Enhancing Object Detection Framework (MMDetection/YOLOv3)

@AwaisChaudhry

Hello, I understand that your project aims to enhance the object detection framework by implementing a dual-branch architecture within MMDetection/YOLOv3, targeting an F1 score of at least 0.85. My approach involves using PyTorch to integrate RGB and frequency-domain branches effectively, creating a robust fusion module for feature combination. I will train and evaluate the model on the Tampered_IC13 dataset, ensuring to meet the F1 score requirement and optimize the model's performance through thorough experiments and adjustments. I will deliver trained model weights, detailed evaluation metrics, and an integration guide to help reproduce the results. What specific performance metrics or benchmarks do you have in mind for model evaluation, apart from the F1 score? Thanks, Muhammad Awais

@Mahihassi

As a seasoned Electrical Engineer with a profound understanding of computer vision, including Python, PyTorch, and MMDetection/YOLOv3, I am confident in my capacity to take on your sophisticated project. My track record in image processing and anomaly detection, combined with my master's degree in Embedded Systems and proficiency in machine/deep learning is the perfect groundwork for this project. I have extensive experience with microcontrollers, FPGAs (Verilog/VHDL), Artificial Intelligence, and Robotics. This diverse background empowers me to think outside the box when confronting complex challenges like developing an improved object detection framework. My robust code writing skills ensure accuracy and replicability. Moreover, my adeptness at optimizing embedded software/hardware pairs beautifully with your project requirements for frequency-domain image processing using tools like Fast Fourier Transform (FFT) or Discrete Cosine Transform (DCT). If chosen, you can anticipate detailed documentation of every progress from training to evaluation logs and techniques to reproduce results efficiently. Let's team up; together we can boost your project performance beyond the baseline!

@Fazeennazar

With over 7 years of experience in software engineering, I, Mohamed, have honed my skills in Python, PyTorch, MMDetection, and YOLOv3 which make me the perfect fit for your object detection project. I specialize not only in enhancing object detection frameworks but also in developing anomaly detection models fueled by computer vision. These skills will be pivotal in executing a successful dual branch scene text detection model for you. As we take a deep dive into this project, I guarantee to produce beyond your expectations. My expertise in both image processing and anomaly detection will seamlessly merge with your requirements. You additionally seek robust Frequency-Domain Imaging skill sets - my familiarity with FFT and DCT elevates me further as a potential candidate to exceed the baseline performance you anticipate. Lastly, I'm committed to punctuality and high-quality output within budgetary constraints. In line with this, I offer detailed training and evaluation logs as well as implementing a fusion module for comprehensive feature combination between branches. To ensure proper transitions and knowledge integration after our cooperation ends, I'll also provide you with an inclusive integration/usage guide to replicate the results we attain together. Let's forge this powerful relationship toward providing the best solution for your project!

@ZohaibRoy

⭐⭐⭐⭐⭐ Develop a Dual-Branch Scene Text Detection Model Efficiently ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for a dual-branch scene text detection model. No need to look any further; Zohaib is here to help you! My team has completed 50+ similar projects in this area. I will implement the dual-branch architecture using RGB and frequency-domain branches within the MMDetection/YOLOv3 framework. I’ll also develop a fusion module for combining features and train the model on the Tampered_IC13 dataset to meet the F1 score target of at least 0.85. ➡️ Why Me? I can easily develop your dual-branch model as I have 5 years of experience in image processing and anomaly detection using PyTorch, MMDetection, and YOLOv3. My skills also include frequency-domain processing techniques, ensuring I can optimize performance effectively. ➡️ Let's have a quick chat to discuss your project in detail and I can show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ PyTorch ✅ MMDetection ✅ YOLOv3 ✅ Image Processing ✅ Anomaly Detection ✅ Frequency-Domain Processing ✅ FFT ✅ DCT ✅ Model Training ✅ Performance Optimization ✅ Evaluation Metrics ✅ Integration Guidelines Waiting for your response! Best Regards, Zohaib

@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

@AITSoft

Hello, I understand that you want to enhance your object detection framework by introducing a dual-branch scene text detection model using MMDetection and YOLOv3. The goal is to seamlessly integrate both RGB and frequency-domain features to achieve an F1 score of at least 0.85. My approach will involve implementing the dual-branch architecture, developing a fusion module for feature combination, and thoroughly training and evaluating the model on the Tampered_IC13 dataset to ensure we meet or exceed your performance targets. I have the necessary skills in PyTorch and image processing and am proficient in evaluating metrics such as Precision, Recall, and F1. I will ensure that you receive trained model weights, comprehensive logs, and a guide for integration and usage so that the results can be reproduced easily. What specific datasets do you want to include for evaluation aside from the Tampered_IC13? Thanks, Shamshad

