Machine Learning Engineer (Computer Vision) to Enhance ResNet Model + Implement Human-in-the-Loop (HITL)

@AwaisChaudhry

Hello! I will start by quickly assessing your current ResNet setup and the HITL gap. The plan is simple: boost accuracy and robustness, add smart uncertainty signals, and build a smooth feedback loop so human input trims labeling burden without slowing you down. I’ll implement confidence/uncertainty estimation, flag low-confidence cases, and route them to a scalable labeling stage. The retraining workflow will take in newly labeled samples and calibrate the model, keeping performance steady as data grows. Expect a production-ready, end-to-end pipeline with monitoring, so you can watch metrics, calibration, and drift in one place. What is the current labeling workflow and labeling speed? What is the target accuracy and acceptable uncertainty metrics? Do you have Label Studio or annotation tool preferences? What is your production pipeline for retraining cadence? Do you require compliance for biological data? What are available compute resources and deployment constraints? Any preferred HITL interfaces and user roles? How will you monitor drift and calibration over time? Do you have existing data splits and eval metrics? Best regards,

@neerajdhameliya

Hello, I’ve built computer vision pipelines with active learning, HITL review workflows, and uncertainty-based retraining in PyTorch. These systems included low-confidence image routing, annotation integration, and continuous model improvement loops. Approach to uncertainty sampling: I’d implement entropy/margin-based scoring to flag uncertain predictions for human review, then retrain using validated labels. I’d also add confidence calibration to reduce overconfident outputs. Suggested improvements: Dataset balancing, stronger augmentations, drift monitoring, and testing newer backbones like EfficientNet or ConvNeXt alongside your ResNet pipeline. I’d be happy to review your current setup and propose a phased implementation plan. Best, Niral

@Fazeennazar

With my 7+ years of experience as a senior engineer, I possess a comprehensive understanding of Artificial Intelligence and Machine Learning (ML), qualities that are exceptional for your project. My strong background in Python and expertise in major deep learning frameworks like PyTorch and TensorFlow make me more than competent to leverage ResNet's potential, fine-tune its structures, and improve your image classification system significantly. Implementing an active learning/human-in-the-loop system is something I've excelled at in comparable projects. I am proficient in handling data with high uncertainty, flagging low-confidence predictions automatically, and intelligently selecting uncertain samples using various measures like entropy or margin sampling. Besides my expertise in CNNs, I also understand the importance of model calibration and have successfully resolved overconfidence issues in the past. My familiarity with medical and biological image data, coupled with my proficiency in OpenCV for advanced image processing tasks makes me even more compatible with your needs. Moreover, we can optimize our collaboration leveraging my MLOps/deployment skills to build a highly scalable training and feedback loop enabling continuous learning from human feedback.

@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

@ZohaibRoy

⭐⭐⭐⭐⭐ Enhance Image Classification with Active Learning in Computer Vision ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project needs and see you're looking for an experienced machine learning engineer to improve your image classification system. Look no further; Zohaib is here to help you! My team has successfully completed 50+ projects similar to yours in computer vision. I will evaluate your current model, design an active learning system, and ensure efficient integration of human feedback. ➡️ Why Me? I have 5 years of solid experience in machine learning and computer vision, focusing on image classification and active learning systems. My expertise includes working with PyTorch and TensorFlow, model calibration, and pipeline design. Additionally, I have a strong grip on uncertainty estimation and performance monitoring, ensuring a comprehensive approach to your project. ➡️ Let's have a quick chat to discuss your project in detail. I can provide samples of my previous work and showcase how I can enhance your system. Looking forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Machine Learning ✅ Computer Vision ✅ Active Learning Systems ✅ PyTorch ✅ TensorFlow ✅ Model Calibration ✅ Uncertainty Estimation ✅ Image Classification ✅ Pipeline Design ✅ Performance Monitoring ✅ Data Annotation Tools ✅ MLOps Waiting for your response! Best Regards, Zohaib

@Mahihassi

With my extensive AI and machine learning experience, particularly in computer vision and deep learning using platforms such as PyTorch and TensorFlow, I feel well-equipped to tackle your project. Having worked on similar active learning systems you require, I understand the complexity of intelligent sample selection for human labeling, model calibration, and the continuous retraining process. I am confident that I can significantly reduce your team's manual labor while improving model accuracy. Furthermore, I have a solid grasp of uncertainty estimation techniques such as entropy and margin-based sampling which would be essential in selecting uncertain samples to send for human labeling. This process will be critical in enhancing your existing image classification system. Additionally, my MLOps and deployment experience will ensure that the implemented system works seamlessly at scale. In terms of work samples, similar to the ones you require in this project, I have built scalable backend systems with AI models and developed platforms that incorporate uncertainty estimation and human feedback loops. Overall, my blend of technical skills alongside critical thinking will provide you with a comprehensive solution that exceeds your expectations. Let's discuss how we can optimize your bacterial colony image classification system!

