Multi-Label Fashion Image Classification with Minimal Human Supervision
Naoto Inoue1 Edgar Simo-Serra2 Toshihiko Yamasaki1 Hiroshi Ishikawa2
1
The University of Tokyo 2 Waseda University
inoue@hal.t.u-tokyo.ac.jp

esimo@aoni.waseda.jp

Abstract
We tackle the problem of multi-label classification of
fashion images, learning from noisy data with minimal human supervision. We present a new dataset of full bodyposes, each with a set of 66 binary labels corresponding
to the information about the garments worn in the image
obtained in an automatic manner. As the automaticallycollected labels contain significant noise, we manually correct the labels for a small subset of the data, and use these
correct labels for further training and evaluation. We build
upon a recent approach that both cleans the noisy labels
and learns to classify, and introduce simple changes thatcan significantly improve the performance.

yamasaki@ay-lab.org

hfs@waseda.jp

Label

Confidence

Stockings& Tights

0.99

Scarf

0.98

Mustard0.95

Blazer

0.93

Gray

0.90

Black

0.89

Boots

0.82

Dress

0.76

Red
..

0.38
..

Figure 1: Example of fashion image classification results
provided by our model. We achieve this performance by
combining several thousand manual annotations with our
automatically-collected large-scale very noisy dataset.

1. Introduction
the quality of the annotations in the dataset. We first extend the Fashion144k dataset [20] by crawling over 1M images with metadata, which contains weak labels, from Chictopia1 . To evaluate the recognition performance, we perform an experiment in multi-label classification, as shownin Fig. 1, and predict the colors and the garments worn by
the person in each image. To evaluate the quality of annotations, we randomly pick 5,300 images in our new dataset,
which we denote as Fashion550k, and manually correct the
weak labels to obtain “clean” labels. We use these limited
annotations to both evaluate and improve the recognition
accuracy. Note that the number of the labels in this subset is quite small, compared to the whole dataset. We build
upon the approach of Veit et al. [25] that uses a neural network to clean the labels by learning a mapping between the
noisy labels and clean labels. By learning this mapping, it
is possible to afterwards jointly clean the dataset and train aprediction model. We evaluate this approach and show thatwith some simple modifications we are able to significantly
improve performance.
To summarize, our contributions are as follows:

Fashion plays an important role in everyday lifestyle,yet understanding fashion is still a very complicated task
for computer vision. In particular, due to the subjectiveness of the fashion, obtaining high-quality data for training learning-based models for fashion task remains an open
problem. In this work, we analyze the importance of thequantity of the data and evaluate the modern tools for automatically cleaning the data in the context of multi-label
prediction.
Unlike more standard computer vision tasks, the fashion
domain is variable and subjective, and networks pre-trainedon large-scale datasets such as ImageNet and Places are
not necessarily best-suited for fashion-related tasks. SimoSerra and Ishikawa [21] introduced Fashion144k, which
consists of fashion images in a variety of scenes with weaklabels. Furthermore, they showed that exploiting a large
number of fashion images with only weak labels and designing a CNN to learn from them can provide a better feature representation than ImageNet or Places for one of the
fashion-related tasks, i.e., fashion style classification. Thus,
obtaining a larger-scale fashion image dataset is desirable
as a basis for fashion recognition tasks. We examine two
aspects that have an impact on the recognition performanceon fashion image datasets: (i) the size of the dataset and (ii)1

Dataset available at http://hi.cs.waseda.ac.jp/˜esimo/
data/fashion550k/.

1

• We present a large-scale weakly-labeled fashion image dataset that includes 5,300 human-annotated images for analyzing the label noise.
• We investigate the effect of the size of the training data
for multi-label fashion classification.
• We evaluate the methods to exploit weak labels jointlywith human-verified data.