@divumanocha

Hello, I am really excited about the opportunity to collaborate with you on this project! It aligns perfectly with my skill set and experience, and I’m confident I can contribute meaningfully to your vision. I genuinely enjoy working on projects like this, and I believe we can create something both functional and visually engaging. Please feel free to check out my profile to learn more about my past work and client feedback. I’d love to connect and discuss the project details further your goals, expectations, and any specific features or ideas you have in mind. The more I understand your vision, the better I can bring it to life. I am ready to get started right away and will put my full energy and focus into delivering quality results on time. My goal is not just to complete the project, but to exceed your expectations and build a long-term working relationship. Looking forward to hearing from you soon! With regards! Divya

@softsolution2000

Hi there I propose to develop a dual-branch scene text detection model within the MMDetection/YOLOv3 framework. I will implement the RGB and frequency-domain branches, create a fusion module for feature combination, and train the model on the Tampered_IC13 dataset to achieve a target F1 score of ≥ 0.85. I have expertise in PyTorch, MMDetection, YOLOv3, and frequency-domain image processing (FFT, DCT). My experience in image processing and anomaly detection will ensure the success of this project. I will deliver trained model weights, training logs, evaluation metrics, integration guide, and aim to exceed baseline performance through optimization and experiments. Please go through my profile its 15 years old see the work I did over the years. ---> No Win No Fee means that your satisfaction is my utmost priority. <---- Lets discuss the job details. Moreover, I am willing to start the job and perform tasks without even being hired; it is just to show my commitment to this project. Looking forward to hear from you. Regards Shah

@TruongPT

Hi, I’ll implement a dual-branch scene text detector inside MMDetection/YOLOv3 with an RGB branch and a frequency-domain branch (FFT/DCT), fuse features via a lightweight attention-aware module, and train end-to-end with custom augmentation and loss tweaks to target ≥0.85 F1 on Tampered_IC13. I’ll deliver trained .pth weights, full training/eval logs, precision/recall/F1 reports, and a clear reproduction guide showing how to run training and inference. My plan includes dataset preprocessing (frequency maps), careful fusion timing (multi-scale), and ablations to find the best fusion point and loss balancing. Potential issues: limited dataset size and domain shift may require strong augmentation or transfer learning, frequency artifacts may hurt localization if not normalized, and training will need GPU time for stable results — I’ll document mitigation steps and hyperparameter choices. I’m highly reliable with a 100% job success score and will keep the code clean and reproducible so you can integrate or extend it easily; thanks, Truong Pham

@koolvishruth

Hello Sir, I am Python developer with 7 years of experience in Yolo, Object detection, Machine learning engineering.I have seen attached zip code I can get this done. Let's connect

@Herculesz

Hi, I can extend your MMDetection/YOLOv3 pipeline with a dual-branch architecture—one branch for RGB features, the other for frequency-domain features (FFT/DCT)—and design a fusion module to combine them effectively. I have experience modifying MMDetection backbones and necks to add parallel feature extractors, normalizing and aligning multi-domain feature maps, and integrating them into YOLO heads for improved text detection in complex scenes. I’ll preprocess the Tampered_IC13 dataset to include both spatial and frequency representations, ensuring consistent augmentation across branches. Training will include careful hyperparameter tuning, loss balancing, and checkpoint monitoring to target F1 ≥ 0.85. Deliverables will include .pth weights, full training/evaluation logs, detailed metrics, and a reproducible guide for integration and retraining. I’ve implemented similar multi-branch architectures for anomaly and forgery detection with measurable performance gains. Thanks, Hercules

@DigitalLogics

Hi Kim0031, If you want to push MMDetection/YOLOv3 to an F1 score ≥ 0.85, I can help by implementing a dual-branch RGB + frequency-domain architecture with an efficient fusion module. I’ll handle training on Tampered_IC13, optimize precision/recall, and provide full deliverables trained weights, logs, metrics, and a clear integration guide. I’ve worked extensively with PyTorch, MMDetection, YOLOv3, and FFT/DCT-based image processing, so I understand how to blend spatial and frequency features for better scene text detection. Quick question: should the RGB and frequency branches share a backbone or remain separate for maximum accuracy? Ready to start and share an initial architecture plan within days. Best

@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.

