Custom Segmentation Loss Design

@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

@Herculesz

Hi, I can help you design custom loss functions tailored for pixel-accurate segmentation in your transformer-based architecture. Here's how I would approach the project: 1. Bespoke Loss Modules: I will develop one or more PyTorch (or JAX) loss functions specifically designed to address fine-grained boundaries and class imbalance. These could be based on advanced techniques like boundary-aware losses (e.g., using edge maps for boundary accuracy) or focal loss to handle class imbalance more effectively. 2. Test Script: I’ll provide a short, simple test script to plug the custom loss into a dummy forward pass. This will help you confirm gradient flow and ensure that the loss is numerically stable. 3. Hyperparameters & Preprocessing: I will outline recommendations for hyperparameters, such as learning rate, batch size, and weighting for class imbalance. Additionally, I’ll offer preprocessing advice that can improve the performance of the loss function, such as normalizing pixel values and augmentation techniques to handle boundaries more effectively. 4. Future Classification Extension: While the immediate deliverable focuses on segmentation, I’ll also provide ideas on how the same design can be adapted for classification tasks in the future, should you need to expand the pipeline. Thanks, Hercules

@Muhammadzeesha59

Hello Sir/MAM I am a skilled full stack developer. Having rich experience in Java , C++ , C , C# , Python , Eclipse , Sql , Mysql , .Net ,Oracle , Object Oriented Programming , Data Structure , Algorithms . I have a perfect grip on “Artificial Intelligence” “Automation” , and work in “Machine Learning” Deep Learning ”. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thanks and Best Regards

@GdevDataSceince

Hi there,I am pleased to present my proposal for your machine learning project, bringing a rigorous, data-driven approach and a commitment to delivering measurable impact. With expertise in Python, TensorFlow, and Scikit-learn, I design robust pipelines—from advanced data preprocessing and feature engineering to model architecture, training, and fine-tuning. I focus on interpretability, scalability, and performance optimization to ensure real-world applicability. My workflow emphasizes precision, continuous evaluation, and clear communication. I am dedicated to transforming complex data into intelligent, high-value solutions tailored to your objectives. https://www.freelancer.com/u/GdevDataSceince Let's discuss this further via chat, and I'll start your project right now. Thanks Gdev

@sf264

Hello, I have gone through your project description and understand your requirements. I delivered a similar project last week with a 5-star review and would love to show that in private. Message me and let's talk more about your project and I will share my approach today. Cheers, Fahad.

@AhmadXpert2410

Hi there, I understand you need custom, segmentation-focused loss functions that go beyond standard Dice or Cross-Entropy to better capture fine boundaries and class imbalance in a transformer-based vision pipeline. I can design PyTorch loss modules combining techniques like boundary-aware losses (e.g., level-set or contour-based terms), focal/Tversky adaptations for imbalance, and hybrid formulations that align well with transformer outputs for pixel-accurate segmentation. My approach will ensure the losses are modular, numerically stable, and easy to plug into your existing training loop. I will include a concise test script with a dummy forward pass to validate gradient flow and stability, along with clear inline comments and a markdown explanation covering the mathematical intuition and when to tune specific components for optimal performance. You will receive clean, production-ready code, guidance on hyperparameters and preprocessing (e.g., normalization, augmentation strategies), and optional notes on how the same design can extend to classification tasks. The goal is to give you a loss framework that meaningfully improves segmentation quality without complicating your pipeline. Regards, Ahmad

@MZITSERVICES

Hi there, I am A.R.M. MASUD, with a strong Data Science background.I am an experienced Machine Learning developer with expertise in designing, training, and deploying intelligent models that deliver real-world value. My background includes supervised and unsupervised learning, deep learning with TensorFlow and PyTorch, and data preprocessing using Pandas, NumPy, and Scikit-learn. I specialize in developing classification, regression, clustering, and predictive models, as well as computer vision and NLP solutions. I follow best practices in feature engineering, hyperparameter tuning, and model evaluation to ensure high accuracy and scalability. My focus is building end-to-end ML pipelines that are efficient, reliable, and tailored to your project’s requirements for maximum impact. https://www.freelancer.com/u/MZITSERVICES I appreciate the opportunity to submit this proposal and am excited about the possibility of working with you to bring your project to life. Thanks A.R.M MASUD

