/InceptionV3-SSD

Tensorflow implementation of InceptionV3-SSD

Primary LanguagePythonMIT LicenseMIT

Modularized SSD implementation in TensorFlow

This repo tries to implement SSD in a modularized fashion.
Inspiration: Speed/accuracy trade-offs for modern convolutional object detectors. (arXiv:1611.10012)

Acknowledgement

This repo is based on the works:

  • SSD-Tensorflow: The Tensorflow re-implementation of Caffe SSD.
  • SSD_tensorflow_VOC: A fork of SSD-Tensorflow with bachnorm and simplified structure. This is the baseline of my implementation.

Dependencies:

  • Python 3.x
  • Tensorflow 1.x
  • CUDA 8.0
  • OpenCV 3.x

Current state

VGG16-SSD300, VGG16-SSD512, MobileNet-SSD300, MobileNet-SSD512 are trainable. For SSD300, batch_size=32 is safe with 8GB of GRAM. VGG16-SSD512 can do 20 and MobileNet-SSD512 can do 16. For fine tuning batch size must be reduced (halved would be OK).

HOWTO:

Prepare data for training

See projects in Acknowledgment. They have documented this quite well. Scripts needed are still not included in this repo.

Train only SSD layers

Download pre trained weights for the backend first. See projects in acknowledgement for reference.

Specify the training parameters in trainer/train_params.py. Pay attention to format, because if parameters are not completely defined, there will be ERROR. ssd_params_train is the reference. Then set the params to use in train_model.py. Data provider are defined in trainer/preparedata.py, change directory to your own. Change data provider in trainer/trainer.py by replacing the function get_*_*_train_data().

Fine tune feature extractor

Much like training the SSD layers, just define a set of params and specify it in train_model.py and run. ssd_finetune_params1 and ssd_finetune_params2 are references which conforms with the guideline given in SSD_tensorflow_VOC. My personal experience is, the model still improves if the training steps are extended.

Get training and test results

Use evaluate_model.py. Parameters are defined in evaluator/eval_params.py.

Demo

Run new_demo.py. All parameters need are defined inside this file. Pay attention to conformity.

Workflow for implementing a new backend for SSD

1. Prepare base network

  • A base network is the backend (i.e. feature extractor) for the SSD net, like vgg_16. Therefore vgg_16_base() in nets/vgg.py serves as a good example. The network body should only contain convolutional layers for the feature extraction and no fc layers. The base network function should have the parameter inputs and return nets, end_points. In the body of the function, a variable scope (i.e. name of the network) should be defined.

  • Define the arg_scope of the network in a separate function, like vgg_base_arg_scope(). For now only weigth_decay and data_format='NHWC' should be used as parameters.

  • If you are defining your own network, the returned net should have the size [H, W] = [64, 64] for SSD512, or [38, 38] for SSD300 in the last two layers/blocks. If you are adapting an existing net, see 4.

2. Add base network in nets/nets_factory.py
Add the file in imports and and the keys for the base network and its arg_scope in base_networks_map and base_arg_scopes_map

3. Define a training parameter set
Define it in trainer/train_params. Set fine_tune_fe=True if there is no pre trained weights to use.

4. Find the feature layer
Set breakpoint in get_model() in ssd/ssdmodel.py (see comment for more info). Set the training parameters in ./train_model.py and run train_model.py in debug mode. On the breakpoint, by reading into values of end_points find out the feature layer to use (according to SSD paper, the output of the second last layer/block should be used). Also make sure you have the right tensor shape at this step. Add an item in feature_layer in ssd/ssd_blocks.py for your base network.

5. Train your model
If you got everything right, you can now train your network with customized backend. If you adopted an existing base network which was pre trained on ImageNet, follow the training routine defined for other networks in train_params.py. If you have defined a new base network, set fine_tune_fe=True and checkpoint_path='' and train.

TODOs:

  • Rewrite core Tensorflow SSD for modularization
  • Connect original VGG backend for SSD
  • Implement test routine
  • Connect other backends for SSD
  • Train and evaluate connected backends
  • MultiGPU usage for training
Model Trained on Train mAP Tested on mAP
VGG_16-SSD300 VOC2007_train ✗ - -
VGG_16-SSD512 VOC07+12 trainval 0.649 VOC07 test 0.579
MobileNet-SSD512 ✗ ✗ - -