DeiT - Data-efficient image transformers & distillation through attention (paper illustrated)

Summary

The script discusses 'Data-Efficient Image Transformer' (DEIT), a breakthrough in training transformers for image tasks with limited data. It highlights DEIT's superior performance over Vision Transformer (ViT), requiring less data and compute power. The paper introduces knowledge distillation techniques, using a teacher network to enhance the student model's learning. DEIT's architecture, including class and distillation tokens, is explained, along with the effectiveness of hard distillation and various data augmentation strategies. The summary emphasizes the importance of a high-quality teacher network and the potential of standalone transformer architectures in the future.

Takeaways

• 📈 The paper 'Data-Efficient Image Transformer' (DEIT) shows it's feasible to train transformers on image tasks with less data and compute power than traditional methods.
• 💡 DEIT introduces knowledge distillation as a training approach for transformers, along with several tips and tricks to enhance training efficiency.
• 🏆 DEIT outperforms Vision Transformer (ViT) significantly, requiring less data and compute power to achieve high performance in image classification.
• 🔍 DEIT is trained on ImageNet, a well-known and much smaller dataset compared to the in-house dataset used by ViT, making it more practical for limited data scenarios.
• 🕊️ Knowledge distillation involves transferring knowledge from a 'teacher' model to a 'student' model, with the teacher providing guidance to improve the student's learning.
• 🔥 A key feature of DEIT is the use of 'distillation tokens' from the teacher network, which, when combined with class tokens, leads to improved performance.
• 🔑 The teacher network in DEIT is a state-of-the-art CNN pre-trained on ImageNet, chosen for its high accuracy to enhance the student model's learning.
• 🔄 DEIT employs various data augmentation techniques such as repeat augmentation, auto augment, random erasing, mix-up, and cut mix to improve model robustness.
• 🔧 Regularization techniques are used in DEIT to reduce overfitting and ensure the model learns the actual information from the data rather than noise.
• 📊 The paper includes ablation studies that demonstrate the effectiveness of distillation tokens and the various augmentation strategies used in training DEIT.
• 🚀 DEIT represents a significant advancement in making transformer models more accessible and efficient for image classification tasks with limited resources.

Q & A

What is the main contribution of the 'Data-Efficient Image Transformer' (DEIT) paper?

The DEIT paper introduces a practical approach to train transformers for image tasks using distillation, and it provides various tips and tricks to make the training process highly efficient. It demonstrates that DEIT outperforms Vision Transformer (ViT) with less data and compute power.

How does DEIT differ from the original Vision Transformer (ViT) in terms of training dataset size?

ViT was trained on a massive in-house dataset from Google with 300 million samples, while DEIT is trained using the well-known ImageNet dataset, which is 10 times smaller.

What is the significance of knowledge distillation in the context of DEIT?

Knowledge distillation is a key technique in DEIT where knowledge is transferred from a pre-trained teacher network (a state-of-the-art CNN on ImageNet) to the student model (a modified transformer), enhancing the student model's performance with less data.

How does the distillation process in DEIT differ from traditional distillation?

In DEIT, the distillation process uses a state-of-the-art CNN as the teacher network and employs hard distillation, where the teacher network's label is taken as the true label, rather than using a temperature-smoothed probability.

What is the role of the 'temperature parameter' in the softmax function during distillation?

The temperature parameter in the softmax function is used to smoothen the output probabilities. A lower temperature makes the probabilities more confident, while a higher temperature makes them more uniform.

What are the different variations of DEIT models mentioned in the script?

The script mentions DEIT-ti (a tiny model with 5 million parameters), DEIT-s (a small model with 22 million parameters), DEIT-b (the largest model with 86 million parameters, similar to ViT-b), and DEIT-b-384 (a model fine-tuned on high-resolution images of size 384x384).

Which teacher network does DEIT use, and why is it significant?

DEIT uses a state-of-the-art CNN from the NeurIPS 2020 paper with the highest accuracy on ImageNet. The better the teacher network, the better the trained transformer will perform, as it transfers knowledge effectively.

What are some of the augmentation and regularization techniques used in DEIT to improve training?

DEIT employs techniques such as repeat augmentation, auto augment, random erasing, mix-up, and cut mix to create multiple samples with variations and reduce overfitting.

How does the script describe the effectiveness of using distillation tokens in DEIT?

The script indicates that distillation tokens, when used along with class tokens, bring better accuracy compared to using class tokens alone, although the exact contribution of distillation tokens is still to be fully understood.

What are the implications of the results presented in the DEIT paper for those with limited compute power?

