MobileNetV3
A Keras implementation of MobileNetV3 and Lite R-ASPP Semantic Segmentation (Under Development).
According to the paper: Searching for MobileNetV3
Requirement
- Python 3.6
- Tensorflow-gpu 1.10.0
- Keras 2.2.4
Train the model
The config/config.json file provide a config for training.
Train the classification
The dataset folder structure is as follows:
| - data/
| - train/
| - class 0/
| - image.jpg
....
| - class 1/
....
| - class n/
| - validation/
| - class 0/
| - class 1/
....
| - class n/
Run command below to train the model:
python train_cls.py
Acknowledgement
Thank @fzyzcjy for your help in this project.
Reference
@article{MobileNetv3,
title={Searching for MobileNetV3},
author={Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang Vijay Vasudevan, Quoc V. Le, Hartwig Adam},
journal={arXiv preprint arXiv:1905.02244},
year={2019}
}
Copyright
See LICENSE for details.
Metadata
Release files for mobilenet-v3 0.1.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mobilenet_v3-0.1.4-py3-none-any.whl | Python 3 | none | any | Details |
Release files / mobilenet_v3-0.1.4-py3-none-any.whl
| Download URL | mobilenet_v3-0.1.4-py3-none-any.whl |
|---|---|
| Size | 18.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0586a68d423ae0ce9c08cd3731f4731ce8fa0070fc0971684bd8aac27d724fc8
|
|
BLAKE2b-256 checksum How to use checksums |
6745c28efd9c4af58a2344c2c6816b6c4cbd32479cdb419b37d06e359150d6da
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.4.2 requests/2.18.4 setuptools/39.1.0 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.6.5
|
Security for mobilenet-v3 0.1.4
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.