Build Custom CNN From Dataset

@AwaisChaudhry

Hi there, I’m Muhammad Awais, ready to build a robust CNN around your dataset. I’ll assess your data format, propose a clean architecture (from scratch or transfer learning), implement reproducible training pipelines in your preferred stack (TensorFlow/Keras or PyTorch), and package everything for easy inference on GPU-enabled setups. You’ll get: preprocessing scripts, training and evaluation code, trained weights, a simple inference script, and a concise hyper-parameter report with hold-out metrics. I’ll keep the code well-documented and modular for reuse, and I’ll align with your feedback loop for any refinements. Here are 8-10 essential questions I’ll use to tailor the approach and ensure the pipeline fits your needs: 1) What’s the exact image size and channel configuration in your dataset, and are there class imbalances we should address? 2) Do you prefer training from scratch or transfer learning, and do you have a baseline model you’d like to beat? 3) What GPU/driver environment will you run on, and do you need containerized deployment (Docker) as part of the package? 4) What evaluation metrics matter most for you (accuracy, F1, AUC, per-class metrics)? 5) Are there memory constraints or latency targets for inference? 6) Should I include data augmentation strategies, and if so, are there domain-specific augmentations to avoid? 7) Is there any regulatory or reproducibility requirement (random seeds, hardware, CUDA version)? 8) What’s your preferred output forma

@AITSoft

Hi there, I will design a robust CNN tailored to your dataset, using a clean, well-commented pipeline in your choice of stack (TensorFlow/Keras or PyTorch). I’ll evaluate whether from-scratch training or transfer learning fits best, set up reproducible preprocessing, model building, and training scripts, and package the model with a simple inference utility for reuse. I will also provide a concise report detailing the final hyper-parameters and hold-out metrics, and ensure the code runs smoothly on a standard GPU setup. Understanding and approach Your goal is a reusable CNN with clear training, evaluation, and deployment steps. I’ll first inspect your data format and create a robust preprocessing and augmentation plan. Then I’ll prototype a few architectures (from a compact baseline to a deeper, more powerful model) and compare transfer learning vs. training from scratch based on validation accuracy and training time. Once chosen, I’ll train, evaluate on your hold-out set, and export the weights, along with a small predict script and a minimal API wrapper for inference. All code will be clean, well-commented, and fully reproducible. Questions for you What are your preferred evaluation metrics and hold-out split strategy for this dataset? Which framework should I use (TensorFlow/Keras vs PyTorch)? Do you have any constraints on model size, latency, or batch size for inference? Should I export a ready-to-run inference script with a tiny API (CLI/REST) or keep it as a si

@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

@umg536

Hello, Having built a formidable reputation in the field of Machine Learning and Deep Learning, my name is Usama and I proudly represent Modular Solutions. We are experts in crafting tailored solutions to meet precise needs and this project aligns perfectly with our skill-set. Proficient in TensorFlow/Keras, PyTorch and other modern deep-learning stacks, we guarantee clean, well-commented code that is fully reproducible. As far as delivering your requirements go, you can expect source code for preprocessing, training and evaluation, trained model weights along with a simple prediction script plus a comprehensive report highlighting the hyper-parameters we have fine-tuned and the metrics we achieved on your hold-out data. Profound experience in Data Analysis uniquely situates us to deliver maximum value from your dataset. Leveraging tools like SPSS, R, Hadoop and Spark, we extract actionable insights from complex datasets - insights that will be invaluable while selecting or designing the most effective architecture for your Custom CNN. Our excellent command in Python combined with CUDA abilities also ensures swift implementation on the standard GPU environment you require. What sets us apart from others is not just our technical proficiency but our unwavering commitment to collaborative partnership. As reiterated by our over 100 satisfied clients, we prioritize transparency throughout the project lifecycle. We believe regular feedb Thanks!

@intelliresponse

With over 13 years of experience working on AI and ML projects, your search ends here! My name is Steven, and I've encountered numerous projects similar to yours where I have developed customized AI and automation solutions; just like you need for your image dataset. My primary language is Python but C++ Programming has also been my strong suit. Through the years, I’ve established myself as a reliable specialist who can provide efficient, effective, and reproducible code. As for your project deliverables of source codes, trained model weights with inference script, as well as a detailed report on hyper-parameters selection and associated metrics - these are all tasks I undertake with meticulousness. Your satisfaction is my priority- so throughout the project timeline, I'll ensure frequent communication and actively seek feedback to refine the pipelines according to any necessary adjustments we may discuss.