The results suggest that DEIT can produce high-performance image classification models with far less data and compute power compared to ViT, making it a practical solution for those with limited resources.

What does the script suggest about the future of standalone transformer architectures?

The script suggests that while DEIT relies on a pre-trained teacher network, the community is eagerly waiting for a standalone transformer architecture that can be trained independently without depending on other networks.

Outlines

00:00

📈 Introduction to Data-Efficient Image Transformers (DEIT)

The first paragraph introduces the concept of Data-Efficient Image Transformers (DEIT), a breakthrough in training transformer models on image data with significantly reduced data and computational resources. The DEIT approach is contrasted with the Vision Transformer (ViT), which requires a massive dataset and substantial computational power. DEIT's efficiency is attributed to a novel training method involving knowledge distillation, regularization, and augmentation techniques. The paragraph also emphasizes the practicality of DEIT for those with limited compute resources, highlighting its ability to achieve high performance with a fraction of the data and training time compared to ViT.

05:02

🔍 Understanding DEIT's Training Techniques and Model Architecture

This paragraph delves into the specifics of DEIT's training techniques, focusing on knowledge distillation as a key method for transferring knowledge from a pre-trained teacher network to a student model. It explains the process of distillation, including the use of a temperature parameter to smoothen output probabilities and the concept of hard distillation where the teacher network's label is used as the true label. The paragraph also discusses the architecture of DEIT, including the use of class tokens, patch tokens, and distillation tokens, and how these elements contribute to the model's improved performance. Additionally, it mentions the different variations of the DEIT model, such as DEIT-ti, DEIT-s, and DEIT-b, each with varying parameters and capabilities.

10:03

🏆 The Impact of Teacher Networks and Training Strategies on DEIT's Success

The final paragraph discusses the importance of the teacher network in the DEIT framework, revealing that a state-of-the-art convolutional neural network pre-trained on ImageNet is used. It underscores the correlation between the quality of the teacher network and the performance of the trained transformer. The paragraph also reviews the effectiveness of various augmentation and regularization strategies employed during training, such as repeat augmentation, auto augment, random erasing, mix-up, and cut mix, which are crucial for achieving DEIT's impressive results. It concludes with an acknowledgment of the audience's patience and an invitation for feedback, reflecting the community-driven nature of the discussion.

Keywords

💡Transformer

A transformer is a type of deep learning architecture that was initially designed for natural language processing tasks. In the context of the video, it refers to the application of transformer models to image classification tasks, which is a significant shift from their traditional use in text. The script discusses how the 'Vision Transformer' (ViT) and 'Data-Efficient Image Transformer' (DEIT) utilize this architecture for computer vision, showcasing its versatility and effectiveness in processing image data.

💡Data-Efficient Image Transformer (DEIT)

DEIT is a specific transformer model that the video focuses on, which is designed to be trained on a smaller dataset compared to traditional models like the Vision Transformer. The script highlights that DEIT outperforms the Vision Transformer while requiring less data and compute power, making it a practical solution for those with limited resources. DEIT incorporates knowledge distillation and other techniques to achieve high performance with reduced training requirements.

💡Knowledge Distillation

Knowledge distillation is a technique where a smaller, 'student' model learns from a larger, 'teacher' model. In the script, this concept is central to how DEIT is trained. The student model learns from the teacher model's output, which is softened by a temperature parameter to smoothen the probabilities. This approach allows DEIT to learn effectively even with limited data, as it benefits from the knowledge already captured by the teacher model.

💡Regularization

Regularization is a strategy used in machine learning to prevent overfitting by reducing the complexity of the model. In the video, regularization techniques are mentioned as part of the strategies DEIT uses to ensure that the model generalizes well to new, unseen data. The script does not detail specific regularization methods but implies their importance in the training process of DEIT.

💡Augmentation

Augmentation refers to the process of creating modified versions of the training data by applying various transformations, such as rotations, scaling, or cropping. The script mentions several types of augmentation techniques used in DEIT, including repeat augmentation, auto augment, rand augment, random erasing, mix-up, and cut mix. These techniques help the model learn more robust features from the data and improve its performance.

💡Vision Transformer (ViT)

The Vision Transformer is a pioneering model that demonstrated the efficacy of transformers in computer vision tasks. The script contrasts ViT with DEIT, noting that ViT requires a massive dataset and extensive compute power for training, making it less practical for those with limited resources. ViT serves as a benchmark against which the efficiency and performance of DEIT are measured.

