Mech-DLK
Mech-DLK integrates the entire work process for training deep learning models and makes it possible to independently, intuitively and quickly train individual neural networks or use already trained models.This saves time and labor costs, simplifying the process while significantly improving training efficiency.
In addition, Mech-DLK supports a variety of training models, including defect detection, classification, or instance segmentation.The entire process can be performed locally by users without external data transfer, providing a high level of data security.
Mech-DLK is part of the machine vision software Mech-Vision and offers a particularly easy-to-learn use of deep learning models. Mech-DLK is available as an individual product - contact us for more information.
Even without a technical background, users can train deep learning models themselves after a simple training session by Mech-Mind.
With a variety of powerful AI algorithms, including instance segmentation, image classification, object recognition, and semantic segmentation, users can quickly train a wide range of models. This makes it easy to solve the most challenging applications such as overlapping object detection and classification, high-precision measurements, and more.
With Mech-DLK, users can effortlessly train deep learning models for all types of objects. The high-precision deep-learning algorithms guarantee excellent accuracy with fewer required parameters. Advanced data expansion allows users to train a model with smaller image sets. And with the built-in Finetune feature, which dramatically increases training efficiency by optimizing existing models, users do not need to train a model from scratch.
Mech-DLK can perform model validation, show the validation results, and compare the validation results with the annotations.This significantly improves the validation efficiency of image processing solutions.
Mech-DLK supports fast project deployment with Mech-Vision and offers SDKs in several programming languages (C, C++, C#) for further customization.
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