Home

Awesome

DeNet

A simple extendable library for training and evaluating Deep Convolutional Neural Networks focussing on image classification and detection.

Requirements

Installation

DeNet currently requires the development version of Theano:

  1. Download Theano git e.g.

    git clone https://github.com/Theano/Theano.git

  2. Revert to revision used in the papers (might not be neccessary):

    git checkout fadc8be492628dcef4760ab4cfcd147824a8226f

  3. Setup PYTHONPATH environment variable:

    export PYTHONPATH=theano_git_dir:$PYTHONPATH

Probably want to add this command to your .bashrc so it doesn't have to be run repetitively.

Features

Training

Training neural networks is performed via either "bin/model-train" or "bin/model-train-multi" for single / multi GPU implementation. Arguments are mostly the same for both implementations. The GPU to use for "model-train" is specified by exporting the "GPU=N" global variable (see examples), for "model-train-multi" GPUs are specified with the "--gpus" argument.

Note: the initial run of "model-train-multi" simply produces a "model-dims.json" file... you must rerun the command to begin training!

Example - Training simple 3 layer CNN on CIFAR10 / CIFAR100:

GPU=0 bin/model-train --border-mode half --activation relu  --model-desc C[128,3] BN A P[2] C[256,3] BN A P[2] C[512,3] BN A P.A R  --learn-rate 0.1 --solver sgd --epochs 30 --train TRAIN --test TEST --extension png

where TRAIN and TEST are the train and test folders for CIFAR10 / CIFAR100

NOTE: see examples/ for a more comprehensive list of examples

Datasets:

Datasets are specified using the "--train FNAME" and "--test FNAME" arguments.

A number of different formats are supported including:

The ImageNet, MSCOCO and Pascal VOC share the image_loader.py interface which enables background loading of the dataset and additional augmentation schemes. For large datasets I recommend a similar implementation.

Augmentation is either specified via the image_loader interface (occuring as the image is loaded from disk) or with the "--distort-mode" argument (operates on preloaded images). The basic distort-mode arguments are:

Models Desc:

Model layers are specified in a feedforward manner via the "--model-desc" argument and take the format "TYPE.TAGS[ARGS]" where TYPE defines the layer type and TAGS are optional single character switchs (specific to each type) and ARGS defines the arguments to supply (if any). In most cases not all ARGS must be specified!

Common Layer Types:

Please refer to parse_desc() in the layer .py files for all arguments e.g. see /src/layer/batch_norm.py

Pretrained Models

See MSCOCO and VOC2007 to download pretrained DeNet models.

MSCOCO Results:

ModelEval. RateMAP@IoU=[0.5:0.95]MAP@IoU=0.5MAP@IoU=0.75
DeNet34 skip (v1)82 Hz29.5%47.7%31.1%
DeNet34 wide (v1)44 Hz30.0%48.9%31.8%
DeNet101 skip (v1)33 Hz32.3%51.4%34.6%
DeNet101 wide (v1)17 Hz33.8%53.4%36.1%
DeNet34 wide (v2)80 Hz33.4%49.8%35.8%
DeNet101 wide (v2)21 Hz36.8%53.9%39.3%
DeNet101 wide x768 (v2)11 Hz39.5%58.0%42.6%

v1 models are from the original paper

v2 models are from the 2nd paper and include Joint fitness NMS, bounded IoU loss and corner clustering

The DeNet101wide x768 (v2) model is the DeNet101 wide (v2) model evaluated with 768x768 pixel input images and 1296 sample RoIs

Evaluation rate is for Titan X (Maxwell) GPU, Cuda v8.0 and CuDNN v5110. Note that variations in these versions can cause the MAP to fluctuate a bit e.g. +- 0.2%

Modifying models

Use the model-modify application to modify parameters for existing models and layers. e.g. to run prediction with 768x768 input images and 1296 RoIs use:

model-modify --input MODEL --output MODEL_X768 --image-size 768 768 --modify-layer denet-sparse sample_num=36
GPU=0 model-predict --batch-size 8 --thread-num 4 --predict-mode detect,mscoco --input MSCOCO_DIR --model MODEL_X768 --extension mscoco,2015-test-dev,images_per_subset=128,crop=768,scale_mode=large

Note the change to the "crop" parameter in the model-predict extension argument.

Contact