi, I can help you develop a high-performance YOLO-based detection and tracking system for mussel monitoring with real-time capabilities. I have experience working with computer vision models, including YOLO (Ultralytics), object detection, and tracking pipelines. I will train a custom model (YOLOv8 or latest stable version) using your dataset and optimize it to achieve at least 15 FPS on NVIDIA GPU while maintaining high accuracy. For tracking and consistent ID assignment, I will integrate reliable tracking algorithms like ByteTrack or DeepSORT. The system will accurately distinguish between “mussel” and “group” classes, ensuring precise counting and stable tracking across frames. I will also fine-tune hyperparameters to maximize precision, recall, and mAP. Deliverables will include trained weights, a complete inference pipeline, training scripts/notebook, and a detailed performance report with sample output video. I can also provide a quick prototype/demo before final delivery. Looking forward to working with you.

@sayantandey007

I have almost 2 years of experience in Computer Vision Field. I have worked with several detection models like YOLO and I also worked with Tracking algorithms like DeepSORT. I can complete this work with in 4 days only.

@SunRaku78

I have worked with yolo models for lot of projects. Would like to help you and make some money while at it thank you. I am a student pursuing bachelor's degree in computer science and engineering Have made a lot of projects on this

@Je4nVrg

I can train a custom YOLO model for mussel detection and tracking. My experience includes Python, ML pipelines, and computer vision workflows. I will set up the training pipeline, optimize for your specific use case, and deliver a working model with inference scripts. Which YOLO version do you prefer?