FastClassification

FastClassification is a tensorflow toolbox for class classification. It provides a training module with various backbones and training tricks towards state-of-the-art class classification.

features
backbone	ResNet50,ResNet101,ResNet152, EfficientNetB0,EfficientNetB1,..., EfficientNetB7
augment	mixup cutmix rand_augment auto_augment
tricks	Sharpness-Aware Minimization(SAM) progressive-resizing lr_finder mixed-precision warmup concat-max-and-average-pool label-smoothing dataset-sample-ratio
other	confusion_matrix data sanity check class imbalance check Precision recall show wrong prediction images tensorboard

Update Log

[2021-06-26]

• Add classic models and training tricks.

Installation

1. Clone project

git clone https://github.com/wangermeng2021/FastClassification.git
cd FastClassification

2. Install environment

• Install tesnorflow (skip this step if it's already installed,test environment:tensorflow 2.4.0)
• Install dependencies: pip install -r requirements.txt

Training:

• Set the learning rate manually:

  python train.py --init-lr 1e-3 --progressive-resizing 224 224 --dataset-dir dataset/catdog --epochs 10 --batch-size 32 --augment cutmix --weights imagenet
  

• For training with Sharpness-Aware Minimization optimizer(support SAM-SGD,SAM-Adam):

  python train.py --optimizer SAM-SGD --augment baseline --init-lr 1e-3 --progressive-resizing 224 224 --dataset-dir dataset/catdog --epochs 10 --batch-size 32  --weights imagenet
  

• For training with lr-finder:

  python train.py --init-lr 0 --progressive-resizing 224 224 --dataset-dir dataset/catdog --epochs 10 --batch-size 32 --augment cutmix --weights imagenet
  

• For training with progressive-resizing:[(128,128),[224,224]]

  python train.py --progressive-resizing 128 128 224 224  --dataset-dir dataset/catdog  --epochs 10 --batch-size 32 --augment cutmix --weights imagenet
  

• For training with mixed-precision:

  python train.py --mixed-precision True --init-lr 1e-3 --progressive-resizing 224 224 --dataset-dir dataset/catdog --epochs 10 --batch-size 32 --augment cutmix --weights imagenet
  

• For training with rand_augment:

  python train.py --augment rand_augment --init-lr 1e-3 --progressive-resizing 224 224 --dataset-dir dataset/catdog --epochs 10 --batch-size 32  --weights imagenet
  

Tensorboard visualization:

• Navigate to http://0.0.0.0:6006: you need to manually enable: "Setting"-->"Reload data" on tensorboard home page to automatically update data

Evaluation results on toy catdog:

(1000 pictures,train:valid=0.7:0.3,EfficientB0,epochs=10,batchsize=32)

model	val_acc
progressive-resizing(224x224)+SGD+baseline	0.987
progressive-resizing(224x224)+SAM-SGD+baseline_augment	0.990
progressive-resizing(224x224)+SAM-SGD+mixup	0.947
progressive-resizing(224x224)+SAM-SGD+cutmix	0.927
progressive-resizing(224x224)+SAM-SGD+auto_augment	0.980
progressive-resizing(224x224)+SAM-SGD+rand_augment	0.960
progressive-resizing(224x224)+SAM-SGD+baseline+mixed-precision	0.980
progressive-resizing(224x224)+SAM-SGD+baseline+lr-finder	0.907
progressive-resizing(128x128,224x224)+SAM-SGD+baseline	0.963
progressive-resizing(128x128,224x224)+SAM-SGD+mixup	0.936

References

• https://github.com/wcipriano/pretty-print-confusion-matrix
• https://github.com/Jannoshh/simple-sam