💡Teacher Network

In the context of knowledge distillation, the teacher network is a pre-trained model that provides guidance to the student model. The script specifies that DEIT uses a state-of-the-art convolutional neural network pre-trained on ImageNet as its teacher. The quality of the teacher network is crucial, as it directly impacts the performance of the student model, which is why DEIT benefits from using a high-accuracy teacher.

💡Student Model

The student model is the model being trained in the knowledge distillation process, learning from the teacher model. In the script, the student model is a modified version of the transformer architecture used in DEIT. The student model's output is trained alongside the distillation loss from the teacher network to improve its learning efficiency and accuracy.

💡Cross-Entropy Loss

Cross-entropy loss is a common loss function used in classification tasks, measuring the difference between the predicted probabilities and the true labels. The script mentions that DEIT uses cross-entropy loss in conjunction with the distillation loss from the teacher network to train the student model. This combination helps the model to learn both from the direct supervision of the true labels and the softened guidance of the teacher network.

💡Temperature Parameter

The temperature parameter in the context of the softmax function is used to control the smoothness of the output probabilities. The script explains that with distillation, the temperature helps in softening the probabilities from the teacher network before they are used to compute the distillation loss. This softening is crucial for the student model to learn effectively from the teacher's output.

💡Ablation Study

An ablation study is a research method used to understand the contribution of individual components of a system by systematically removing or modifying them. The script refers to an ablation study conducted in the DEIT paper, which helps to understand the impact of various techniques such as distillation tokens, class tokens, and different training strategies on the model's performance.

Highlights

Training transformers on images is practical, as shown by the Data-Efficient Image Transformer (DEIT).

DEIT proposes knowledge distillation as a training approach for transformers.

The paper provides tips and tricks to make transformer training efficient.

DEIT outperforms Vision Transformer (ViT) with less data and compute power.

ViT requires a massive dataset from Google, while DEIT uses the smaller ImageNet.

DEIT's training time is significantly reduced to two to three days on a 4-8 GPU machine.

Understanding DEIT requires knowledge of distillation, regularization, and augmentation.

Distillation involves transferring knowledge from a teacher network to a student model.

Regularization aims to reduce overfitting and improve model generalization.

Augmentation creates varied samples from the same input to enhance model robustness.

DEIT uses a modified distillation approach with a pre-trained CNN as the teacher network.

The student architecture in DEIT is a modified transformer that incorporates CNN outputs.

Hard distillation is used in DEIT, taking the teacher network's label as the true label.

DEIT's architecture includes class tokens, patch tokens, and distillation tokens.

Experiments show that distillation tokens significantly improve DEIT's performance.

DEIT comes in various sizes: DEIT-ti, DEIT-s, DEIT-b, and DEIT-b/384 for different needs.

The teacher network used in DEIT is a state-of-the-art CNN from the NeurIPS 2020 paper.

Better teacher networks lead to better trained transformers in DEIT.

Distillation is more effective than soft distillation, achieving higher accuracy.

DEIT's success relies on various augmentation and regularization strategies.

Repeat augmentation, auto augment, rand augment, mix-up, and cut mix are among the techniques used.

The paper's ablation studies reveal the contribution of each technique to DEIT's performance.

The standalone transformer architecture trained independently is still awaited.

Transcripts

training data efficient

image transformer or date in short is

one of the first papers to show that

it is practical to train transformers

for tasks

on images the paper not only proposes

distillation as a training approach to

train transformers

but also provides a bunch of tips and

tricks to make the training

super efficient the paper shows

a straight comparison of vision

transformer with date

and clearly date outperforms vision

transformer by a good margin

not only that date requires far less

data

and far less compute power to produce a

high performance image classification

model

for those of us who only have limited

compute power

and are wondering how to train the

wisdom transformer on a custom data set

date is the answer let's learn about

date in this video

[Music]

