Convolutional Neural Network Experts in Munich
for accurate computer vision, matched in minutes with vetted freelancersHire experts who design image classification, object detection and segmentation systems with CNN architectures, PyTorch or TensorFlow. Get precise access to vetted, available freelancers who can support your computer vision project quickly.
Meet FRATCH Experts in Munich, who have recently used Convolutional Neural Network
Robert D.
Last position:
Co-Founder and Managing Director at Infinite Mind GmbH
I help leadership teams turn the potential of AI into measurable business results — fast, pragmatic, and with people at the core.
As Co-Founder of Infinite Mind, I work with CEOs and innovation leaders to identify high-impact AI opportunities, design actionable solutions, and support adoption across the organization. Our focus: driving productivity gains, smarter workflows, and scalable value.
Over the past ten years, I've worked at the intersection of Digital Transformation, Data, and Machine Learning, advising companies in software, high-tech, media, and insurance. I’ve led large-scale initiatives, including the group-wide adoption of Generative AI, and understand the strategic and human challenges of driving change at scale.
I combine a technical background in machine learning (M.Sc. Electrical & Computer Engineering, TUM) with a broader perspective shaped by degrees in Physics and Philosophy (LMU Munich). In addition to my consulting work, I’ve co-founded a tech-enabled charity and supported early-stage founders as a business coach.
If you're looking to go beyond the AI hype and make it actually work in your business — let’s talk.
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Martin R.
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
René W.
Last position:
Conference Operator at Brähler Systems GmbH
- Developed the iOS/Android Delegate App and the Conference Operator
- Updated and developed a user-friendly conference environment and real-time video streaming
- Optimized the overall conference experience by implementing customizable features for flexible setup
- Enhanced the efficiency and usability of conference technology, enabling a seamless workflow and improved participant interaction experience
Raghu Ram V.
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Tobias B.
Last position:
Lead XR Project at BMW Group
- Showcasing the world's first fully immersive AR glasses experience in a moving car at CES 2024.
- Speaker about augmented reality at international conferences (e.g. the AR Ride Concept @ Unite 2024).
- Lead a 12-person interdisciplinary software team developing Android head-unit integrations, navigation & ADAS UI, and embedded software.
- Define technical direction, drive cross-domain architecture and integration, and mentor engineers across Android, UI/UX and embedded stacks.
- Oversee a small fleet of test vehicles for validation, tests, and data collection.
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Suzan K.
Last position:
Digital Marketing & Analytics; Identity and Access Management at Rohde & Schwarz GmbH & Co. KG
- Webinar moderation.
- Created quarterly KPI reports using Adobe Analytics, Sprinklr, and M4C.
- Researched and proposed AI tools for marketing process optimization; pitch decks, integration planning.
- Defined customer personas, analyzed competitor social media activity, and improved CRM data quality.
- Executed IDM tasks related to user provisioning, access reviews, and role management.
- Performed integration testing with MidPoint, Active Directory (AD), and SAP systems.
- Supported identity lifecycle management across onboarding, role changes, and deprovisioning.
Marwa H.
Last position:
Computer Vision Project
- Developed a convolutional neural network (ConvNet)-based model that achieved 95% accuracy in traffic sign recognition and classification.
- Programming language: Python 3.7.
- Libraries: Numpy, matplotlib, scikit-learn, scikit-image.
- Deep learning framework: Tensorflow.
Adithya B.
Last position:
Edge AI Software Engineer at Neura Robotics GmbH
- Deployed and optimized Vision-Language-Action (VLA) and diffusion policy models on NVIDIA Jetson Orin and Jetson Thor, meeting real-time inference latency targets for humanoid robot control loops.
- Built TensorRT engine pipelines (PyTorch → ONNX → TensorRT) with INT8/FP8 post-training quantization, calibration dataset design, and quantization-aware validation, reducing inference memory footprint by over 3× on Jetson without accuracy regression.
- Developed custom CUDA C++ plugins and CUDA Graphs for latency-deterministic, real-time policy execution – meeting hard runtime and memory constraints on embedded GPU targets.
- Developed an inference engine for VLA models on top of llama.cpp bringing different VLA policies under single runtime, packaging each as a single self-contained GGUF that needs no Python or PyTorch.
- Profiled and tuned GPU execution using NVIDIA Nsight Systems and Nsight Compute, identifying CUDA kernel bottlenecks, memory bandwidth saturation, and SM occupancy issues across Jetson Orin and Thor compute profiles for cross-layer performance optimization.
