Improve YOLO26 Crate Detection

@Herculesz

Hi, the core issue here is not just YOLO26 tuning; it is whether the current labels and scene coverage support a 94% hold-out result from roughly 100 images. The real engineering risk is dataset quality and distribution shift, because augmentation can improve robustness but it will not fix inconsistent boxes, class ambiguity, or a weak validation split. I’ve built production ML systems where the fastest gains came from tightening evaluation, isolating failure modes, and making the training path reproducible in Python rather than chasing architecture changes too early. The closest relevant work in my background is AI-Driven Marketing Suite Development -- 2 for applied computer vision delivery, plus Python Bug Localization Using Transformer Models (CodeBERT + TreeBERT) for disciplined training, held-out evaluation, and clean handoff. I usually structure this as three passes: audit labels and split quality, benchmark targeted augmentations against the current baseline, then optimize inference to stay within the RTX 3060 latency target. I’d also separate accuracy validation from pruning or quantization so speed work does not hide regression. If useful, I can start by reviewing the dataset and outlining the highest-probability failure points before retraining. Thanks, Hercules

@GdevDataSceince

Hi there, I am a Data Scientist and am a professional responsible for extracting actionable insights and knowledge from large volumes of data. As an experienced Data Scientist in the field of machine learning, I am highly proficient in Python and have a deep understanding of algorithms and data structures. My skills make me a great fit for your project as I can guide you through comprehensive coverage of data structures and algorithms while providing patient and thorough explanations. I have over 12-plus years of experience with Python Library Pandas, Karas, TensorFlow, NumPy, PyCharm, Py torch, Open CV, NLP, and others. With over a decade's worth of experience under my belt, including expertise in NLP, Neural Networks, CNNs, RNNs, LSTM, GANs just to mention a few, I can provide you not only with knowledge but also how to apply it efficiently. Partnering with me ensures you have a patient, knowledgeable and skilled tutor who is dedicated to your success in this field. My top priority is to provide a high quality of work, https://www.freelancer.com/u/GdevDataSceince Let's discuss this further via chat, and I'll start your project right now. Thanks Gdev

@intelliresponse

With over a decade of experience in developing high-performing AI solutions like the one you require, I believe I am the ideal candidate for your project. My background in deep learning, image processing, and machine learning (ML) will be invaluable in improving the reliability of your YOLO26 crate detection model. Over my career, I have developed several models that boast high levels of accuracy even with constrained resources. For instance, I recently tackled an OCR problem similar to yours using bank statements and achieved remarkable results. Moreover, my ability to create concise documentation will help you continue to train the model effectively on your hardware. With every completed project comes improved knowledge and skillset; thus, you can trust me to deliver clean code with sustainability in mind. Together, we can utilize these skills to train a YOLO26 variant of your choice to achieve ≥94 % mAP or equivalent-class level accuracy on your hold-out set—with inference times comfortably within your real-time requirements on RTX 3060 or similar hardware specifications. Let's create something impactful together!

@sf264

Hello, I will push the performance of your current YOLO 26 model past 94 % without relying on power hungry cloud instances. Message me at your earliest convenience to discuss more details. Let's get started, Fahad.

@khuzaima024

Hi, I can improve your YOLO model using data augmentation, preprocessing, and training optimization to significantly boost detection accuracy on your crate dataset. I have experience fine-tuning YOLO models on small datasets and will deliver optimized weights, training code, and clear documentation for local deployment. Can you share your current YOLO version and a few sample images so I can evaluate the best approach?

@usmanbashir12100

Hi, This is exactly the kind of optimization project I enjoy. Rather than simply retraining YOLO, I would start by analyzing the dataset quality, annotation consistency, class imbalance, and failure cases causing the low Eurobox and bread-crate detection rates. With only ~100 images containing thousands of objects, there is usually significant room for improvement through targeted augmentation, tiling/slicing, anchor optimization, hyperparameter tuning, and model selection before collecting additional data. My goal would be to push detection performance beyond your 94% target while maintaining real-time inference on local hardware (RTX 3060-class GPU). I'll provide the optimized model weights, reproducible training pipeline, validation results, and clear documentation so you can continue training independently. I'd be interested in reviewing a sample of the dataset and current evaluation metrics to estimate the fastest path to the target accuracy. Muhammad Usman