@shivampanchal

With over 5 years of experience in the field, I am deeply motivated and ready to tackle your unique project head-on. I have dexterously wielded tools such as TensorFlow, Keras, and PyTorch to build and fine-tune complex CNN models for bespoke datasets just like yours. My work often involves coaxing the most out of limited or irregular data— resulting in robust, reliable, and optimized solutions that stand the test of time. Comprehending your need for a clean, well-documented, reproducible codebase with competitive results, I assure you of my attention to detail during every step of the project. To deliver upon your expectation, I will not only provide a well-performing model but also the complete source code for pre-processing, training, evaluation as well as any necessary dataset refinements. Furthermore, my commitment extends not just till project completion but to enabling your self-sufficiency. In addition to deploying a ready-to-use system for inference using your trained model on a GPU environment plus a report upholding hyper-parameters used and performance metrics achieved meticulously on your hold-out data, I will provide comprehensive support and answer all your queries post-contract.

@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

@kamran2012

Interesting project, I will design and train your CNN — covering data preprocessing, architecture selection, training pipeline, and a clean inference script with exported weights. Everything will be well-commented and reproducible in a standard GPU environment. Before jumping into a custom architecture, I will benchmark your dataset against a few pretrained backbones — ResNet, EfficientNet — using frozen feature extraction first. This reveals whether transfer learning gives you a strong baseline or whether a purpose-built architecture will outperform, saving significant training time and compute. Questions: 1) How many classes does your dataset have, and roughly how many images per class? 2) What is your target deployment — local GPU server, cloud endpoint, or edge device? Send me a message and we can go over the details. Best regards, Kamran

@Herculesz

Hi I can build a complete CNN training and inference pipeline around your image dataset using PyTorch or TensorFlow/Keras with clean, reproducible, and well-documented code. The key technical challenge is selecting the right architecture and preprocessing strategy so the model generalizes well on hold-out data instead of only fitting the training images. I can handle dataset inspection, image resizing/normalization, augmentation, train/validation/test splitting, transfer learning or custom CNN design, model training, evaluation, and metric reporting. For stronger results, I would compare suitable pretrained backbones such as ResNet, EfficientNet, or MobileNet against a lighter custom CNN depending on dataset size, class balance, and deployment needs. The final package will include preprocessing scripts, training code, saved model weights, an inference script, and a concise report covering hyperparameters, achieved metrics, and any dataset issues found. I will keep the project structured so it can run smoothly on a standard GPU environment and be reused later for prediction without retraining. Thanks, Hercules

@devendrathakur12

i’ve done very similar recently, built CNN pipelines in PyTorch with transfer learning and clean reproducible training + inference setup. What is your dataset size and class distribution, and do you already have a train/val/test split? What GPU/VRAM will you use so I can tune batch size and model choice? I suggest starting with transfer learning (EfficientNet/ResNet) and fine-tuning; it converges faster and gives better generalization. I also suggest adding strong augmentations and early stopping; this reduces overfitting and stabilizes metrics. I’ll set up preprocessing and splits, then train and tune the model with tracked experiments. Next I’ll export weights and build a simple inference script. Finally, I’ll deliver metrics, configs, and a clean reproducible repo. Best, Dev S.

@hoangnn90

✅Full Experience in Training and Prediction Model with Python Programming(PyTorch/Tensorflow)✅. ✳️I am very confident that complete your project perfectly. ✳️I can guarantee the quality of the job and deliver the result on time. I hope we will discuss in more detail via chat. Best regards!

