TensorFlow 2 meets the Object Detection API

Posted by , Google Research
At the TF Dev Summit earlier this year, we mentioned that we are making more of the TF ecosystem compatible so your favorite libraries and models work with TF 2.x. Today we are happy to announce that the compatible. If you are a frequent visitor to the paper by Zhou et al, and (2) for all of the models provided as TF2 style object-based checkpoints

Access to and and , and introduce TF2 backbones implemented in Keras. Then depending on the version of TensorFlow that a user is running, these models will be either enabled or disabled.

Leverage community-maintained existing backbone implementations. Instead of re-implementing backbone architectures (e.g. MobileNet or ResNet) in Keras, our models depend on implementations in the .

No changes to the frontend config language. In order to make migration from TF1 to TF2 as easy as possible for our users, we’ve worked hard to ensure that model specifications using OD API’s config language produce equivalent model architectures in both TF1 and TF2 and that models can be trained to the same level of numerical performance under both TF versions. As an example, if you have an existing ResNet-50 based RetinaNet model config that is trainable using TF1 binaries, then to train the same model with TF2 binaries, you would simply change the name of the feature extractor in the config (in this case from ssd_resnet50_v1_fpn to ssd_resnet50_v1_fpn_keras); all other hyperparameter specifications would remain unchanged.

This release is just one example of making the TF ecosystem TF2 compatible and easier to use. Over the next few months, we will continue to migrate large-scale codebases from TF1 to TF2. In addition, we are working to provide a more integrated, end-to-end experience in the TF ecosystem for researchers looking for easy-to-use modeling, starting with a unified computer vision library coming soon.
As always, please feel free to reach out with questions and feedback via GitHub. We appreciate help from the open source community. In particular, if you are a prior TF1.x user of the TensorFlow Object Detection API and there is a feature that you really like that you don’t see supported in the TF2 pipelines, we encourage you to let us know as this may help us to prioritize as we continue to release features/models.

Acknowledgements

This release is the result of a close collaboration among a number of teams within Google Research. In particular we want to highlight the contributions of the following individuals: first, a special thanks to Tomer Kaftan and Yanhui Liang for initiating this entire effort and doing most of the early heavy lifting. We also thank our main OD API contributors: Vighnesh Birodkar, Ronny Votel, Zhichao Lu, Yu-hui Chen, Sergi Caelles Prat, Jordi Pont-Tuset, Austin Myers. We are also grateful to many other contributors including: Sudheendra Vijayanarasimhan, Sara Beery, Shan Yang, Anjali Sridhar, Kathy Ruan, Karmel Allison, Allen Lavoie, Lu He, Yixin Shi, Derek Chow, David Ross, Pengchong Jin, Jaeyoun Kim, Jing Li, Mingxing Tan, Dan Kondratyuk, Kaushik Shivakumar, Yiming Shi and Tina Tian. Finally we also thank our interns and summer of code students for their contributions: Kathy Ruan, Kaushik Shivakumar, Yiming Shi, Vishnu Banna, Akhil Chinnakotla, and Anirudh Vegesana.