Discover over 15,000 top freelancers
Statistics of experts using Convolutional Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 11 years)
Position duration
2.5 years (Germany: 1.8 years)
Positions per freelancer
9 (Germany: 8)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Finance
Bachelor's degree or higher
100%
Master's degree or higher
100% (Germany: 91%)
Doctorate
30% (Germany: 14%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Munich using Convolutional Neural Network
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Convolutional Neural Network experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (91%)
- Automotive (64%)
- Education (64%)
- Manufacturing (55%)
- Banking and Finance (36%)
- Healthcare (36%)
- Insurance (27%)
- Advertising (18%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What CNNs do
A Convolutional Neural Network, commonly called a CNN or ConvNet, learns visual patterns from structured data such as images, video frames and medical scans. Convolutional layers detect features from edges and textures to shapes and objects, while later layers combine them for a task-specific prediction. This makes CNNs useful when software must interpret visual information at scale.
Typical applications
CNN specialists deliver systems that turn raw visual data into reliable business outputs:
- Image classification for products, documents and industrial parts
- Object detection for people, vehicles, defects and equipment
- Image segmentation for medical scans, geospatial imagery and inspection
- Optical character recognition and document understanding
- Video analysis for monitoring, logistics and quality control
Ecosystem and tooling
Practical work spans data preparation, model design, training and deployment. Experts commonly use PyTorch or TensorFlow with CUDA-enabled hardware, Python data libraries and tools for annotation, experiment tracking and model serving. Transfer learning with established vision models can reduce training effort, while ONNX and TensorRT can support efficient inference in production.
When to bring in specialists
Freelance expertise helps when a team has valuable image or video data but lacks a robust path from prototype to production. Typical signals include inconsistent labels, poor performance on real-world images, slow inference or a model that works in tests but fails under changing lighting and camera conditions. Specialists can assess feasibility, define evaluation criteria and establish a repeatable training pipeline.
Strong delivery practices
Strong professionals connect model quality with the conditions in which the system will run. They examine class balance, annotation quality, data leakage and edge cases before selecting an architecture. They also document preprocessing, monitor drift, compare precision and recall in context, and make inference costs, latency and hardware requirements clear to stakeholders.
Beyond the model
A successful CNN project includes more than a trained network. It may require camera integration, image storage, data versioning, API design, cloud or edge deployment and monitoring. Munich teams in manufacturing, mobility, healthcare and research may need on-site workshops alongside remote implementation, so clear communication and practical knowledge of the operating environment matter as much as model architecture.
Frequently asked questions
Before you brief your next project: the most common questions about Convolutional Neural Network.
A Convolutional Neural Network is used to recognize and interpret visual patterns in images and video. Common applications include classification, object detection, segmentation, defect inspection, medical imaging and optical character recognition.
A CNN captures local visual patterns efficiently through convolutional filters and often performs well with limited latency or edge hardware. Vision transformers can model wider relationships across an image, but they may require different data, training methods and infrastructure. The right choice depends on the dataset, task and deployment constraints.
A strong ConvNet specialist should understand image annotation, data augmentation, evaluation design and transfer learning. Useful adjacent skills include PyTorch or TensorFlow, Python, GPU acceleration, model serving, MLOps and integration with cameras, databases or business APIs.
The required background depends on the risk and scope of the assignment. A proof of concept may need focused experience with a similar visual task, while production work calls for evidence of handling data quality, failure cases, monitoring, deployment and maintenance. Ask for concrete examples involving comparable inputs and operating conditions.
A Convolutional Neural Network project can often be delivered remotely when data access, annotation workflows and deployment environments are available online. On-site sessions in Munich can still help with camera calibration, factory inspection, clinical workflows or stakeholder workshops. Agree early on data handling, language expectations and access to physical equipment.
A CNN is not always the best option. Rule-based methods or classical techniques such as edge detection and feature descriptors may be preferable when the environment is tightly controlled, labeled data is scarce or the task is simple and explainable. A specialist should compare both approaches against accuracy, maintenance and compute needs.
Look for a clear explanation of data splits, labeling risks, evaluation metrics and the difference between laboratory results and production behavior. Ask how the professional handles false positives, changing conditions, model drift and reproducibility. A capable CNN specialist connects technical choices to measurable operational outcomes.
A typical Convolutional Neural Network engagement can include a data assessment, labeled dataset strategy, baseline model, training pipeline, evaluation report and deployment package. Production assignments may also require API integration, inference optimization, monitoring, documentation and handover materials.
The average hourly rate of freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects is 94 €, which corresponds to a daily rate of about 754 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 30% hold a doctorate.
On average, freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are German (100%), English (100%), and French (36%).
The most common industries among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are Information Technology (91%), Automotive (64%), and Education (64%).
The most common business areas among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (100%).
Main locations of FRATCH Experts, who have recently used Convolutional Neural Network
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Berlin Request a free demo
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FRATCH CEO