@irsolutions

With over a decade of experience in machine learning engineering and high-scale systems, I understand your project goal of enhancing the ResNet model and implementing a Human-in-the-Loop system for image classification. My background in developing high-complexity systems, such as scaling Telegram Mini Apps for over 1 million users, directly applies to the challenges your project faces in improving accuracy and reducing manual effort. A strategic insight for your project would be to focus on implementing uncertainty estimation techniques like entropy or margin sampling to intelligently select samples for human labeling. In a similar project, I successfully reduced manual effort by implementing automated flagging of low-confidence predictions and integrating newly labeled data into the retraining workflow. I encourage you to reach out to discuss how we can collaborate on structuring a scalable training and feedback loop for your project. Let's chat about how I can leverage my expertise in PyTorch, TensorFlow, computer vision, and active learning systems to achieve your project goals efficiently.

@AITSoft

Hi there, I understand you want to boost your ResNet-based image classifier for bacterial colonies by adding a solid HITL active learning loop, uncertainty estimation, and a scalable retraining workflow. My plan is to first audit your current pipeline, identify bottlenecks in labeling and data flow, and implement a lightweight uncertainty module (entropy/m margin) with automatic low-confidence flagging. Then I’ll set up a crisp HITL loop: automatically collect uncertain images, route them to human annotators (with an intuitive interface), and feed the labels back into retraining with calibration steps to reduce overconfidence. I’ll build a production-ready pipeline with monitoring so you can track accuracy, calibration error, and labeling workload over time. The result will be a robust, scalable system that cuts manual work while improving accuracy and adaptation to new data. What is the most important factor you want to optimize first: accuracy, labeling efficiency, or latency of HITL feedback? If needed, I’ll propose model tweaks or data augmentation strategies and help set up end-to-end MLOps, including versioning, tests, and rollback capabilities. Questions to align on details: What is the acceptable latency for uncertain cases to reach a human annotator? Which labeling tool do you prefer (Label Studio, Supervisely, or a custom UI)? Do you want confidence scores to be exposed to downstream systems for decision rules? What is the target calibration metric and accept

@Herculesz

Hi I can help improve your ResNet-based bacterial colony classification system by adding a practical human-in-the-loop active learning workflow. The key technical problem is reducing manual labeling while still improving accuracy, especially when the model is overconfident or uncertain on visually similar colony classes. I have experience with PyTorch/TensorFlow, CNN image classification, ResNet fine-tuning, uncertainty estimation, model calibration, annotation workflows, and production ML pipelines. My approach is to first evaluate current accuracy, class imbalance, confidence behavior, and failure cases, then implement uncertainty-based sampling using entropy, margin scores, and calibration methods like temperature scaling. I’ll design a pipeline that flags low-confidence images, routes them to human review through tools like Label Studio, and feeds newly labeled samples back into retraining. I’ll also add monitoring for precision, recall, calibration error, and data drift so the system improves continuously. The goal is a scalable feedback loop that reduces manual work while making predictions more reliable. Thanks, Hercules

@hoangnn90

✅Full Experience in Image Classification and Computer Vision with Python Programming✅. ✳️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!

@quan9824

Leveraging a vast skillset in Machine Learning, including Deep Learning, Computer Vision, and AI Systems, I am confident that I can make a substantial impact on enhancing your ResNet model and implementing the HITL system. My experience in object detection, image recognition, CV especially in medical fields equips me to tackle your biological image data with confidence. As an active learning systems expert, I understand the importance of minimizing the manual effort while improving model performance. My understanding of algorithm guarantees that I do more than test your current pipeline; I am driven to identify scope for improvement and optimize it efficiently. My previous projects involved Image Processing and Recognition of growing accuracy from the research level to production-ready model which aligns quite well with your needs. Having worked with PyTorch, Tensorflow for various use-cases including OCR and NLP, I am competent in deploying ML models using industry standards(MLOps) which is an added bonus alongside my full-time availability, allowing immediate modifications if any emergencies arise. Incorporating Model calibration, monitoring pipeline and uncertainty estimation with one another will be crucial steps in escalating the efficiency thereby reducing repetition. Choosing me will assure you a committed and proactive Engineer who will be sensitive to your needs and timeline throughout

@tangramua

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@Feriver

Hello, I understand you need an experienced computer vision ML engineer to improve a ResNet-based bacterial colony classification system and implement a full human-in-the-loop (HITL) active learning pipeline to reduce manual labeling while improving accuracy and robustness. My approach is to first audit your current ResNet model, dataset distribution, and inference pipeline to identify key failure modes such as class imbalance, overconfidence, and boundary uncertainty. I will then implement uncertainty estimation techniques (entropy, margin sampling, MC dropout or temperature scaling) to flag low-confidence predictions automatically. On top of this, I will design an active learning loop where uncertain samples are routed to a labeling interface (e.g., Label Studio), and newly labeled data is fed back into a retraining pipeline. This will include data versioning, retraining triggers, and evaluation tracking to ensure continuous model improvement without disrupting production inference. I will also improve model calibration to reduce overconfidence, and optimize training strategies (augmentation, loss tuning, or architecture refinements if needed) to improve generalization. The final system will be structured as a scalable ML pipeline with clear separation between inference, human feedback, and retraining modules. Thanks, Asif

