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ChainerCV SegNet for MC2ARCdataset
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README.md
calc_weight.py
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README.md

segnet_arc

ChainerCV SegNet for MC2ARCdataset

Requirement

  • Python 3.x / 2.x (Recommended version >= 2.7.11 or >= 3.5.1)
  • OpenCV 3.x / 2.x
  • Chainer 2.x (Recommended version == 2.1.0)
  • ChainerCV 0.7.0
  • Pillow(PIL) (Recommended version >= 3.1.0)
  • numpy (Recommended version >= 1.10)
  • matplotlib (Recommended version >= 1.4)

Usage

1. Download the dataset

Please download the full-dataset(png) from the following URL.

After the download completes, unzip "ARCdataset_png.zip".

2. Prepare the dataset for semantic segmentation

You need preparing the dataset for SegNet. It is saved to new directory. please change make_data.py as below.

# Original dataset dir (Source)
original_dataset = "<Your dataset path>"

# Dataset dir for SegNet (Destination)
segnet_dataset = "<New dataset path>"

original_dataset is path of your dataset which was downloaded. segnet_dataset is new dataset path. For example, <Your dataset path>/for_segnet/ would be good.

After setting path, Please run make_dataset.py.

$ python make_dataset.py

3. Get class weight

Please change readARCdataset.py as below.

root = "<Your dataset path>"

This is same as segnet_dataset of make_data.py.

And please run calc_weight.py.

$ python calc_weight.py

You can get class_weight.npy which is necessary file for training.

4. Training

Please run train.py.

$ python train.py

You can use following argments.

  • --gpu (default -1) : # of GPU. Negative value indicates CPU.
  • --batchsize (default 8)
  • --class_weight (default 'class_weight.npy')
  • --out (default 'result') : Output path of training results and models.

Note that default setting is NOT using GPU. If you want to use GPU, please run as below.

$ python train.py --gpu <GPU ID>

5. Testing

Please run test.py.

$ python test.py --model <Trained model path> --output <segmentation result path>

You can use following argments.

  • --gpu (default -1) : # of GPU. Negative value indicates CPU.
  • --model : training model path.
  • --input ( default /test)
  • --output : Output path of segmented images(results).

--model is path of training model which was saved in result (or --out of train.py). It was named model_iteration-XXXXX.

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