- Add Yolo V2 (darknet19) Model to
research/object_detection. - Configure a Mask R-CNN Network with Mobilenet as Backbone / Feature Extractor in
research/object_detection. - Train mask_rcnn_mobilenet_v1_coco on the COCO Dataset
- Fix Pre- and Postprocessing Deviceplacement for SSD and Faster R-CNN Models on CPU
- Add Option to evaluate either on all checkpoints and/or only on CPU (for low resources users)
- Deploy mask_rcnn_mobilenet_v1_coco on a Nvidia Jetson Tx2 on CPU
- Deploy mask_rcnn_mobilenet_v1_coco on a Nvidia Jetson Tx2 on GPU
- Rule the World
This repository contains a number of different models implemented in TensorFlow:
The official models are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read. We especially recommend newer TensorFlow users to start here.
The research models are a large collection of models implemented in TensorFlow by researchers. They are not officially supported or available in release branches; it is up to the individual researchers to maintain the models and/or provide support on issues and pull requests.
The samples folder contains code snippets and smaller models that demonstrate features of TensorFlow, including code presented in various blog posts.
The tutorials folder is a collection of models described in the TensorFlow tutorials.
If you want to contribute to models, be sure to review the contribution guidelines.