@nrajuu07

As a skilled Machine Learning Engineer with a focus on medical image analysis and computer vision, I can offer you the expertise you need to take your existing image classification system to new heights. My experience in developing robust deep learning models, particularly ResNet-based architectures, and working with medical and biological image data make me an ideal fit for your project description. Beyond the technical requirements, my experience prioritizes efficiency, scaling, and compatibility with real-world use cases. I design production-ready ML pipelines and have talent in MLOps and deployment; crucial skills that ensure my solutions use robust methodologies and are ready for immediate implementation. I am excited about the possibility of working on this project with you and promise to deliver a functional system that reduces manual effort while improving accuracy continuously. So let's kickoff!

@noursherif030

As an AI and computer vision enthusiast with a proven track record in developing intelligent systems, I believe my experience aligns perfectly with your requirements. My proficiency in PyTorch and TensorFlow, in conjunction with my deep understanding of CNNs and image classification, makes me well-positioned to evaluate and enhance your current image classification system based on the ResNet architecture. Moreover, I've had hands-on experience in the implementation of active learning / HITL systems, which I believe will be a key component of your project. Active Learning methods such as uncertainty estimation are vital to selecting and prioritizing uncertain samples for human labeling, thereby drastically reducing manual effort without compromising on accuracy. Given my background in Software Engineering complemented by expertise in cybersecurity and network security, I fully understand the importance of model calibration for mitigating overconfidence issues which invariably affects any computer vision pipeline. I'm certain that I can design a production-ready ML pipeline integrating these necessary features whilst maintaining a secure environment that can be effortlessly scaled as per your requirement. Let's work together to reduce your manual labeling workload and create a continuous learning system from human feedback

@xuezhe315

Hi, As someone with a strong background in artificial intelligence and machine learning, particularly in the field of computer vision, I believe I have the expertise you need to take your image classification system to the next level. My experience with PyTorch and TensorFlow paired with my understanding of CNNs and image classification will allow me to effectively evaluate and improve your current ResNet model. Additionally, my familiarity and proven track record in active learning and human-in-the-loop (HITL) systems make me well-equipped to design and implement the features you require. For instance, I’ve efficiently implemented uncertainty-based sampling in previous projects, which could be highly beneficial for your workflow. Moreover, I understand how crucial it is to structure scalable training and feedback loops—I have designed production-ready ML pipelines that could easily integrate newly labeled data into the retraining workflow. My ultimate goal is to deliver solutions that reduce your manual effort while improving model performance—a philosophy shared by your project. Best Wishes.

@kamran2012

This looks like a great fit, I will evaluate your ResNet pipeline, implement confidence-based uncertainty estimation — entropy and margin sampling — and build the full HITL active learning loop so uncertain predictions route to human reviewers and labeled corrections feed back into automated retraining. For model calibration, I will apply temperature scaling post-training — ResNets tend toward overconfident softmax outputs on biological images, and proper calibration is critical before uncertainty scores can reliably trigger the HITL flag. Questions: 1) What is your current dataset size and class distribution across colony types? 2) Are you open to integrating Label Studio for the human review interface? Ready to start whenever you are. Kamran

@alizain721

You’re already at fast inference with a ResNet — the next step is making the model tell you when it’s unsure so humans only see the hard cases. Usually the real bottleneck isn’t model capacity but choosing the right uncertainty signal and a tight feedback loop so new labels actually improve the model fast. I recently built an active-learning/HITL pipeline for microscopy image classification: added uncertainty sampling, Label Studio integration, and online fine-tuning — cut manual labeling by roughly half while raising validation accuracy. I’d start by profiling your ResNet (calibration, class imbalance, failure modes), add calibrated uncertainty (temperature scaling + ensemble or MC-dropout), implement entropy/margin sampling with automatic flagging, push uncertain items to an annotation endpoint, and run scheduled fine-tune cycles with a rolling validation set. I’ll also add simple monitoring (calibration drift, per-class recall) so you know when to intervene. Quick question: how many labeled images do you have and which framework is the model in (PyTorch or TensorFlow)?

@mykhaylob4

✋ Hi there. I can enhance your ResNet model with confidence estimation, build an active learning HITL pipeline, and reduce manual labelling by sending uncertain samples for human review. ✔️ I have built two active learning systems for medical image classification before, each one using entropy sampling, model calibration with temperature scaling, and a Label Studio integration for human feedback loops. ✔️ I will evaluate your current ResNet, add uncertainty estimation via Monte Carlo dropout or softmax entropy, flag low‑confidence predictions, connect to a lightweight annotation queue, then set up a retraining workflow that incorporates new labelled data. Let’s chat so you can share your current model checkpoint and a sample of bacterial colony images. Mykhaylo