release

DeepDetect Server comes with a REST API that makes the building of Deep Learning Web and other applications easy. By Deep Learning application here we refer to either or both of: Training: Training deep neural network from image, videos, text, CSV or other data, including training from scratch, re-training and finetuning (aka. transfer learning) Inference: Running one or more deep neural networks, collecting and using results. This includes running the so-called DeepDetect chains of models, i.

DeepDetect release v0.17.0 DeepDetect v0.17.0 was released a few weeks agao. Below we review the main features, fixes and additions. DeepDetect v0.17 was released few weeks ago. It has the novel Visformer architecture, and new image data augmentation with Torch in C++, among other things.https://t.co/jy42d1zXMf The DeepDetect is fully Open Source and continues to bring our real-world DL advances to production — jolibrain (@jolibrain) June 1, 2021 In summary Improved image data augmentation for image classification and object detection models with the Torch backend Improved inference batching for object detectors with torch C++ Visformer architecture for large-scale image classification Fixes & Improvements fix to RetinaNet training torch graph fix when loading weights Docker images CPU version: docker pull jolibrain/deepdetect_cpu:v0.

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.

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.

DeepDetect v0.13 was released last week, with improved multi-model chaining on ARM and CPU hardware. This is considered useful for embedded AI applications and CPU cloud instances alike.https://t.co/i1J96Q1YR1#deeplearning #AI #ARM — jolibrain (@jolibrain) January 29, 2021 DeepDetect release v0.13.0 DeepDetect v0.13.0 was released a couple weeks ago. Below we review the main features, fixes and additions. In summary: NCNN backend for efficient and lightweight inference on ARM and CPUs: Support for batches of multiple images in inference Ability to use NCNN models with multi-model chains Updated to the latest NCNN code Basic image data augmentation for vision models trained with the Torch backend Improvements to the NCNN backend NCNN is a great library for neural network inference on ARM and embedded GPU devices.

DeepDetect release v0.12.0 DeepDetect v0.12.0 was released recently. Here we briefly review the main novel features and important release elements. In summary: Vision Transformers support with two new ViT light architectures Torchvision image classification models NCNN improved inference for image models State-of-the-art time-series forecasting with N-BEATS New local high-throughput REST API server with OATPP DeepDetect release v0.12 with support for Vision Transformers (ViT) for image classification, improved N-BEATS for time-series and new OATPP webserver #DeepLearning #PyTorch https://t.