ViT-CIFAR

PyTorch implementation for Vision Transformer[Dosovitskiy, A.(ICLR'21)] modified to obtain over 90% accuracy(, I know, which is easily reached using CNN-based architectures.) FROM SCRATCH on CIFAR-10 with small number of parameters (= 6.3M, originally ViT-B has 86M). If there is some problem, let me know kindly :) Any suggestions are welcomed!

1. Quick Start

1. Install packages

$git clone https://github.com/omihub777/ViT-CIFAR.git
$cd ViT-CIFAR/
$bash setup.sh

2. Train ViT on CIFAR-10

$python main.py --dataset c10 --label-smoothing --autoaugment

• (Optinal) Train ViT on CIFAR-10 using Comet.ml
  If you have a Comet.ml account, this automatically logs experiments by specifying your api key.(Otherwise, your experiments are automatically logged using CSVLogger.)

$python main.py --api-key [YOUR COMET API KEY] --dataset c10

2. Results

Dataset	Acc.(%)	Time(hh:mm:ss)
CIFAR-10	90.92	02:14:22
CIFAR-100	66.54	02:14:17
SVHN	97.31	03:24:23

• Number of parameters: 6.3 M
• Device: V100 (single GPU)
• Mixed Precision is enabled

2.1 CIFAR-10

• Accucary

• Loss

2.2 CIFAR-100

• Accuracy

• Loss

2.3 SVHN

• Accuracy

• Loss

3. Hyperparams

Param	Value
Epoch	200
Batch Size	128
Optimizer	Adam
Weight Decay	5e-5
LR Scheduler	Cosine
(Init LR, Last LR)	(1e-3, 1e-5)
Warmup	5 epochs
Dropout	0.0
AutoAugment	True
Label Smoothing	0.1
Heads	12
Layers	7
Hidden	384
MLP Hidden	384

4. Further improvements

• Longer training gives performance boost.
  • ViT doesn't seem to converge in 200 epochs.
• More extensive hyperparam search(e.g. InitLR/LastLR/Weight Decay/Label Smoothing/#heads...etc) definitely gives performance gain.

5. Ref.

• "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale", Dosovitskiy, A., et. al, (ICLR'21)

  • Vision Transformer paper.

• "TransGAN: Two Transformers Can Make One Strong GAN", Jiang, Y., et. al, (2021)

  • This repo is inspired by the discriminator of TransGAN.

• "Training data-efficient image transformers & distillation through attention", Touvron, H., et. al, (2021)

  • Some tricks comes from this paper.