Awesome
Weakly Supervised Data Augmentation Network
This is the official TensorFlow implementation of WS-DAN.
Compatibility
-
The code is tested using Tensorflow r1.x under Ubuntu 16.04 with Python 2.x and Python 3.x.
-
Recommend Environment: Anaconda
Requirements
$ git clone git@github.com:tau-yihouxiang/WS_DAN.git
$ cd WS_DAN
$ python setup.py install
- opencv, tqdm
$ conda install -c menpo opencv
$ pip install tqdm
Datasets and Pre-trained models
| Datasets | #Attention | Pre-trained model |
|---|---|---|
| CUB-200-2011 | 32 | WS-DAN |
| Stanford-Cars | 32 | WS-DAN |
| FGVC-Aircraft | 32 | WS-DAN |
Inspiration
The code is based on the Tensorflow-Slim Library.
Preparing Datasets
Download and pre-process images and labels to tfrecords.
The convert_data.py will generate ./tfrecords folder blow the provided $dataset_dir
- CUB-200-2011 Bird folder structure
-Bird
└── Data
└─── tfrecords
└─── images.txt
└─── image_class_labels.txt
└─── train_test_split.txt
└─── images
└─── ****.jpg
$ python convert_data.py --dataset_name=Bird --dataset_dir=./Bird/Data
- Stanford-Cars folder structure
-Car
└── Data
└─── tfrecords
└─── devkit
| └─── cars_train_annos.mat
| └─── cars_test_annos_withlabels.mat
└─── cars_train
| └─── ****.jpg
└─── cars_test
└─── ****.jpg
$ python convert_data.py --dataset_name=Car --dataset_dir=./Car/Data
- FGVC-Aircraft folder structure
-Aircraft
└── Data
└─── tfrecords
└─── fgvc-aircraft-2013b
└─── ***
$ python convert_data.py --dataset_name=Aircraft --dataset_dir=./Aircraft/Data
Running training
ImageNet pre-trained model
Download imagenet pre-trained model inception_v3.ckpt and put it blow folder ./pre_trained/
DATASET="Bird"
TRAIN_DIR="./$DATASET/WS_DAN/TRAIN/ws_dan_part_32"
MODEL_PATH='./pre_trained/inception_v3.ckpt'
python train_sample.py --learning_rate=0.001 \
--dataset_name=$DATASET \
--dataset_dir="./$DATASET/Data/tfrecords" \
--train_dir=$TRAIN_DIR \
--checkpoint_path=$MODEL_PATH \
--max_number_of_steps=80000 \
--weight_decay=1e-5 \
--model_name='inception_v3_bap' \
--checkpoint_exclude_scopes="InceptionV3/bilinear_attention_pooling" \
--batch_size=12 \
--train_image_size=448 \
--num_clones=1 \
--gpus="3"\
--feature_maps="Mixed_6e"\
--attention_maps="Mixed_7a_b0"\
--num_parts=32
Running testing
DATASET="Bird"
TRAIN_DIR="./$DATASET/WS_DAN/TRAIN/ws_dan_part_32"
TEST_DIR="./$DATASET/WS_DAN/TEST/ws_dan_part_32"
python eval_sample.py --checkpoint_path=$TRAIN_DIR \
--dataset_name=$DATASET \
--dataset_split_name='test' \
--dataset_dir="./$DATASET/Data/tfrecords" \
--eval_dir=$TEST_DIR \
--model_name='inception_v3_bap' \
--batch_size=16 \
--eval_image_size=448\
--gpus="2"\
--feature_maps="Mixed_6e"\
--attention_maps="Mixed_7a_b0"\
--num_parts=32
Visualization
$ tensorboard --logdir=/path/to/model_dir --port=8081
Contact
Email: yihouxiang@gmail.com
Other Re-implementation
License
MIT