/ConvNext-2020s

Primary LanguagePython

A ConvNet for the 2020s

Our implementation of paper: A ConvNet for the 2020s, using tensorflow 2

This library is part of our project: Building an AI library with ProtonX

ConvNeXt Architecture :

Authors:

Advisors:

Reviewers:

I. Set up environment

  • Step 1: Make sure you have installed Miniconda. If not yet, see the setup document here.

  • Step 2: Clone this repository: git clone https://github.com/protonx-tf-04-projects/ConvNext-2020s

II. Set up your dataset

  1. Download the data:
  • Download dataset here
  1. Extract file and put folder train and validation to ./data
  • train folder was used for the training process
  • validation folder was used for validating training result after each epoch

This library use ImageDataGenerator API from Tensorflow 2.0 to load images. Make sure you have some understanding of how it works via its document Structure of these folders in ./data

train/
...cats/
......cat.0.jpg
......cat.1.jpg
...dogs/
......dog.0.jpg
......dog.1.jpg

validation/
...cats/
......cat.2000.jpg
......cat.2001.jpg
...dogs/
......dog.2000.jpg
......dog.2001.jpg

III. Training Process

Review training on colab:

Training script:

!python train.py --train-folder ${train_folder} --valid-folder ${valid_folder} --num-classes ${num-classes} --image-size ${image-size} --lr ${lr} --batch-size ${batch-size} --model ${model} --epochs ${epochs}

Example:

!python train.py --train-folder $train_folder --valid-folder $valid_folder --num-classes 2 --image-size 224 --lr 0.0001 --batch-size 32 --model tiny --epochs ${epochs}

There are some important arguments for the script you should consider when running it:

  • train-folder: The folder of training data
  • valid-folder: The folder of validation data
  • model-folder: Where the model after training saved
  • num-classes: The number of your problem classes.
  • batch-size: The batch size of the dataset
  • image-size: The image size of the dataset
  • lr: The learning rate
  • model: Type of ConvNeXt model, valid option: tiny, small, base, large, xlarge

IV. Predict Process

python predict.py --test-data ${link_to_test_data}

V. Result and Comparision

Your implementation

Epoch 195: val_accuracy did not improve from 0.81000
63/63 [==============================] - 74s 1s/step - loss: 0.1756 - accuracy: 0.9300 - val_loss: 0.5760 - val_accuracy: 0.7930
Epoch 196/200
63/63 [==============================] - ETA: 0s - loss: 0.1788 - accuracy: 0.9270
Epoch 196: val_accuracy did not improve from 0.81000
63/63 [==============================] - 74s 1s/step - loss: 0.1788 - accuracy: 0.9270 - val_loss: 0.5847 - val_accuracy: 0.7890
Epoch 197/200
63/63 [==============================] - ETA: 0s - loss: 0.1796 - accuracy: 0.9290
Epoch 197: val_accuracy did not improve from 0.81000
63/63 [==============================] - 74s 1s/step - loss: 0.1796 - accuracy: 0.9290 - val_loss: 0.5185 - val_accuracy: 0.7840
Epoch 198/200
63/63 [==============================] - ETA: 0s - loss: 0.1768 - accuracy: 0.9290
Epoch 198: val_accuracy did not improve from 0.81000
63/63 [==============================] - 74s 1s/step - loss: 0.1768 - accuracy: 0.9290 - val_loss: 0.5624 - val_accuracy: 0.7870
Epoch 199/200
63/63 [==============================] - ETA: 0s - loss: 0.1744 - accuracy: 0.9340
Epoch 199: val_accuracy did not improve from 0.81000
63/63 [==============================] - 74s 1s/step - loss: 0.1744 - accuracy: 0.9340 - val_loss: 0.5416 - val_accuracy: 0.7790
Epoch 200/200
63/63 [==============================] - ETA: 0s - loss: 0.1995 - accuracy: 0.9230
Epoch 200: val_accuracy did not improve from 0.81000
63/63 [==============================] - 74s 1s/step - loss: 0.1995 - accuracy: 0.9230 - val_loss: 0.4909 - val_accuracy: 0.7930

VI. Feedback

If you meet any issues when using this library, please let us know via the issues submission tab.