carrier-of-tricks-for-classification-pytorch

carrier of tricks for image classification tutorials using pytorch. Based on "Bag of Tricks for Image Classification with Convolutional Neural Networks", 2019 CVPR Paper, implement classification codebase using custom dataset.

• author: hoya012
• last update: 2020.08.06
• supplementary materials (blog post written in Korean)

0. Experimental Setup (I used 1 GTX 1080 Ti GPU!)

0-1. Prepare Library

pip install -r requirements.txt

0-2. Download dataset (Kaggle Intel Image Classification)

• Intel Image Classification

This Data contains around 25k images of size 150x150 distributed under 6 categories. {'buildings' -> 0, 'forest' -> 1, 'glacier' -> 2, 'mountain' -> 3, 'sea' -> 4, 'street' -> 5 }

0-3. Download ImageNet-Pretrained Weights (EfficientNet, RegNet)

• facebook_research_pycls
• download RegNetY-1.6GF and EfficientNet-B2 weights

1. Baseline Training Setting

• ImageNet Pretrained ResNet-50 from torchvision.models
• 1080 Ti 1 GPU / Batch Size 64 / Epochs 120 / Initial Learning Rate 0.1
• Training Augmentation: Resize((256, 256)), RandomHorizontalFlip()
• SGD + Momentum(0.9) + learning rate step decay (x0.1 at 30, 60, 90 epoch)

python main.py --checkpoint_name baseline;

1-1. Simple Trials

• Random Initialized ResNet-50 (from scratch)

python main.py --checkpoint_name baseline_scratch --pretrained 0;

• Adam Optimizer with small learning rate(1e-4 is best!)

python main.py --checkpoint_name baseline_Adam --optimizer ADAM --learning_rate 0.0001

2. Bag of Tricks from Original Papers

Before start, i didn't try No bias decay, Low-precision Training, ResNet Model Tweaks, Knowledge Distillation.

2-1. Learning Rate Warmup

• first 5 epochs to warmup

python main.py --checkpoint_name baseline_warmup --decay_type step_warmup;
python main.py --checkpoint_name baseline_Adam_warmup --optimizer ADAM --learning_rate 0.0001 --decay_type step_warmup;

2-2. Zero gamma in Batch Normalization

• zero-initialize the last BN in each residual branch

python main.py --checkpoint_name baseline_zerogamma --zero_gamma ;
python main.py --checkpoint_name baseline_warmup_zerogamma --decay_type step_warmup --zero_gamma;

2-3. Cosine Learning Rate Annealing

python main.py --checkpoint_name baseline_Adam_warmup_cosine --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup;

2-4. Label Smoothing

• In paper, use smoothing coefficient as 0.1. I will use same value.
• The number of classes in imagenet (1000) is different from the number of classes in our dataset (6), but i didn't tune them.

python main.py --checkpoint_name baseline_Adam_warmup_cosine_labelsmooth --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1;
python main.py --checkpoint_name baseline_Adam_warmup_labelsmooth --optimizer ADAM --learning_rate 0.0001 --decay_type step_warmup --label_smooth 0.1;

2-5. MixUp Augmentation

• MixUp paper link
• lambda is a random number drawn from Beta(alpha, alpha) distribution.
• I will use alpha=0.2 like paper.

python main.py --checkpoint_name baseline_Adam_warmup_mixup --optimizer ADAM --learning_rate 0.0001 --decay_type step_warmup --mixup 0.2;
python main.py --checkpoint_name baseline_Adam_warmup_cosine_mixup --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --mixup 0.2;
python main.py --checkpoint_name baseline_Adam_warmup_labelsmooth_mixup --optimizer ADAM --learning_rate 0.0001 --decay_type step_warmup --label_smooth 0.1 --mixup 0.2;
python main.py --checkpoint_name baseline_Adam_warmup_cosine_labelsmooth_mixup --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1 --mixup 0.2;

3. Additional Tricks from hoya012's survey note

3-1. CutMix Augmentation

• CutMix paper link
• I will use same hyper-parameter (cutmix alpha=1.0, cutmix prob=1.0) with ImageNet-Experimental Setting

python main.py --checkpoint_name baseline_Adam_warmup_cosine_cutmix --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --cutmix_alpha 1.0 --cutmix_prob 1.0;

3-2. RAdam Optimizer

• RAdam Optimizer paper link

python main.py --checkpoint_name baseline_RAdam_warmup_cosine_labelsmooth --optimizer RADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1;
python main.py --checkpoint_name baseline_RAdam_warmup_cosine_cutmix --optimizer RADAM --learning_rate 0.0001 --decay_type cosine_warmup --cutmix_alpha 1.0 --cutmix_prob 1.0;

3-3. RandAugment

• RandAugment paper link
• I will use N=3, M=15.