@MZITSERVICES

Hi there, I am A.R.M. MASUD, with a strong Data Science background. As a Python developer, I have extensive experience building robust, scalable, and efficient solutions that address various business needs. I understand the importance of delivering high-quality, well-architected code, and I am committed to working closely with you to ensure the success of this project. I implement core functionality using Python, utilizing relevant libraries and frameworks such as Pandas, NumPy, GUI, SciPy, Matplotlib, Seaborn, Plotly, Scikit-learn, TensorFlow, Keras, PyTorch, spaCy, Flask, Django, FastAPI, OpenCV, and Jupyter. I am a professional responsible for extracting actionable insights and knowledge from large volumes of data through Machine Learning models, including CNNs, RNNs, LSTMs, GANs, Transformers, FNNs, ANNs, and DNNs. I conduct comprehensive unit, integration, and performance testing to ensure the solution is error-free and optimized. 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

@lalitdeveloper7

Hi, I can optimize your YOLO model to improve Eurobox and bread-crate detection using techniques such as data augmentation, preprocessing, hyperparameter tuning, model optimization, pruning, quantization, and lightweight YOLO variants. I have experience with Python, YOLO, OpenCV, PyTorch, Computer Vision, object detection, and model optimization. The goal will be to maximize mAP while keeping the model efficient for local inference on your hardware. I will provide the updated model weights, training scripts, reproducible workflow, and documentation to ensure the results can be easily replicated and maintained. Please let me know further. Thanks.

@elijahhmonreal

Hi there, I just read your posting. It sounds like you need an experienced software engineer who can help build, improve, and maintain a reliable solution using Computer Graphics, Neural Networks, Computer Vision, Deep Learning, Machine Learning (ML), Image Processing, YOLO, Performance Tuning, Data Augmentation and Model Tuning. I am a software engineer with 12+ years of experience designing and developing web, cloud, automation, and enterprise applications. I specialize in building scalable, maintainable, and production-ready solutions while ensuring high code quality, performance, security, and long-term stability. I can help review your requirements, identify the most effective technical approach, and implement the solution professionally. Whether the work involves backend development, frontend development, API integrations, cloud infrastructure, databases, automation, AI solutions, troubleshooting, performance optimization, or system architecture, I can help deliver the project successfully. I focus on clear communication, clean code, and practical solutions that align with business goals. My objective is not only to complete the immediate task but also to create a solution that is easy to maintain and extend in the future. Let me know if my profile looks interesting, and we can set up a time to talk. Best regards, Elijah M.

@muhammads867

Hi , I checked your project about Improve YOLO26 Crate Detection and I can help you with this. I have experience working on similar projects and can deliver a clean, modern and high quality result based on your requirements. You can check my previous work here: https://www.freelancer.com/u/muhammads867 I understand your requirements clearly. I focus on delivering high-quality, professional results that match your vision. Quick question: Do you have any reference design or style in mind? I’m ready to start right away Best Regards, M. Sajid

@toqeer74

Given the current model's subpar performance at below 50% accuracy for Eurobox and bread-crate detection, refining the training approach is crucial. With only ~100 training images, leveraging advanced data augmentation techniques such as mosaic augmentation and judicious use of rotation and flipping can significantly enhance the dataset's diversity. Implementing a lighter YOLO variant with quantization will enable efficient inference on a mid-range GPU, targeting that ≤50 ms threshold. I can deliver the updated model weights, training script, and a concise README within 10 days. Should I send over a brief outline of how I'd tackle this?

@demivision

Hi there, Thank you for outlining your project in such detail. We're DemiVision LLC, a dedicated team specializing in computer vision, deep learning, and efficient model deployment. We understand your challenge: maximizing Eurobox and bread-crate detection accuracy with limited labeled data, all while maintaining lightweight, reproducible performance on local hardware. Your goal of pushing past 94% mAP using just ~100 training images is ambitious but absolutely achievable with a smart, methodical approach. Our team has extensive experience improving object detection systems under tight resource constraints—especially with various YOLO versions and their lighter, optimized variants. Here’s how we’d approach your project: - Comprehensive Data Augmentation: We’ll enrich your dataset with advanced augmentations (mosaic, cutmix, rotations, flips, color jittering, etc.) to simulate more diverse scenarios and improve model generalization. - Model Selection & Tuning: We'll experiment with lighter YOLO options (e.g., YOLOv4-tiny, YOLOv5n/s, YOLO-NAS Nano), leveraging pruning and quantization to ensure fast inference on your target hardware without sacrificing accuracy. - Algorithmic Optimization: We’ll fine-tune hyperparameters, apply strong regularization, and explore loss adjustments specifically tailored for dense crate scenarios. - Efficient Training Pipeline: All scripts and processes will be designed for local execution—no cloud dependencies required. - Transparent Delivery: You’ll receive updated model weights, a clearly commented training script, and a concise README to ensure seamless reproduction on your end. We’re excited by your focus on measurable results, code clarity, and practical deployment. Let’s collaborate to bring your YOLO26 crate detection to state-of-the-art levels! Looking forward to discussing the technical details and exploring your hold-out set. Best regards, DemiVision LLC

