Image Classification

This is for beginners to be able to easily use image classification and design of the general code, with keras implementation, It will be continuously updated! If this works for you, please give me a star, this is very important to me.😊

Introduction

Implemented Network

• [x] AlexNet
• [x] VGG
• [x] GoogleNet
• [x] ResNet
• [x] MobileNet
• [x] DenseNet
• [x] SENet
• [x] EfficientNet
• [x] InceptionV3
• [x] Xception
• [ ] ShuffeNet

You can choose any network to train, the specific configuration is in ./core/config,py.

Dataset

A dataset of five flower species.

pretrain weights

For convenience, I have uploaded the ImageNet pre-training weights to release.

Quick start

1. clone this repository

git clone https://github.com/Runist/image-classifier-keras.git

2. You need to install some dependency package.

cd image-classifier-keras
pip install -r requirements.txt

3. Download the flower dataset.

wget https://github.com/Runist/image-classifier-keras/releases/download/v0.2/dataset.zip
unzip dataset.zip

4. Start train your model.

python train.py

You will get the following output on the screen:

Downloading data from https://github.com/Runist/image-classifier-keras/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
98%[=============================>]

Preparing train resnet50.
Freeze the first 176 layers of total 177 layers. Train 50 epoch.
Epoch 1/50
  8/103 [=>............................] - ETA: 2:03 - loss: 1.9460 - accuracy: 0.1172 - lr: 1.7510e-06

5. You can run evaluate.py to watch model performance.

python evaluate.py

100%|███████████████████████| 364/364 [00:26<00:00, 13.74step/s, accuracy=0.951]
accuracy = 0.9505, precision = 0.9505, recall = 0.9516
Confusion matrix: 
 [[62  0  0  0  1]
 [ 4 85  0  0  0]
 [ 0  2 59  0  3]
 [ 0  0  0 68  1]
 [ 1  2  3  1 72]]

Other

To be continue...