python main.py --checkpoint_name baseline_Adam_warmup_cosine_labelsmooth_randaug --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1 --randaugment;

3-4. EvoNorm

• EvoNorm paper link
• EvoNorm paper review post(Korean Only)

python main.py --checkpoint_name baseline_Adam_warmup_cosine_labelsmmoth_evonorm --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1 --norm evonorm;

3-5. Other Architecture (EfficientNet, RegNet)

• EfficientNet paper link
• EfficientNet paper review post(Korean Only)
• RegNet paper link

• I will use EfficientNet-B2 which has similar acts with ResNet-50
  • But, because of GPU Memory, i will use small batch size (48)...
• I will use RegNetY-1.6GF which has similar FLOPS and acts with ResNet-50

python main.py --checkpoint_name efficientnet_Adam_warmup_cosine_labelsmooth --model EfficientNet --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1;
python main.py --checkpoint_name efficientnet_Adam_warmup_cosine_labelsmooth_mixup --model EfficientNet --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1 --mixup 0.2;
python main.py --checkpoint_name efficientnet_Adam_warmup_cosine_cutmix --model EfficientNet --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --cutmix_alpha 1.0 --cutmix_prob 1.0;
python main.py --checkpoint_name efficientnet_RAdam_warmup_cosine_labelsmooth --model EfficientNet --optimizer RADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1;
python main.py --checkpoint_name efficientnet_RAdam_warmup_cosine_cutmix --model EfficientNet --optimizer RADAM --learning_rate 0.0001 --decay_type cosine_warmup --cutmix_alpha 1.0 --cutmix_prob 1.0;

python main.py --checkpoint_name regnet_Adam_warmup_cosine_labelsmooth --model RegNet --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1;
python main.py --checkpoint_name regnet_Adam_warmup_cosine_labelsmooth_mixup --model RegNet --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1 --mixup 0.2;
python main.py --checkpoint_name regnet_Adam_warmup_cosine_cutmix --model RegNet --optimizer ADAM --learning_rate 0.0001 --decay_type cosine_warmup --cutmix_alpha 1.0 --cutmix_prob 1.0;
python main.py --checkpoint_name regnet_RAdam_warmup_cosine_labelsmooth --model RegNet --optimizer RADAM --learning_rate 0.0001 --decay_type cosine_warmup --label_smooth 0.1;
python main.py --checkpoint_name regnet_RAdam_warmup_cosine_cutmix --model RegNet --optimizer RADAM --learning_rate 0.0001 --decay_type cosine_warmup --cutmix_alpha 1.0 --cutmix_prob 1.0;

4. Performance Table

• B : Baseline

• A : Adam Optimizer

• W : Warm up

• C : Cosine Annealing

• S : Label Smoothing

• M : MixUp Augmentation

• CM: CutMix Augmentation

• R : RAdam Optimizer

• RA : RandAugment

• E : EvoNorm

• EN : EfficientNet

• RN : RegNet

Algorithm	Test Accuracy
B from scratch	86.47
B	89.07
B + A	94.13
B + A + W	94.57
B + A + W + C	94.20
B + A + W + S	93.67
B + A + W + C + S	93.67
B + A + W + M	94.03
B + A + W + S + M	94.27
B + A + W + C + S + M	93.73
:------------:	:------------:
BAWC + CM	94.20
BWCS + R	93.97
BAWCS + RA	93.93
BAWCS + E	93.53
BWC + CM + R	94.27
:------------:	:------------:
EN + AWCSM	94.07
EN + AWC + CM	94.33
EN + WCS + R	94.50
EN + WC + CM + R	94.33
:------------:	:------------:
RN + AWCSM	94.57
RN + AWC + CM	94.83
RN + WCS + R	94.37
RN + WC + CM + R	94.90

• Tip: I recommend long training epoch if you use many regularization techniques (Label Smoothing, MixUp, RandAugment, CutMix, etc). Remember that i use just 120 epoch.
  • reference: "Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network", 2020 arXiv

5. How to run all of experiments?

• see gpu_history.sh

6. Code Reference

• Gradual Warmup Scheduler: https://github.com/ildoonet/pytorch-gradual-warmup-lr
• Label Smoothing: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Classification/ConvNets/image_classification/smoothing.py
• MixUp Augmentation: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Classification/ConvNets/image_classification/mixup.py
• CutMix Augmentation:https://github.com/clovaai/CutMix-PyTorch
• RAdam Optimizer: https://github.com/LiyuanLucasLiu/RAdam
• RandAugment: https://github.com/ildoonet/pytorch-randaugment
• EvoNorm: https://github.com/digantamisra98/EvoNorm
• ImageNet-Pretrained EfficientNet, RegNet: https://github.com/facebookresearch/pycls