(a) Fashion550k

2. Related Work2.1. Fashion
There are many fashion-related problems in computer
vision research, such as semantic segmentation of garments [19, 29, 31, 13, 12], image retrieval [8, 5, 33],
fine-grained classification of garments and styles [2, 3,
14, 10, 30], fashion landmark detection [15], and imageconditional domain transfer [32]. Predicting more subjective properties such as popularity [26] and fashionability [20] have also been studied. Many of these approaches
collected large scale image datasets by crawling the Internet. They also exploit weak labels [20, 28] or manually annotate the images by crowd-sourcing [14]. In this paper, weconstruct the Fashion550k dataset that consists of fashion
images with weak annotations, and evaluate the influenceof noisy annotations. We believe this dataset is also usefulfor other tasks such as fashionability estimation and clothing parsing (as a weak supervision).

2.2. Learning from Noisy Data
There are two approaches to learn from noisy labeleddata with a neural network: the first approach aims to directly learn from noisy labels and focus mainly on constructing a model that takes noise into consideration. The
simplest approach is to model a label noise that is conditionally independent of the input image [18, 23]. Xiao et
al. [27] and Misra et al. [17] proposed image-conditioned
noise model. However, these methods face the challengeof distinguishing difficult samples from mislabeled training
samples, and rely on heuristic-based noise models.
Another approach is called semi-supervised approach,which is to learn from noisy labeled data with neural network is to combine a small set of clean labels. Veit et al.
[25] proposed to learn the mapping between noisy and clean
labels and then to exploit the mapping for training deep neural networks by imitating the student-teacher models usedin [16, 7]. In this approach, a base CNN is used as a feature extractor and is combined with two additional networks
consisting of a label cleaning network and an image classifier. The label cleaning network is input the noisy labels
in addition to the visual features, and is trained to predictcleaned labels that are provided as a privileged information.
The output of the cleaning network is regarded as the target(b) DeepFashion

Figure 2: Example images from our Fashion550k and DeepFashion [14]. Images in Fashion550k often have fullyvisible centered individuals with natural and diverse background. On the other hand, DeepFashion is constructed torecognize details and landmarks of each clothing. Thus,
images in DeepFashion are focused on each item and withmore clear backgrounds and surroundings.
label to predict for the image classifier. This approach canhandle both incorrect and missing labels without assuming
the type of noise and greatly boost the classification performance. We evaluate this approach, propose simple changes
to improve the performance, and furthermore propose an alternative scheme in which the confidence of the label cleaning network is used.

3. Dataset
We extended the Fashion144k dataset [20]. The extended dataset, called Fashion550k, consists of 550,661
user posts from the clothing-oriented website Chictopia.
Fashion550k is about four times the size of the original
Fashion144k. In Fig. 2, example images of Fashion550k
are shown. Each post contains at least a single image withmetadata including label tags. Each photograph shows adifferent angle of the user or a zoom-in on different garments. Users often add a short description of what they are
wearing and tags of the types and colors of the garments.
Note that this information is very noisy: e.g., not all garments are tagged, and some users make only a part of theinformation available. We parsed all the information provided with each post. For a representative statistics of thedataset, refer to Table 1.
Since Fashion550k has been collected without any filtering, some of the images are not suitable for learning. For
example, some images focus on only one object, or havestrong filters. Thus, we cleaned the data following the ap-

Total (Unique)

Per user

Per post

posts
users
photostags

550,661
15,217
1,061,468
3,627

36.19 (71.41)
1.76 (0.90)
1.98 (1.55)

1.93 (1.26)
2.14 (2.03)

proach of [21]. In [21], to filter out such images the authorsfine-tune a VGG16 model [22] pre-trained on ImageNet forthe binary classification task of whether or not given image
is suitable for training. They achieve about 94% accuracy.
By directly using this classifier on our crawled images, we
obtain images with a fully-visible centered individual. Additionally, we filtered out all images with no tags, to obtain
407,772 images in total.
As weak annotations, we utilize tags that consist ofcolour-garment pairs, such as blue-jeans and red-sweater.
We split the tags into colors and garments, resulting ina total of 123 unique weakly-annotated tags. However,some tags, such as orange and carrot-orange, or watch andbracelet, are hard to visually discriminate under various illumination and lighting conditions. Furthermore, some tags
are hard to detect because occurrence is minimal or because
they are highly occluded, as in the case of iPhone-case andearrings. Thus, we discard or merge such tags, resulting ina total of 66 unique weakly-annotated tags. The classes inthis dataset are not evenly distributed, as shown in Fig. 3a.
The class black has more than 200,000 annotations, whereas
the class tie has only 1,435 annotations.
We additionally verified the tags in a subset of the collected images to provide minimal supervision. The numberof the images verified was 3,000 for training, 300 for validation, and 2,000 for testing. The rest of the images (called thenoisy dataset) are used to train the baseline classifier. We
estimate the quality of the noisy labels using the verified
tags for the 5,300 images. Fig. 3b shows the distributionof the quality of the original noisy labels. The noise occurs
regardless of the class frequency. We observe that 26.4% of
the original labels are false positives. Further, we also observe that originally positive labels accounts for only 54.2%of the labels actually verified positive. These observations
show that directly using these weak labels as ground truth
will lead to poor performance, which we will later validatein our experiments.