@DigitalLogics

Hi there, I’ll turn your dataset into a clean, reproducible CNN pipeline that’s actually usable beyond training. I’ll start by validating your data and setting up a consistent preprocessing flow. Then I’ll choose the right approach based on your dataset, either a custom CNN or transfer learning with models like ResNet or EfficientNet to get strong performance quickly. Training will be modular and GPU-ready with proper augmentation, checkpointing, and evaluation on hold-out data. I’ll tune hyperparameters based on results, not guesses. You’ll get full code for preprocessing, training, and evaluation, along with trained weights and a simple inference script you can reuse. If you want a model that’s reliable and ready to use, I can start immediately. Best; Zaman

@juanjosec63

Hi, I have strong experience building CNN models in PyTorch and TensorFlow, including dataset preprocessing, transfer learning, and GPU-optimized training pipelines. For this project, I can design the right CNN architecture (from scratch or using models like ResNet/EfficientNet), train and tune it on your dataset, and deliver a clean, reproducible pipeline with preprocessing, training, and evaluation code. I will also provide trained weights, a simple inference script, and a concise report with hyperparameters and performance metrics so you can reuse the model easily. Best regards, Juan

@nrajuu07

As a seasoned Machine Learning Engineer with a strong background in medical image analysis and deep learning, I have deep experience in creating and deploying robust Convolutional Neural Networks just like the one you're looking for. My deep learning work spans the use of PyTorch, TensorFlow, and OpenCV for varied purposes like image classification, semantic segmentation, object detection, among others. In fact, I've extensively incorporated well-known CNN architectures such as YOLOv8, UNet ViT, EfficientNet, ResNet, and Mask RCNN - proving my mettle to deliver scalable ML solutions for diverse domains. I'm particularly excited about your project owing to my previous endeavor in similar domains handling large scale datasets such as MRI, CT scans and whole slide images. This has given me a thorough understanding of preprocessing challenges that could arise and how to effectively address them while maximizing efficiency without compromising quality. Let's collaborate on your project and unlock its untapped potential!

@snbhanja

Hi, I have worked on mnay CNN usecases using all kinds of transfer learning and base models like VGG, Resnet etc. I used keras and pytorch based on preference. My code will be modular, easy to understand and implement. I will share the training code, inference cide and report regarding hyperparameters/metrics with plots. Lets connect.

@avinashdubeyg209

Hi there, I can help you build a custom CNN using TensorFlow/Keras, ensuring a high-quality model that meets your needs. With experience in machine learning, I'll design an architecture tailored to your dataset, applying either from-scratch training or transfer learning for efficiency. I'll provide you with all the necessary source code for preprocessing, training, and evaluation, plus a straightforward inference script. I guarantee clean and well-commented code so you can easily replicate or modify the process. Let's create something great together. Looking forward to discussing more!

@edulov

Hello. I've made it several times (not sure that >15 is many). Both on existing architecture or FULLY CUSTOM. In several occasions - for low resolution and low quality images or heavily unbalanced data sets. If you have real interest, we have to talk first, since I am working with "fresh" platform clients w/o advance payment. Regards,

@rosscarter1011

Hi there, Building a Convolutional Neural Network demands precision, especially when it comes to architecture selection and training methodologies. A common issue faced is choosing the optimal architecture and ensuring the model generalizes well on your dataset. I will guide you in designing or selecting the right architecture, and expertly handle training using either a from-scratch approach or transfer learning, ensuring your model is robust and ready for inference. Here are my questions: Could you specify the size and nature of your dataset? Also, do you have any preferred metrics or benchmarks you aim to achieve? Let’s discuss your project now!

@toriquldev123

Hello Dear! Greetings from Toriqul Global Solutions! We are a reliable and experienced web design & development company led by Engineer Toriqul Islam (B.Sc. in CSE, RUET), with over 10 years of proven industry experience delivering quality digital solutions. At Toriqul Global Solutions, we build modern, user-friendly, high-performance websites focused on simplicity, elegance, and functionality to boost engagement and business growth. I have some questions——— 1. Image count and class distribution? 2. Need classification, detection, or segmentation? Technologies We Use: Custom Websites Development Using ======>Full Stack Development. 1. HTML5 2. CSS3 3. Bootstrap4 4. jQuery 5. JavaScript 6. AngularJS 7. React JS 8. Node.js 9. WordPress 10. PHP 11. Ruby on Rails 12. MYSQL 13. Laravel 14. .NET 15. CodeIgniter 16. React Native 17. SQL / MySQL 18. Mobile app development 19. Python 20. MongoDB and more skills What will you get? • Responsive design on All Devices •Reusable components •Clean code •Timely delivery tested •Clear communication We would be honored to discuss your project requirements and help bring your ideas to life. Thank you for your time and consideration. Warm Regards, Toriqul Global Solutions