MobileNet Experts in Germany
matched in minutes from over 15,000 CVs with the power of AI.Hire experts who build lightweight vision models, mobile image classifiers, and TensorFlow Lite pipelines with MobileNet, MobileNetV2, and MobileNetV3. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used MobileNet
Nenad Biresev
Last position:
Safety Video Analytics Project for Airbus at Airbus
- Developed a real-time video analytics proof-of-concept for deployment on NVIDIA Jetson edge devices.
- Implemented DeepStream pipelines including object detection, tracking, human pose estimation, face anonymization, and zone intrusion detection.
- Built a Qt/Python demonstration UI interfacing with the AI pipeline via REST APIs.
Enjeda Cekaj
Last position:
Associate Researcher — AI & Computer Vision at University of Augsburg
- Research multimodal AI systems integrating image, text, and structured data.
- Build end-to-end AI pipelines for data processing, model training, and evaluation.
- Develop and test computer vision and image recognition solutions using deep learning.
Javid Hasanov
Last position:
Research Assistant (Application Project - CHAI) at FH Kiel & Christian-Albrechts-Universität zu Kiel (CAU)
- Developing an AI-based corrosion detection system for maritime infrastructure as part of the CHAI Research Project.
- Built a binary image classification model to detect corrosion using a dataset of 5,000+ metal surface images.
- Automated data labeling from segmentation masks and designed bounding box generation workflows for individual corrosion areas.
- Conducted data preprocessing, data augmentation, and model evaluation (accuracy, precision, recall, F1-score).
- Collaborated with the research team to integrate computer-vision workflows for corrosion monitoring and dataset enhancement.
Ashmi Jha
Last position:
Software Developer at Myrix Labs
- Engineered high-performance APIs with FastAPI + MongoDB, integrating live weather data (NOAA, NWS).
- Developed an AI chatbot with OpenAI APIs — context-aware by location, profession & interests.
- Created admin dashboard APIs for real-time monitoring and zero-downtime configuration.
- Integrated Stripe Embedded Payments with secure transactions & subscription management via webhooks.
Jana Becker
Last position:
Data Scientist at masem research institute GmbH
- Supporting clients in data science and machine learning projects
- Designing the architecture for ETL pipelines of an AI platform for semi-automated processing of sensitive customer data for R+V
- Modernizing the tech stack with Docker and Elasticsearch
- Training colleagues on monitoring and reporting with the ELK stack
- Developing an ML application for quick detection of turf diseases using a fine-tuned MobileNetV2 and semantic segmentation for a golf course builder
- Taking on team lead and project management to transform a monolithic on-premise system into a web application
Adithya Naik
Last position:
Vehicle Classification and Detection using Neural Networks
Detecting and classifying vehicles in images and video for traffic monitoring
- A YOLO + Faster R-CNN model built for real-world traffic and autonomous-vehicle scenarios. Awarded Best Paper Award at St Joseph Engineering College, March 2025.
What it does
- The model takes images or video frames and both localizes and classifies vehicles by type, making it usable for downstream applications such as traffic-flow monitoring or perception in autonomous-vehicle systems.
What I did
- Combined YOLO (for fast detection) with Faster R-CNN (for higher-precision classification), rather than relying on a single architecture, trading off speed and accuracy where each mattered most.
- Achieved 90% mean Average Precision (mAP), evaluated using IoU-based metrics rather than just raw accuracy, to properly reflect localization quality.
- Handled the full data processing and evaluation pipeline in Python using TensorFlow and OpenCV.
- The accompanying paper was awarded the Best Paper Award by the Department of CSE at St Joseph Engineering College.
Tech stack: Python, TensorFlow, OpenCV, YOLO, Faster R-CNN
Discover over 15,000 top freelancers
Statistics of experts using MobileNet
Aggregated from the professional profiles of matched freelancers.
Experience
7 years
Position duration
1.2 years
Positions per freelancer
5
Top business areas
Product Development, Information Technology, Research and Development
Top industries
Information Technology, Education, Automotive
Bachelor's degree or higher
100%
Master's degree or higher
100%
Certifications per freelancer
1
Most common languages
German, English, Azerbaijani
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany 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 Germany using MobileNet
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
MobileNet is a family of lightweight convolutional neural networks for vision tasks. It is designed for fast inference on mobile devices, edge hardware, and other constrained environments where memory and compute matter.