4. Proposed Method
In this paper, our goal is to train a multi-label image
classifier on a large dataset with extremely noisy labels,
where additionally a small subset of the dataset that has
human-verified labels available. This setting can often hap-

10

10

10

6

5

4

3

0

10

20

30

40

50

60

Class index(a) Class frequencies.

Share of labels positively verified

Property

10

Class frequencies

Table 1: Statistics of the dataset. The number between thebrackets indicates standard deviation of each property.

1.0

0.8

0.6

0.4

0.2

0.0
0

10

20

30

40

50

60

Class index(b) Quality of original labels.

Figure 3: Label statistics for Fashion550k. We show andorder by the class frequency, and represent the quality of theoriginal labels as the accuracy of the noisy labels comparedto manually cleaned labels.

pen when we collect images from the web or social media
and have experts to correct some of the labels.
Formally, we have a very large training dataset T . T
consists of tuples of noisy labels y and images x, T ={(xi , yi ), . . .}. Additionally, we have a small dataset V
with human verified labels v, V = {(xj , yj , vj ), . . .}. The
number of the data in T is significantly larger than that in
V . In our experiments, T exceeds V in the number of databy two orders of magnitude. Each y and v is a sparsed-dimensional vector with a binary annotation for each of
the d classes indicating whether it is present in the image ornot. Our aim is to fully utilize the accurate annotation verified by a human in V and noisy but huge number of labels
in T .

4.1. Multi-Task Label Cleaning Network
We base our approach on the model of Veit et al. [25], inwhich a label cleaning network is used in combination witha classification network. We next briefly summarize [25].
4.1.1 Model architecture
The network is designed to jointly learn to generate accurate
labels from noisy labels and to learn a more accurate multilabel classifier from the generated labels. An overview ofthe model is shown in Fig. 4. There are two classifiers g
and h on top of a CNN-based feature extractor f .
The first classifier g, shown on the bottom of Fig. 4, iscalled the label cleaning network. It learns a mapping fromnoisy labels y to human-verified labels v, conditional on the
input image. Its output c denotes the cleaned labels. The
classifier g has two separate inputs, the noisy labels y andthe visual features f (x). Each input is projected into an embedding by a linear layer and the two are concatenated, thentransformed with a hidden linear layer. Finally, y is added
to the output by an identity-skip connection and clipped to[0, 1] to remain in the valid label space. In short, c is com-𝒙

Noisy labels𝒚LinearSigmoid

Predicted
labels𝒑LinearReLU

Linear𝒚Base CNN

Label Cleaning Network

LinearLinear

Image
𝒙

Base
CNN

Image Classifier

Cleanedlabels𝒄＋𝒚𝒙𝒙Base CNN

Base CNN

ImageclassifierLabelcleaning
networkLabelcleaning networkwith ReLU+BN

Image
classifier

𝒑

𝒄

𝒄

𝒑

(b) Phase1(c) Phase2(a) Veit et al.

Figure 4: Overview of the multi-task label cleaning networkin [25]. Dashed arrow in the figure indicates a data flow
without gradient back-propagation.
puted as follows:

Figure 5: Comparison between Veit et al. and the proposed
method.
The cleaned labels ci is regarded as constant in order to prevent a trivial solution ci = pi = 0.