@seandinwiddie

✨✨✨ ✨✨✨ ✨✨✨ ✨✨✨✨✨✨ ✨✨✨ ✨✨✨ ✨✨✨✨✨ Hi, Dear. Portfolio : https://www.freelancer.com/u/seandinwiddie I’m confident I can help you design and implement advanced segmentation loss functions tailored for transformer-based vision models in PyTorch, with a strong focus on boundary precision, class imbalance handling, and stable gradient flow. I specialize in deep learning systems for segmentation pipelines, including custom loss engineering beyond standard Dice/Cross-Entropy setups. I will start by implementing a composite loss module combining boundary-aware supervision (e.g., differentiable edge loss via Sobel/Laplacian feature maps), focal-based weighting for class imbalance, and a transformer-friendly soft IoU variant to improve pixel-level alignment. Next, I will provide a clean PyTorch implementation with modular components so you can easily plug it into your existing segmentation transformer pipeline (e.g., SegFormer/ViT-based decoders). Finally, I will include a minimal test harness script that runs a dummy forward/backward pass to verify gradient stability, numerical safety, and expected convergence behavior, along with recommended hyperparameters and preprocessing tips (class reweighting, label smoothing, resolution scaling strategies). Looking forward to collaborating. Best Regards. Sean D. ✅

@jay8mac

This is exactly the kind of work I love doing, and I'm currently offering premium quality at a reduced rate while building my reputation — meaning you get full dedication without the full price tag. I've studied the specifics of your project and understand your need for segmentation loss designs that suit a transformer-based architecture for fine-grained boundaries and handle class imbalances. This bespoke solution is key for boosting model effectiveness. I have extensive experience in artificial intelligence and computer vision. My skills align perfectly with your requirements for delivering well-documented PyTorch or JAX loss modules for segmentation, inclusive of validation code. Looking forward to potentially collaborating closely to enhance your pipeline. Regards, Jason McLachlan

@adily1

Hey , I just finished reading the job description and I see you are looking for someone experienced in Machine Learning (ML), Computer Vision, Image Processing, Deep Learning, AI Development, Artificial Intelligence and Documentation. 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? 1. I have done more than 250 major projects. 2. I have not received a single bad feedback since the last 5-6 years. 3. You will find 5 star feedback on the last 100+ major projects which shows my clients are happy with my work. Timings: 9am - 9pm Eastern Time (I work as a full time freelancer) 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, Adil.

@engrnaib

Hi, I can design custom segmentation loss functions tailored for transformer-based models, focusing on boundary precision and class imbalance beyond standard Dice/Cross-Entropy. ✔ PyTorch loss modules (clean, well-documented) ✔ Hybrid approach (e.g., Boundary-aware + Focal/Tversky variants) ✔ Dummy test script with gradient checks & stability validation ✔ Clear markdown explaining math + tuning guidelines I have experience working with vision pipelines and custom loss design for segmentation tasks. ⏱️ Delivery: 3–5 days Ready to start immediately.

@rogelitoc

Hello, In my opinion, the problem of this project is that crafting custom loss functions for transformer-based segmentation requires precision in handling class imbalance and pixel-level accuracy. I will implement tailored PyTorch loss modules focusing on pixel accuracy, leveraging existing Dice and Cross-Entropy as a base while introducing enhancements for boundary detection. The architecture will ensure consistent data flow through gradient calculations, addressing edge cases such as sparse classes. I will reuse robust components from standard loss functions without altering their fundamental logic. The deliverables will include well-documented loss modules, a test script for gradient verification, and a markdown overview of the mathematical foundations of the approach. With extensive experience in developing advanced loss functions, I can ensure high-quality outputs. I'd love to discuss in more detail. Regards.

@PetrusLubbe

‼️ONLY PAY WHEN YOU'RE 100% HAPPY‼️ You need custom loss functions that go beyond Dice or Cross-Entropy to handle fine-grained boundaries and class imbalance in transformer-based segmentation. I’ll create clear, well-documented PyTorch loss modules tailored to your architecture, with a concise test script ensuring gradient flow and numerical stability. I’ll also provide practical hyper-parameter guidance and preprocessing tips to maximize performance. While I’m new to Freelancer, I’ve built similar segmentation losses off-platform that improved pixel accuracy significantly. Let’s chat! Worst case, you get a free consultation and real insight. Regards Pietie Lubbe.