Where it fits
It is used for image classification, object detection backbones, feature extraction, and embedded computer vision.
- Mobile apps that need on-device vision
- Edge systems with limited hardware
- Prototype models that must run quickly
- Production pipelines for TensorFlow Lite
Core variants
Teams often work with MobileNet, MobileNetV2, and MobileNetV3. The choice depends on latency targets, accuracy needs, and the deployment stack around TensorFlow, Keras, or converted ONNX models.
What strong specialists do
Strong professionals know how to adapt input sizes, tune transfer learning, and replace heavy backbones with efficient ones without breaking the rest of the model. They also check how quantization, pruning, and hardware support affect real inference behavior.
When companies bring them in
Companies usually need MobileNet expertise when they want a model that fits on a device, when an existing vision model is too slow, or when a prototype must be turned into a reliable release. In Germany, this often comes up in industrial inspection, retail vision, logistics, and connected products.
Collaboration and delivery
A good freelancer can work remotely with model code, training data, and deployment scripts, or join on-site when camera setup and edge testing need close coordination. Clear handover usually includes training notes, export steps, and guidance for mobile or embedded deployment.
Frequently asked questions
Questions about MobileNet? Start with the answers below.
MobileNet is used for vision tasks that need efficient inference, especially image classification and object detection on mobile or edge devices. It is a strong fit when a team needs useful accuracy without a heavy model. Many projects use it as a backbone before adding custom layers for a specific product.
MobileNet is smaller and faster than many standard convolutional networks, which makes it easier to run on constrained hardware. The tradeoff is that it may need careful tuning to reach the same accuracy as a larger model. A good specialist helps choose whether the speed gain is worth the model change.
MobileNetV2 is often chosen for a stable, well-known lightweight baseline, while MobileNetV3 adds design changes that can improve efficiency in some settings. The better choice depends on your target device, framework support, and how much retraining you can do. A freelancer should explain the deployment impact, not just the architecture difference.
A strong MobileNet specialist usually also knows TensorFlow, Keras, model export, and deployment for TensorFlow Lite or similar runtimes. Skills in image preprocessing, data labeling, and evaluation are also important. For edge projects, knowledge of quantization and device constraints is often essential.
MobileNet projects can be small experiments or production releases, but the real need is practical experience with training and deployment. If the model will run on a device, you want someone who has shipped optimized vision pipelines before. That matters more than general machine learning theory alone.
Yes, MobileNet work is often well suited to remote collaboration because most tasks happen in code, data, and model artifacts. For teams in Germany, remote experts can cover training, conversion, and performance tuning, while on-site visits may help with camera calibration or edge testing. Mixed setups are common when hardware is involved.
Look for clear evidence that the MobileNet specialist can explain why a variant was chosen, how it was trained, and how it was deployed. Good signs include clean model export steps, realistic evaluation, and awareness of device limits. You also want someone who can discuss failure cases, not only successful results.
Ask which version of MobileNet they have used, which framework they prefer, and how they handle transfer learning and optimization. You should also ask how they measure latency, accuracy, and deployment risk on the target device. Those answers tell you whether they can support a real product, not only a notebook demo.
The average hourly rate of freelancers in Germany who have used MobileNet in their recent projects is 69 €, which corresponds to a daily rate of about 551 € based on an 8-hour working day.
Of the freelancers in Germany who have used MobileNet in their recent projects, 100% hold at least a Bachelor's degree and 100% hold at least a Master's degree.
On average, freelancers in Germany who have used MobileNet in their recent projects have 7 years of professional experience, with a single engagement typically lasting around 1.2 years.
The most common languages among freelancers in Germany who have used MobileNet in their recent projects are German (100%), English (100%), and Azerbaijani (17%).
The most common industries among freelancers in Germany who have used MobileNet in their recent projects are Information Technology (83%), Education (67%), and Automotive (50%).
The most common business areas among freelancers in Germany who have used MobileNet in their recent projects are Product Development (100%), Information Technology (83%), and Research and Development (83%).
Main locations of FRATCH Experts, who have recently used MobileNet
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 Request a free demo
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Philipp Thomaschewski
FRATCH CEO
