Blog

DeepDetect v0.16.0

Training high accuracy Object Detectors

7 May 2021

Training an object detector using Pytorch and DeepDetect Pytorch is the most widely used framework in ML research and industry today. Pytorch also offers one of the most versatile and useful pure C++ API among the Deep Learning frameworks. For these reasons our focus has been to develop the DeepDetect C++ torch backend so that it is as complete as possible wrt most of the automation task DeepDetect supports: computer vision, NLP, time-series and CSV data mostly.
DeepDetect v0.16.0

DeepDetect v0.16.0

29 April 2021

DeepDetect release v0.16.0 DeepDetect v0.16.0 was released last week. Below we review the main features, fixes and additions. DeepDetect v0.16 was released last week. With support for object detector training with torch C++ and a choice of backbone networks.https://t.co/uDw9ejQdiQ DeepDetect is our production toolset that aggregates our advances solving real-world automation problems.#DeepLearning #AI — jolibrain (@jolibrain) April 30, 2021 In summary Object detector (Faster-RCNN, RetinaNet) training from Torch C++ backend FP16 inference on GPU with Torch backend Madgrad optimizer with Torch backend Fixes & Improvements Improved memory allocation with TensorRT backend Fixed case-sensitivity of service names Improved object detection RefineDet model input image size control Ability to load partial model with the Torch backend Fixed in-memory crops in chains Docker images CPU version: docker pull jolibrain/deepdetect_cpu:v0.
DeepDetect v0.15.0

DeepDetect v0.15.0

2 April 2021

DeepDetect release v0.15.0 DeepDetect v0.15.0 was released last week. Below we review the main features, fixes and additions. DeepDetect v0.15 was released last week with new SWA optimizer for deep nets, fixes and improvements from time-series forecasting with seasonality to object detectors.https://t.co/00WZppZ2xh DD is our swiss army knife for applied deep learning! All docker https://t.co/ISsZ3ljAM4 — jolibrain (@jolibrain) April 2, 2021 In summary Stochastic Weight Averaging (SWA) for training with the torch backend All our builds are now using Ubuntu 20.
Training a face mask remover with JoliGAN

Removing face masks with JoliGAN

26 March 2021

Having fun with https://t.co/Y7NXTdxTlV removing masks… #GAN #DeepLearning pic.twitter.com/VTUMO9j4Lh — jolibrain (@jolibrain) March 20, 2021 This post shows a somewhat frivolous application of our JoliGAN software, that removes masks from faces. We use this example as a proxy to much more useful industrial and augmented reality applications (actually completely unrelated to faces, etc…). Some result samples are below. At Jolibrain we solve industrial problems with advanced Machine Learning, and we build the tools to help us fullfil this endeavour.
Object Detector with the DeepDetect Platform

Quickly build an object detector with the DeepDetect Platform

19 March 2021

This post describes how we quickly train object detectors at Jolibrain. As a tutorial we train a car detector from road video frames using the DeepDetect Platform that is a Web UI on top of the DeepDetect Server. All our products are Open Source. At Jolibrain we are specialized in hard deep learning topics, i.e. for which no on-the-shelf product does yet exist. Everything else we automate and put into DeepDetect Server and Platform.
DeepDetect v0.14.0

DeepDetect v0.14.0

10 March 2021

DeepDetect v0.14 was released last week with inference for object detectors with torch, a novel Transformer architecture for time-series, and the new SAM optimizer.https://t.co/ISIrVaWR0w Docker images at https://t.co/xNYgOQnG9h for CPU, CUDA & RT.#deeplearning #MLOps — jolibrain (@jolibrain) March 10, 2021 DeepDetect release v0.14.0 DeepDetect v0.14.0 was released a couple weeks ago. Below we review the main features, fixes and additions. In summary Inference for torch object detection models Novel Transformer architecture for time-series Vision Transformer with Realformer support (https://arxiv.
Sharp and wide minima

Model Training and Generalization

5 March 2021

Machine learning aims at replacing hand-coded functions with models automatically trained from data. Such automation is a reasonable and certainly inevitatable trend in computer science. The main ‘trick’ however remains in training models that generalize well to unseen data. Training a model involves a potentially large set of data dubbed the training set. It is somewhat easy for the machine, and especially for deep neural networks to fully memorize these samples.
Time Series forecasting with NBEATS

Time Series forecasting with NBEATS

26 February 2021

This is the second article on time-series with Deep Learning and DeepDetect. It shows how to use a type of deep neural network architecture named NBEATS dedicated to time-series. In our earlier post on time-series with recurrent networks and DeepDetect, we did use LSTMs with DeepDetect, and here we thus focus on more appropriate architectures. This blog post shows how to obtain much more configurable and accurate time-series forecasting models than with other methods.
OCR API with DeepDetect

Setting up an OCR REST API with DeepDetect

19 February 2021

This article shows how to setup a REST API for an OCR system in five minutes. Goal: setup an API endpoint to which send images and get text position and characters in return Technology: A deep neural object detector that locates text in images A deep neural OCR model that reads detected text into a character string For this, DeepDetect provides: A REST API for Deep Learning applications Pre-trained models that are free to use A simple way to chain models so that a single API call does all the work DeepDetect setup Let’s start a ready-to-use docker image of DeepDetect server with CPU and/or GPU support.
Time Series forecasting with DeepDetect

Time Series forecasting with Deep Learning using DeepDetect

12 February 2021

This article is the first of an ongoing serie on forecasting time series with Deep Learning and DeepDetect. DeepDetect allows for quick and very powerful modeling of time series for a variety of applications, including forecasting and anomaly detection. This serie of posts describes reproducible results with powerful deep network advances such as LSTMs, NBEATS and Transformer architectures. The DeepDetect Open Source Server & DeepDetect Platform do come with the following features with application to time series: