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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

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Image Classification-Deep Learning-For Beginner

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