Skip to content

Latest commit

 

History

History
141 lines (97 loc) · 7.24 KB

README.md

File metadata and controls

141 lines (97 loc) · 7.24 KB
Raw

Fast-image-classification

Fast image classification using Bottlenecks

Image classification can be a daunting task if proper algorithms are not in use. Its well known that Deep Learning that to especially Convolutional Networks took the computer vision community in a boom. Image classification task is where a given image is put in classified into one of the classes the algorithm is trained on.

The common way to approach image classification is fine tuning (Transfer Learning) pre-trained neural networks. Fine tuning a neural network is the process of training pre-trained neural network for our specific task. This is conventionally done by just training the final layers of the CNN (keeping base layers non trainable).

While keeping the base of the network non-trainable means the weight matrices and bias matrices doenst change. In the finetuning procedure we are doing many unnecessary forward passes in the non-trainable part (base layers) which significanlty increases the time taken to finetune.

So here we do a first forward pass through the non trainable part of the network and store them (Called bottlenecks). Then with the stored file as the dataset we will be training only the trainable part in the fine-tuning process. This considerable speeds up the classification procedure.

This image summarises the entire idea of using bottlenecks in fine-tuning process.

image_describing_bottleneck_learning

Different Speedups Obtained

Model | No of Epochs | Time | Accuracy ResNet50 | 6 | 206 sec | 89.92%

Installation Instructions

cd Fast-image-classification
pip install -r requirements.txt

This will install all requirements except opencv. This you can install by

sudo apt-get install python-opencv

Docker image

In case you are looking out for docker images

docker pull tlokeshkumar/img-classifier:v1
docker run -it tlokeshkumar/img-classifier:v1
cd ~
cd Fast-image-classification

Currently this is the CPU version installed. For GPU version for this image use nvidia-docker.

Quick Start

For a quick start, you can run this script file

chmod +x sample_train.sh
./sample_train.sh

This script file will download the dataset, split it into training and validation datasets, and starts the training and also completes with sample predictions. Feel free to modify this script file to suit your needs!

Functions of different Programs

You can do bottleneck training on all the keras image classification models (keras 2.1.6). This part explains how to use different parts of this program to do classification.

Program Function
general_model.py Takes care of creating bottlenecks, reading them and training part of the fine-tuning process
general_test.py Takes care of integrating the 2 separate parts (non-trainable and trainable parts). Can be used for testing and integration with other applications.
common_network.py This contains the actual code for the non-trainable network, the user defined trainable network and also the preprocessing functions. Both general_model.py and general_test.py use the models defined here.
train_val_split.py This program can be used to split the dataset into train and validation sets. Look into the program for directory order or else there is a vulnerability of things getting messed up.

Training your own Classifier

  • First choose your base model. This is the model upon which fine tuning will happen. This can be either VGG16, ResNet, InceptionV3, Inception ResnetV2 etc.
  • Depending on your problem choose which is the bottleneck tensor. Generally the layer before the softmax activation is used as bottleneck vector. (This is the default option also). By choosing bottneck vector you must choose the layer name in the pre-trained model in keras. (Eg. activation_31 in ResNet50 basemodel can be a botteneck tensor)

After deciding them (or leaving that to defaults) do the following:

  1. In common_network.py if you would like to change the trainable part of the network include that in the function**

    def transfer_model(bottleneck, LABEL_LENGTH, weights = None):

    This function holds the definition of the trainable part of the fine-tuning process.

    By default its just a single layer feedforward network to output (Softmax is the activation function)

  2. In case your data isn't split into train and validation sets split them using train_val_split.py

    python train_val_split.py --dataset <dataset_folder> --val_split 0.2

    This code will split the dataset into training set and validation set with ratio $0.8:0.2$.

    Please follow the directory structure given in the program

  3. Once train set and validation set is present, general_model.py must be executed. This does the bottlenecks creation job and also training job and saves the checkpoint. This completes your training your classifier !!

    Sample Execution

    python general_model.py --train train_dir --val val_dir --logs log_resnet --bottleneck_dir bottlenecks  --base_model resnet50 --bottlenecks_batch_size 100 --epochs 1 --bottleneck_tensorname activation_31

This table illustrates different kinds of arguments available in this program

Argument Function
train The path to the train set
val Path to the validation set
logs Path where the log files will be stored
create_bottleneck When this flag specified, bottlenecks are created. If you have already computed the bottenecks features and stored them, donot specify this flag.
bottleneck_dir path where the bottleneck files are stored. (Generally they are hdf5 compressed)
omit_bottlenecks If this flag is specified, no bottleneck accelerated training happens. Normal finetuning occurs.
load_weights Path where the trained weights of the trainable model is present. (Used incase of further fine tuning)
bottleneck_tensorname Enter the name (layer name in keras) in the base model to select as the bottleneck layer. If not specified, the final layer (just before softmax is chosen)
base_model This is the base pre-trained model. This has all options from VGG -> ResNet -> Inception -> InceptionResNet etc. Read help command for more details.
batch_size_train Batch size for training procedure
epochs No of epochs to train
bottlenecks_batch_size Batch size used to create bottlenecks (No of images sent per step)

Testing your trained Classifier

general_test.py program specifies a sample test case. You can use that to develop the same.

Sample Execution

python general_test.py --weight_file Resnet50_top.h5 --label_file essential_files/label_map.json --img_dir val_dir/daisy --base_model resnet50 --bottleneck_tensorname activation_31

Note:

  • Bottlemeck_tensorname must be same in both training and testing to avoind clashes in shapes.
  • Base model must also be same.

Different arguments and its functions are elaborated in the program. Run this to know them.

python general_test.py --help

References

How to Retrain an Image Classifier for New Categories by tensorflow was the main motivation in doing this. Some ideas were taken from there also.

Future Work

  • Multiprocessing for bottleneck (hdf5 files) creation must be done