vision transformer or vit was the first

paper

which showed that transformers can be

used for computer vision tasks

it trained on a massive data set of 300

million samples

and the data set is an in-house data set

from google

and it's not it available to download on

the other hand

date is trained only using the

well-known imagenet

which is a 10 times smaller data set

because of the massive dataset size

vision transformer

needs extensive compute power for

training making it

impractical to train models in the

limited data regime

on the other hand the training time for

date is two to three days

on a single 4 gpu or 8 gpu machine

now this is an impressive leap in

performance so let's delve

deeper and try to understand date much

better

to understand date we need to know

distillation

regularization and augmentation

so knowledge distillation is when you

transfer knowledge

from one model or network to another

network

by some means regularization

is when you try to reduce overfitting of

a network

to given limited training data so that

your model

does not learn the noise in the data but

the actual

information from the data augmentation

is when we create multiple samples of

the same input

with some variations though these are

some of the techniques used in date the

key contributor

is distillation so let's recap

distillation first

and see how it is used in this paper

let's say we have a neural network in

classic machine learning setting

that recognizes cats and dogs to train

this network

we first pass the cat image through the

model and get the representation

or embeddings of the image the embedding

is then passed through

a softmax function to get the

probabilities

for the input classes doc and cad

we then compute a cross entropy loss

comparing

with the ground with labels and train

the entire network

with distillation we distill the

knowledge from another network

called the teacher network or the

teacher model

we first get the embeddings from the

teacher network

we now pass it through a softmax with a

special

temperature parameter tau to get the

output probabilities

the significance of temperature is to

smoothen

the output probabilities for instance if

the softmax function

says the probability of cat is 0.9

with the temperature set to 0.5 it only

says the probability of cat

as 0.7 with the output of the teacher

network

we compute a distillation loss between

the teacher output

and the ground truth and sum the loss

with the cross

entropy loss of our student model in

order to join the

student model

date proposes a modified version of

distillation approach we just saw

the teacher network that they use is a

state-of-the-art convolutional neural

network

that is pre-trained on imagenet the

student architecture

is the modified version of transformer

the main modification is that the output

of the cnn

is also passed as an input to the

transformer

while computing the distillation loss we

do what is called the hard distillation

where the temperature is equal to one

what it means is that we literally take

the label of the teacher network

as the true label we then sum up this

distillation loss

with a cross entropy of the transformer

and trying the transformer

[Music]

now with that information if we take a

look at this figure from the paper

i think we gain a better understanding

of date so the class tokens and patch

tokens is the same as the vision

transformer

they are put through several layers of

attention and we obtain the classes

on top of that we also have the

distillation tokens

which are complementary to the class

tokens but come from the teacher network

the authors have experimented and shown

in the paper that

distillation token trained with a

teacher token

is what provides the impressive

improvement in performance

they've also experimented with several

variations of this data architecture

date ti is a tiny model with 5 million

parameters

date s is a small model with 22 million

parameters

date b is the largest model and is the

same as vision transformer b

with 86 million parameters

date b 384 is the model fine tuned on

high resolution

training images of size 384 by 384

and finally the date with this symbol

stands for the proposed distillation

procedure

[Music]

now i've been mentioning about the

teacher network which is a convolutional

neural network

but which network do they use the answer

is that they use a state-of-the-art

network proposed in this new ribs 2020

paper

and they went for the biggest 16 gf

model which has the highest accuracy of

82.9 on imagenet

why because the better the teacher

network

the better our trained transformer will

be

as can be seen from the results heart

distillation

seems to be quite effective compared to

soft distillation

as it reaches an accuracy of 83 percent

that is not possible otherwise we can

also observe that the distillation

tokens bring

better accuracy when used along with

class tokens

instead of just using the class tokens

as in the vision transformer

lastly increasing the training ebox and

training for longer time

somehow seems to be more effective when

it comes to transformers

[Music]

until this paper training a transformer

on images wasn't easy

so they should have adapted quite a few

tricks and strategies

to train the transformer successfully

this table

summarizes some of the augmentation and

regularization

tricks that they used in order to arrive

at the impressive results proposed let's

briefly

look at each of them

repeat augmentation is when we first

augment images in a batch

and use all the images together in this

simple example

of a batch of a dog and a cat

we augment them and make a batch of four

images instead of two

auto augment is when you search for the

best augmentation policy

for the given data rather than manually

defining

some augmentations irrespective of the

data

rand augment can be implemented in two

lines of code

as shown here the idea is that you

randomly choose

n augmentations from a pool of

augmentations

and simply use the chosen ones

random erasing is super easy too you

randomly erase a rectangular patch in

the input image

and use the erased image in case of

mix-up you add up or do some arithmetic

of the inputs

to arrive at a new training sample

in cut mix you cut out a region from a

given

input and stick in another sample with

different label

and modify the label accordingly

so to summarize these are the tricks

that that were experimented in the paper

and in this ablation studies table they

present the results of use

using each of them while the paper says

that distillation tokens

bring something to the table we are yet

to figure out what exactly it is that

contributes to the better performance

clearly it's bringing something quite

different from the class tokens

also let's not forget that the teacher

network used here

is already trained on imagenet

so clearly we have to wait to see a

standalone transformer architecture

trained independently without depending

on any other networks

while we eagerly wait for that

transformer architecture

i would like to thank you so much for

watching patiently

till the end and supporting the

community by

leaving your comments below thank you

very much

you