https://github.com/droyed/tfodtools
Tensorflow Object Detection Tools : Start to finish on object detection project in 3 commands. Add mobile inferencing.
https://github.com/droyed/tfodtools
bash object-detection script tensorflow tensorflowjs tools
Last synced: 5 months ago
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Tensorflow Object Detection Tools : Start to finish on object detection project in 3 commands. Add mobile inferencing.
- Host: GitHub
- URL: https://github.com/droyed/tfodtools
- Owner: droyed
- License: mit
- Created: 2021-09-18T06:41:57.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-09-19T20:51:42.000Z (almost 5 years ago)
- Last Synced: 2025-04-19T21:58:42.244Z (about 1 year ago)
- Topics: bash, object-detection, script, tensorflow, tensorflowjs, tools
- Language: Shell
- Homepage:
- Size: 360 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
Tensorflow Object Detection Tools
=================================
|Py-Versions| |Bash-Version| |OS| |License|
This repository is conceived with the idea of simplifying `tensorflow objection detection workflow `_ to minimize the hassles of setting up various components and maximizing automation between them. It also extends to incoporate a mobile-based inferencing of the trained models. Thus, this could work as an `end-to-end toolkit` solution for a tensorflow based objection detection project.
Steps
-----
.. code-block:: console
$ cd shell_scripts
**I.** Setup python virtual environment :
.. code-block:: console
$ bash setup_tfODenv.sh
This will interactively setup the environment and will be a one-time process. It also sets up a config file that facilitates the next steps. So, training on new data or with new models would re-use this setup.
Before proceeding with next step, let's make sure we have the input data in the required format. It's explored in detail at `Tensorflow object detection data setup - Setup images and xmls `_.
**II.** Setup data and training :
.. code-block:: console
$ bash setup_data_train.sh
This will setup everything needed for training, start it and also dynamically create `save_model.sh` and `create_mobile_optimized_model.sh` that are scripts needed later on for inferencing.
**III.** Once done with training, we can save this model with :
.. code-block:: console
$ bash save_model.sh
The saved model could then be loaded into tensorflow environment for inferencing.
**IV. (Optional)** We can also create mobile optimized version for inferencing on mobile devices with :
.. code-block:: console
$ bash create_mobile_optimized_model.sh
This will create `model.json` and `shard` files :
.. code-block:: console
.
├── group1-shard1of3.bin
├── group1-shard2of3.bin
├── group1-shard3of3.bin
└── model.json
`Setup mobile inference `_ discusses a setup workflow on using these files for a mobile based inferencing.
.. |Py-Versions| image:: https://img.shields.io/badge/Python-3.6+-blue
:target: https://github.com/droyed/tfodtools
.. |Bash-Version| image:: https://img.shields.io/badge/Bash-4.2+-green
:target: https://github.com/droyed/tfodtools
.. |OS| image:: https://img.shields.io/badge/Platform-%E2%98%AFLinux-9cf
:target: https://github.com/droyed/tfodtools
.. |License| image:: https://img.shields.io/badge/license-MIT-green
:target: https://raw.githubusercontent.com/droyed/tfodtools/master/LICENSE