@ashrafsbai6655

Hi there, I checked your requirements and guarantee you that i have relevant experience in Python,AI and data science it's gonna be done within the highest quality . Let's contact via chat so that I can start work immediately

@hayat38402

Hi, how are you doing? I went through your project description and I can help you in your project. We are a team of expert engineers, we have successfully completed 1000+ Projects for multiple regular clients from OMAN, UK, USA, Australia, Canada, France, Germany, Lebanon and many other countries. We are providing our services in following areas:  Neural Network/ Natural Language Processing  Machine learning/Data Mining  Deep Learning and Computer Vision  Image Recognition & Artificial Intelligence  AI text analysis model and Reinforcement Learning.  Omnet++ and Sumo simulation,  Python/ Matlab  Asterisks PBX  NS3 simulation  Linux We'll make sure that your project is done in a perfect way and do our best until you were satisfied. I am confident I can provide you with top-notch materials that will fit your needs.

@abhisaini0188

Hello, Thank you so much for posting this opportunity it sounds like a great fit, and I’d love to be part of it! I’ve worked on similar projects before, and I’m confident I can bring real value to your project. I’m passionate about what I do and always aim to deliver work that’s not only high-quality but also makes things easier and smoother for my clients. Feel free to take a quick look at my profile to see some of the work I’ve done in the past. If it feels like a good match, I’d be happy to chat further about your project and how I can help bring it to life. I’m available to get started right away and will give this project my full attention from day one. Let’s connect and see how we can make this a success together! Looking forward to hearing from you soon. With Regards! Abhishek Saini

@Mobilehead

Hello, I once faced a challenge integrating dual-branch architectures in object detection that complicated feature fusion, but I found a streamlined solution. I understand you're looking to enhance the YOLOv3 framework with RGB and frequency-domain branches. My plan includes developing an effective fusion module to boost performance and meet your target F1 score of 0.85. I’m skilled in PyTorch, MMDetection, and frequency-domain processing. I can deliver the trained model weights, logs, and a clear guide for reproducing results efficiently.

@dfordesigners

Hello Kim0031, I reviewed your project, "Enhancing Object Detection Framework (MMDetection/YOLOv3)," and it’s an excellent match for my expertise. With 500+ completed projects and a 4.9⭐ Top-Rated profile, I deliver high-performance websites, custom applications, and creative digital solutions that are secure, fast, and visually outstanding. My skill set includes ✅ Web Development: WordPress, Shopify, WooCommerce, Laravel, PHP, React.js, Vue.js, Python ✅ Databases: MongoDB, MySQL, PostgreSQL ✅ E-commerce: Store setup, product optimization, custom checkout flows, payment gateways (PayPal, Stripe, Square) ✅ UI/UX Design: Pixel-perfect, responsive layouts, Figma prototypes, branding assets ✅ SEO & Marketing: On-page SEO, technical optimization, speed enhancement ✅ Integrations: APIs, CRMs, analytics, automation tools ✅ Maintenance: Bug fixing, security hardening, backups, and ongoing support Why work with me: ✔️ 95% completion rate 400+ 5-star reviews ✔️ 1-month free support after delivery ✔️ FREE basic SEO & security setup ✔️ US time zone availability for real-time collaboration If you’d like, I can create a quick tailored action plan for your project so you know exactly how I’ll get it done—before we even start. Let’s make your project a success. Kausar Parveen Multimedia Graphic Designer & Full-Stack Developer Your Partner in Digital Excellence

@va01v

Hello Kim, I am excited about the opportunity to enhance the existing object detection framework with a dual-branch architecture using MMDetection/YOLOv3. With my expertise in image processing and experience in developing advanced models, I am confident in achieving the target F1 score of at least 0.85. My approach will involve implementing the dual-branch architecture effectively, ensuring the integration of RGB and frequency-domain branches. I will also develop a robust fusion module to optimize feature combination. Training and evaluation on the Tampered_IC13 dataset will be conducted meticulously, and I will provide detailed evaluation metrics to track progress. I look forward to delivering trained model weights, comprehensive training logs, and an integration guide to facilitate reproducibility. My goal is not only to meet but to exceed baseline performance through rigorous optimization and experimentation. Thanks, ALESSIO