4.2. Proposed Method

c = max(min(g(f (x), y) + y, 1), 0)(1)

The second classifier h is called the image classifier. It
learns to predict labels by imitating the first classifier using
only the image as input. The image classifier h is shown in
the top row of Fig. 4. It is composed of a linear layer followed by a sigmoid as an activation function. We denotethe predicted labels by p. It is a d-dimensional vector in[0, 1]d and computed by p = h(f (x)). It indicates the likelihood of the visual presence of the d classes and is used toevaluate the quality of the whole network.
4.1.2 Training strategy
Two losses are used to train the model: the label cleaningloss Lclean to enhance the quality of the cleaned labels cand the classification loss Lclassify to enhance the quality ofthe predicted labels p.
The label cleaning network is supervised by the verified labels of all samples j in the human verified set V .
The cleaning loss is based on the L1 -distance between thecleaned labels yj and the verified labels vj .
X
|cj − vj |(2)
Lclean =j∈V

The classification network is also supervised by two
terms. For all samples i from the noisy subset T , the image
classifier is supervised by the cleaned labels ci that is produced by the cleaning network. For samples j from verified
dataset V , we can directly supervise pi by verified labels vi .
For both terms, a cross-entropy loss is used.
XX
pi log(ci )(3)pj log(vj ) −Lclassify = −
j∈V

i∈T

In Veit et al., as shown in Fig. 5a, the two networks aretrained simultaneously. However, they assume a large number of verified labels actually. They use about 40K images
with verified labels in their paper. If we have much smaller
number of verified labels, the label cleaning network tends
to overfit.
Thus, we propose to improve the label cleaning network. To avoid overfitting, ReLU and batch normalization(BN) [9] are added after each linear layer in the label cleaning network. Since this network tends to overfit even withthis modifications, we use the validation subset of Fashion550k to find the best model and do early stopping.
Further, for more stable training, we also propose to separate the whole training process into two phases. We train
the label cleaning network on a pre-trained base CNN asshown in Fig. 5b. Further, we freeze the whole network andfeed all images in the noisy subset and get cleaned labels c.
Subsequently, we train the image classifier on another pretrained base CNN as shown in Fig. 5c. As target labels, v isused for the training subset, and c is used for the noisy subset. The loss used in our method is the same as employed in
Section 4.1.2.

5. Experimental Results
We train our model on the noisy and training subset, findthe best model and hyper-parameter using the validation
subset, and evaluate on the test subset in Fashion550k. We
evaluate our approach using multi-label classification. For
each of the 66 target classes, we predict a score which indicates the likelihood of the concept described by the classpresenting in the image.
As evaluation metrics, the class-agnostic average precision (APall ), and the mean of the each class-average preci-

sion (mAP ) are used. APall regards every annotation for allclasses equally by handling them as coming from one single
class and thus is biased towards more frequent classes.

Table 2: Comparison of the models evaluated by the test
subset of Fashion550k. We use all 3000 images withcleaned labels for the training. (noisy:clean ≃ 136 : 1)

5.1. Baseline and Compared Methods

Model

APall

mAP

As the baseline model for our evaluation, we train a CNN
on the noisy labels from Fashion144k and Fashion550k.
117,746 and 402,472 images are used to train on Fashion144k and Fashion550k, respectively. This model iscalled Baseline and is used as the initial weight for all
the other compared methods except ours. The Baselinemodel is based on a 50-layer ResNet [6] pre-trained on ImageNet [4]. The last softmax layer of the network is replacedwith a 66-way sigmoid layer to predict the probability ofeach of the labels.
We compare the following approaches:
Fine-tune with clean labels. Most common approachwith cleaned labels is to feed the clean labels directly tothe network and supervise the last layer. However, this approach is prone to overfitting, since the number of clean labels is limited and cannot utilize huge noisy labels fully.
Fine-tune with mix of clean and noisy labels. This approach handles the insufficient training examples. We finetune the last layer of the base CNN with a combination oftraining samples from the small clean and the large noisy
subset (in a 1 to 9 ratio).
Veit et al. [25]. This approach utilizes the clean labels
using the label cleaning network. There are two variants ofthis method, “with pre-training” and “trained jointly”. In“with pre-training”, first, we train just the label cleaning
network. Subsequently, the classification network and theimage classifier are jointly trained. In this training, a learning rate of the classification network is smaller (×10−1 )than the image classifier. In “trained jointly”, the classification network and the image classifier are trained jointly
from the beginning. A difference with the original implementation is that in this case we opt to use the ResNet50 [6]as the Base CNN instead of the Inception v3 model [24].
For further details, please refer to the original paper.
Improved Model. This approach only implements ourmodification of adding ReLU and BN to the label cleaning
network, which is proposed in Section 4.2. This model istrained in the same way as original method of Veit et al.
Ours. This approach implements both using Improved
Model and the two-phase training shown in Fig. 5b andFig. 5c. To reduce the computational overhead, we initialize
the base-CNN and the prediction network with the weightof Fine-tune with clean labels.

