For running the Tensorflow Object Detection API locally, Docker is recommended. You can install the TensorFlow Object Detection API either with Python Package Installer (pip) or Docker, an open-source platform for deploying and managing containerized applications. How to fine-tune a pre-trained model on custom data.How to run a pre-trained model on an image and a video stream.How to install the Tensorflow Object Detection API.In this article, I will walk you through the most important information of the new update, including: First-class support for keypoint estimation, including multi-class estimation, more data augmentation support, better visualizations, and COCO evaluation.įor more information, check out their blog post.Colab demonstrations of eager mode compatible few-shot training and inference.Access to DistributionStrategies for distributed training.COCO pre-trained weights for all of the models provided as TF2 style object-based checkpoints.A suite of TF2 compatible (Keras-based) models – including popular TF1 models like MobileNET and Faster R-CNN – as well as a few new architectures including CenterNet, a simple and effective anchor-free architecture based on the recent Objects as Points paper and EfficientDet – a recent family of SOTA models discovered with the help of Neural Architecture Search.New binaries for train/eval/export that are eager mode compatible.The Tensorflow Object Detection API officially supports Tensorflow 2 now. Now, the waiting has finally come to an end.
Over the last year, the Tensorflow Object Detection API (OD API) team has been migrating the OD API to support Tensorflow 2. Tensorflow Object Detection with Tensorflow 2
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