myTiny-imageNet-classification

This repository is a minimum implementation to train a image classification network on tiny ImageNet dataset. It is recommended for beginners in learning deep learning computer vision. It has detailed instruction to train on your custom dataset.

Requirements

Ubuntu 16.04 LTS
Python 3.5
PyTorch 0.4.1+
Torchvision
TensorboardX
OpenCV
Numpy

Installation

In your command terminal, type these command below

Installing git (skip this step if git has been installed)

sudo apt-get install git

Clone this repository and go to the repository directory

git clone https://github.com/albertchristianto/myTiny-imageNet-classification
cd myTiny-imageNet-classification

Install these python libraries

sudo pip3 install opencv-python numpy tensorboardx
sudo pip3 install pytorch torchvision

You are ready to run the code

Train a Image Classification Network

To train a image classification network using this repository, you must:

Download tiny-ImageNet dataset here and extract it
Generate train.txt, validation.txt, and test.txt inside datalist folder by running the following commands

python3 utils/createDataList.py --tinyImagenetPath [TINY_IMAGENET_FOLDER_PATH]

And it is ready to train the network

python3 train.py

You can also resume your training using this following command. The checkpoint file is in the checkpoint folder.

python3 train.py --resume --checkpoint_dir [YOUR_CHECKPOINT_PATH]

To monitor the training loss, you can run this following command on another command window and open your browser to access the provided address

tensorboard --logdir [YOUR_CHECKPOINT_PATH]

Test a Image Classification Network

To test a image classification, you can run this following command

python3 test.py --val_txtPath [PATH_TO_VALIDATION_TXT_FILE] --weight_path [PATH_TO_YOUR_PTH_FILE]

Inference Code

To see the classification result of a image, run the following command

python3 demo.py ----img_Path [PATH_TO_A_IMAGE_FILE] --weight_path [PATH_TO_YOUR_PTH_FILE]

Training on Custom Dataset

To train a image classification on your custom dataset, you can follow these steps:

Set your dataset format look like this

${DATA_ROOT}
|-- YOUR_DATASET
    |-- train
        |-- your_class_0
            |-- 000000000009.jpg
            |-- ... 
        |-- your_class_1
            |-- 000000000139.jpg
            |-- ...
        ...
    |-- validation
        |-- your_class_0
            |-- 000000000009.jpg
            |-- ... 
        |-- your_class_1
            |-- 000000000139.jpg
            |-- ...
        ...

Generate your train.txt, val.txt, and test.txt for your dataset using this format. For reference, you can see here.

{DATA_ROOT}/YOUR_DATASET/train/your_class_0/000000000009.jpg 0
{DATA_ROOT}/YOUR_DATASET/train/your_class_1/000000000139.jpg 1
{IMG_PATH} {LABEL}
...

Run this following command

python3 train.py --train_txtPath [PATH_TO_TRAIN_TXT_FILE] --val_txtPath [PATH_TO_VALIDATION_TXT_FILE]

For advanced training, run this command to see the training option

python3 train.py -h