MobileNet Experts in Germany
in minutes from over 15,000 CVs with the power of AI.Hire experts who build lightweight image classifiers, object detection pipelines, and edge-ready models with MobileNet, MobileNetV2, and MobileNetV3. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used MobileNet
Nenad B.
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 C.
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 H.
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 J.
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 B.
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 N.
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 19 Sep 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
MobileNet 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 (83%)
- Education (67%)
- Automotive (50%)
- Healthcare (50%)
- Banking and Finance (33%)
- Manufacturing (33%)
- Retail (33%)
- Aerospace and Defense (17%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Mobile vision
MobileNet is a family of compact neural networks for image tasks on phones, embedded devices, and other systems with tight compute limits. It is used for classification, detection, and feature extraction where speed and model size matter more than heavy model capacity.
Typical use
Teams bring in MobileNet specialists for practical computer vision work:
- image classification for apps and devices
- object detection or transfer learning on top of MobileNet
- on-device inference for edge products
- model conversion for TensorFlow Lite or similar runtimes
Core stack
Strong specialists know the full workflow around MobileNet, not only the model name. They work with TensorFlow, Keras, TensorFlow Lite, image preprocessing, augmentation, quantization, and export formats that keep inference small and stable.
When to hire
Companies usually look for freelance help when a vision feature must run locally, when a prototype needs to become production-ready, or when an existing model is too large for deployment. In Germany, this often comes up in manufacturing, mobility, retail, and industrial inspection projects.
What good experts do
A solid MobileNet professional can tune input pipelines, adapt a pretrained backbone, and measure trade-offs between latency, size, and accuracy. They also document deployment steps clearly so backend, app, and product teams can use the model without guesswork.
Delivery focus
Freelance work with MobileNet often ends in concrete deliverables, not research notes. Expect trained models, evaluation reports, mobile or edge deployment guidance, and support for integration into Android, iOS, or embedded workflows when local collaboration in Germany is needed.
Frequently asked questions
Questions about MobileNet? Start with the answers below.
MobileNet is used for compact computer vision tasks where a model must stay small and fast. Companies use it for image classification, object detection, and feature extraction on mobile apps, edge devices, and embedded systems.
MobileNet is usually chosen when latency and model size matter more than maximum accuracy. It is lighter than many large convolutional backbones, which makes it a better fit for on-device inference and resource-limited environments.
MobileNet is the family name, while MobileNetV2 and MobileNetV3 are later versions with updated blocks and efficiency trade-offs. In practice, freelancers should know how to choose between them based on the target device, runtime, and accuracy needs.
A strong MobileNet specialist should also know TensorFlow or Keras, image preprocessing, transfer learning, and model export for mobile runtimes. Quantization and performance testing matter too, especially when the model must run locally on a device.
MobileNet work makes sense as soon as a team needs a working prototype, a model adaptation, or deployment support. You do not need a research team to get value, but you do need someone who has shipped real inference pipelines and can handle trade-offs.
Yes, most MobileNet work can be done remotely if the freelancer gets access to sample data, device specs, and clear testing criteria. On-site sessions in Germany can still help when the model must be checked against hardware, cameras, or factory constraints.
A good MobileNet professional explains why a version, preprocessing step, or compression method was chosen. They should show prior work with evaluation results, deployment constraints, and clear handover notes, not just a trained notebook.
Ask which MobileNet version fits your target device, how the model will be evaluated, and whether the expert can support export to your runtime. If your team is in Germany, also check how they handle time zones, language, and collaboration with your app or product team.
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
