Awesome
transfer-mxnet
Unsupervised transfer learning for image classification written in mxnet.
This is a library for unsupervised transfer learning using mxnet. We mainly implemented three algorithms:
mmd
described in paper "Learning Transferable Features with Deep Adaptation Networks".jmmd
described in paper "Deep Transfer Learning with Joint Adaptation Networks".AdaBN
described in paper "REVISITING BATCH NORMALIZATION FOR PRACTICAL DOMAIN ADAPTATION".
For original caffe implementation of mmd
and jmmd
, please refer to here
If you have any problem about this code, feel free to concact us with the following email:
Note that this repo is only for unsupervised image classfication transfer learning.
Experiments
We introduce our experiments on cars dataset:
Source dataset
is a high quality cars image dataset fetched from web with accurate annotated labels(car models).Target dataset
is a surveillance image dataset which is public available(compcars-sv).
During training, we set all labels in Target dataset
to null-label(9999 by default) then it becomes unsupervised TL problem.
Method | Accuracy |
---|---|
CNN(no TL) | 68.7% |
AdaBN | 71.6% |
DAN(mmd) | 73.7% |
JAN(jmmd) | 78.9% |
If you want to train your own models with mmd(especically JAN as it is the best approach), please use following steps.
Data Preparation
- Download mxnet resnet-152 imagenet-11k pretrained model to
model/
directory, from here. - Prepare your source domain dataset to
data/source.lst
in mxnetlst
format. - Prepare your target domain dataset to
data/target.lst
. Generally, label id intarget.lst
should be null-label(9999 by default). But semi-supervised TL is also allowed, you can choose hundreds of items to be their real valid label id. - Prepare your validation dataset in target domain to
data/val.lst
. It can be the same withdata/target.lst
but all with valid labels.
Training Model
Env variables setting.
export MXNET_CPU_WORKER_NTHREADS=15
Training stage 1, do softmax training on source dataset only.
python fine-tune.py --train-stage 0 --pretrained-model 'model/resnet-152' --pretrained-epoch 0 --model-prefix 'model/mmd' --num-classes 263 --lr 0.01 --lr-step-epochs '10,16' --num-epochs 18 --lr-factor 0.1 --gpus 0,1,2,3 --batch-size 64
Training stage 2, do softmax+mmd joint training to produce final model.
python fine-tune.py --train-stage 1 --pretrained-model 'model/mmd' --pretrained-epoch 18 --model-prefix 'model/mmd1' --num-classes 263 --lr 0.0001 --lr-step-epochs '6,8,10,12' --num-epochs 14 --lr-factor 0.5 --gpus 0,1,2,3 --batch-size 64
Parameter Tuning
TODO, check them in source code now.
Use AdaBN
python adabn.py --model <trained-model-prefix> --epoch <load-epoch> --val 'data/val.lst' --gpu 0
It will firstly calculate BN statistics using target domain dataset then write back to preloaded model. Second, use this modified model to validate the classification accuracy on target dataset. You can change the corresponding BN layers name in source code.