@mayr81

Hello, I’d love to support your work on creating custom segmentation loss functions for your transformer-based vision pipeline. I have extensive experience developing bespoke PyTorch modules and can design a loss that handles pixel-level precision, boundary sensitivity, and class imbalance with clean, well-commented code. I can also provide a simple gradient-flow test script along with guidance on hyper-parameters and preprocessing so the loss performs reliably in your setup. If needed, I can outline how this approach could be extended to classification without distracting from the segmentation milestone. I’m confident my background in computer vision and deep learning lets me deliver a stable and readable implementation.

@Maxxionn

Hi , I’ve carefully reviewed your job post and it’s clear you’re looking for someone with solid experience in Machine Learning (ML), AI Development, Image Processing, Documentation, Artificial Intelligence, Computer Vision and Deep Learning. This is exactly within my core expertise, and I’m confident I can deliver reliable, high-quality results. Rather than rushing into assumptions, I prefer to understand the project properly. I’d appreciate your clarification on a few points: Is the job description complete, or are there additional requirements or expectations? Do you already have any work completed, or will this be built entirely from scratch? Do you have a preferred timeline or deadline in mind? Why you can confidently work with me: Successfully completed 250+ major projects across different industries Maintained 100% positive feedback over the last 5–6 years Earned 100+ recent 5-star reviews, showing long-term client satisfaction I focus on clear communication, clean execution, and on-time delivery I work as a full-time freelancer and am available 9 AM – 9 PM (Eastern Time), ensuring fast responses and consistent progress. Due to client confidentiality, I share relevant work samples only in private chat. Let’s start a conversation so I can show you similar work and suggest the best approach for your project. Looking forward to working with you. Best regards, Arsalan Khan

@matheussilva114

Hi, I have read your description and I fully understand your needs. I am a senior engineer with over 7 year of experience on Machine Learning (ML), Artificial Intelligence, Image Processing, Documentation, Computer Vision, Deep Learning, AI Development. Please visit my profile to view my latest projects, certificates, and work history. Best, Matheus Regards, Matheus

@toqeer74

Creating loss functions that effectively address class imbalance and fine-grained boundaries requires a meticulous approach to formulation. Leveraging PyTorch, I can develop tailored loss modules that not only enhance pixel-accurate segmentation but also include techniques like Focal Loss for class imbalance and boundary-aware gradient methods to refine context at the edges. Alongside well-documented code, I will provide a concise test script to validate the gradient flow, ensuring numerical stability. Hyper-parameter suggestions will be included, alongside preprocessing tweaks to maximize performance. I can deliver your initial requirements within 10 days. Can we hop on a 10-minute call this week?

@hammadfarooq1122

As an AI and ML expert with a deep understanding of PyTorch, I am excited about the opportunity to design and implement tailored, sophisticated loss functions for your segmentation task. I fully appreciate the complexity of your vision problem - combining transformers with pixel-accurate segmentation tasks - and I am confident in my ability to meet these unique challenges head-on. My experience extends beyond generic Dice or Cross-Entropy formulations, allowing me to bring innovative, custom solutions that specifically address your requirement at hand. Moreover, the deliverables that you need will be handled with meticulous attention to details. My code is not just functional but comprehensible and transparent as well. You can expect clean, readable code complete with inline comments and a brief markdown on the math behind your customized losses. Finally, I'd like to emphasize my adaptability and future-oriented approach. Whilst the immediate priority is a solution for pixel-accurate segmentation, I believe in building adaptable systems. With this project specifically, I envision how this bespoke solution could potentially be fashioned to tackle classification tasks in the future. In other words, you get not just what you need right now but someone who thinks ahead for your entire project. Give me this opportunity and together we'll produce results that exceed expectations.

@kamran2012

Hi, I will deliver custom PyTorch loss modules combining boundary-aware terms with adaptive class weighting — plus a test script verifying gradient flow and numerical stability across edge cases. One approach worth exploring: a composite loss pairing a distance-transform-weighted boundary term with a focal Tversky component. This lets you tune precision-recall tradeoff per class while penalizing boundary errors more heavily — especially effective with transformer patch artifacts. Questions: 1) How severe is the class imbalance — ratio of smallest to largest class? 2) Are you using a ViT-based decoder or something like SegFormer? Ready to start whenever you are. Kamran