If you find the code useful for your research, please consider citing our latest
work:
works:
* DeepLabv3+:
```
@article{deeplabv3plus2018,
...
...
@@ -39,11 +41,21 @@ work:
}
```
* MobileNetv2:
```
@inproceedings{mobilenetv22018,
title={Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation},
author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen},
booktitle={CVPR},
year={2018}
}
```
In the current implementation, we support adopting the following network
backbones:
1. MobileNetv2 [8]: A fast network structure designed for mobile devices. **We
will provide MobileNetv2 support in the next update. Please stay tuned.**
1. MobileNetv2 [8]: A fast network structure designed for mobile devices.
2. Xception [9, 10]: A powerful network structure intended for server-side
deployment.
...
...
@@ -71,7 +83,7 @@ Some segmentation results on Flickr images:
Demo:
*<ahref='deeplab_demo.ipynb'>Jupyter notebook for off-the-shelf inference.</a><br>
*<ahref='https://colab.sandbox.google.com/github/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb'>Colab notebook for off-the-shelf inference.</a><br>
@@ -18,12 +18,49 @@ A: We have not tried this. The interested users could take a look at Philipp Kr
___
Q5: What if I want to train the model and fine-tune the batch normalization parameters?
A: Fine-tuning batch normalization requires large batch size, and thus in the train.py we suggest setting `num_clones` (number of GPUs on one machine) and `train_batch_size` to be as large as possible.
A: If given the limited resource at hand, we would suggest you simply fine-tune
from our provided checkpoint whose batch-norm parameters have been trained (i.e.,
train with a smaller learning rate, set `fine_tune_batch_norm = false`, and
employ longer training iterations since the learning rate is small). If
you really would like to train by yourself, we would suggest
1. Set `output_stride = 16` or maybe even `32` (remember to change the flag
`atrous_rates` accordingly, e.g., `atrous_rates = [3, 6, 9]` for
`output_stride = 32`).
2. Use as many GPUs as possible (change the flag `num_clones` in train.py) and
set `train_batch_size` as large as possible.
3. Adjust the `train_crop_size` in train.py. Maybe set it to be smaller, e.g.,
513x513 (or even 321x321), so that you could use a larger batch size.
4. Use a smaller network backbone, such as MobileNet-v2.
___
Q6: How can I train the model asynchronously?
A: In the train.py, the users could set `num_replicas` (number of machines for training) and `num_ps_tasks` (we usually set `num_ps_tasks` = `num_replicas` / 2). See slim.deployment.model_deploy for more details.
___
Q7: I could not reproduce the performance even with the provided checkpoints.
A: Please try running
```bash
# Run the simple test with Xception_65 as network backbone.
sh local_test.sh
```
or
```bash
# Run the simple test with MobileNet-v2 as network backbone.
sh local_test_mobilenetv2.sh
```
First, make sure you could reproduce the results with our provided setting.
After that, you could start to make a new change one at a time to help debug.
___
## References
1.**Deep Residual Learning for Image Recognition**<br/>