@graphicart466

Hello, I am confident in tackling your YOLO26 crate detection improvement by leveraging deep learning, model tuning, and data augmentation techniques. My experience in image processing and optimizing models for accuracy and efficiency aligns with your goal to surpass 94% mAP while maintaining real-time inference. I will focus on lightweight variants, pruning, quantisation, and smart augmentation to meet your hardware constraints. Next, I will deliver updated weights, training scripts, and a clear README, ensuring easy reproducibility. Could you share the current dataset or any specific challenges you've encountered during training? What specific challenges have you faced with your current dataset or training process? Thanks, Gourav.

@riyaatd

Hey, I think you would like to read this. One specific detail: Your YOLO26 model needs improvement in Eurobox and bread-crate detection, aiming for over 94% accuracy without cloud reliance. My skills and services: I specialize in fine-tuning algorithms, implementing smart preprocessing, and applying creative data augmentation techniques to enhance accuracy. I propose experimenting with lighter YOLO variants, pruning, quantization, and mosaic augmentation for optimal results on your local hardware. What you'll get: A refined model surpassing 94% accuracy, with lightweight requirements for mid-range GPUs or CPUs, ensuring real-time processing within 50ms/image. I would like to discuss more about the project. You lose nothing. If we create milestones, your payment will be fully protected, and you can use this message as proof for a refund. Kind Regards, Riyaat Deliverables: - Enhanced YOLO26 model weights - Optimized training script - Concise README for easy replication

@minh085

Hello, I can build your interactive business calculator with clean, modular JavaScript architecture and fully responsive UI. I have experience developing logic-heavy web tools where formulas, rules, and conditional flows are translated into accurate real-time calculations. You’ll receive a reliable, maintainable tool focused on accuracy, usability, and future extensibility. I can start immediately after reviewing your requirements. Best regards

@softdev0924

Hello, As per your project post, you need to improve your crate detection model from below 50% accuracy toward a reliable production-level result while keeping training and inference practical on local hardware. I can review your current YOLO setup, dataset quality, labels, class balance, image resolution, augmentation strategy, and validation split, then tune the pipeline for Eurobox and bread-crate detection. With only around 100 images, the key will be smarter augmentation, cleaner annotations, preprocessing, transfer learning, careful hyperparameter tuning, and selecting the right lightweight YOLO variant for speed. I can provide updated weights, training scripts, reproducible settings, and a concise README. I will also optimize inference for RTX 3060-level performance using model size control, pruning or quantization where useful, and test against your hold-out set with clear mAP and timing results. My background includes computer vision, image processing, deep learning, performance optimization, and C++/Python-based vision systems. Best regards, Roovee Felicilda

@Rapha

Hi! My name is Rafael, I'm an automation architect. I can help turn that process into a clearer, repeatable workflow that is easier to run and review. Please contact me through chat so we can review the details, confirm the real scope, and estimate timing and cost properly.

@johnedwardl

Hi, I can enhance your YOLO 26 model to achieve over 94% accuracy for Eurobox and bread-crate detection while ensuring it runs efficiently on a mid-range GPU. With my extensive experience in computer vision and model optimization, I’ll implement advanced techniques such as data augmentation, pruning, and quantization to maximize performance without relying on cloud resources. I’ll leverage creative strategies like mosaic augmentation and color tweaks to enrich your training dataset, compensating for the limited number of images. My approach ensures that the model remains lightweight and inference times stay within your target of 50 ms per image on an RTX 3060. I’m confident in delivering the updated model weights, training script, and a detailed README to facilitate easy reproduction. To ensure we stay aligned, could you confirm the specific hold-out set you’ll provide for validation? Let’s discuss how we can achieve your goals effectively. Thank you.

@Tlionn

I can create the model youre asking for. I have close to 6 years of experience developing models with low data and long tailed datasets. I deploy systematic ways to identify and fix model accuracies, starting with data and moving to the training algorithms as needed.