Baseline (Fashion144k)
Baseline (Fashion550k)
Fine-tuning with mixed labels
Fine-tuning with clean labels
Veit et al. (pretrain)
Veit et al. (joint)
Improved Model (pretrain)
Improved Model (joint)
Ours

62.23
69.18
72.38
79.39
78.60
78.92
80.01
79.70
79.87

49.66
58.68
61.50
64.04
62.81
63.08
64.34
64.03
64.62

5.2. Training Details
All approaches are implemented using PyTorch [1] andoptimized with Adam [11] by a batch size of 64. All theimages are resized to 256×256. In the training phase, theimages are then randomly cropped into 224×224 with random horizontal flipping. In the test phase, the images are
center-cropped into 224×224. We employed early stopping
using APall on the validation subset.
The baseline network is initially trained with the binary
cross-entropy loss between the noisy labels and the predictions of the network for 100,000 iterations using an initial
learning rate of 10−3 . For fine-tuning, we use a learning
rate of 10−4 for the last linear layer and 10−5 for the otherlayers, for additional 20,000 iterations.
The other variants are trained for additional 20,000 iterations. In this paper, we regard the ResNet except the last
two layers, i.e., linear layer and sigmoid layer, as the baseCNN. The last two layers are regarded as the image classifier. For the label cleaning network, the arrangement of thelinear layers are the one used in Veit et al. as in Fig. 4 andthe number of filters for the each linear layer is set to 512.
We use an initial learning rate of 10−5 for the base-CNN
and 10−4 for the other layers. To train the cleaning network
and image classifier jointly, we sample training batches that
contain samples from T as well as V in a ratio of 9:1. Tobalance the losses, we weight Lclean with 0.1 and Lclassify
with 1.0 for the variants of Veit et al., which is the same as
the parameters used in the paper.

5.3. Results
We first discuss the overall performance of the proposed
method in Table 2. The performance regarding APall ishigher than mAP . This means that the AP for popularclasses is higher. Training solely on the noisy labels from
our Fashion550k, which is about four times bigger than
Fashion144k [20], shows significant performance gain andshows about +7% improvement in APall and about +9%
improvement in mAP compared to training solely on noisylabels from Fashion144k. This result suggests that collecting larger dataset is still necessary for the recognition performance improvement.

X
X
X
X

X
X
X
X

Ours

Top5
predictions
Dress
Bag & Purse
Sunglasses
White
Pink
Sandals
Brown
Skirt
Black
Stockings& Tights
Brown
Red
Shirt
Ruby-red
Shoes
Dress
Black
Red
Orange
Brown
Socks
Shoes

Fine-tuningwithclean labels

Image fromthe test subset

Baseline

Table 3: Examples from the test subset in Fashion550k.
We show the top 5 most confident predictions along withwhether the prediction is correct or incorrect. Our approachconsistently removes false predictions.

X
X
X
X
X

×
×
X

X
×
×
×
X
X
X
×

X
X

X
X

X
X

X
X
X

×

X
X
X
×

X
X
X
×
X

×
×

Then we conduct an analysis on combining noisy andclean labels. Simply fine-tuning with cleaned labels improves APall and mAP significantly because Fashion550k
is very noisy. We would like to emphasize that the two variants of the original Veit et al. do not work and are evenworse than fine-tuning with clean labels. Further, there is
still certain improvement in improved model and ours inboth APall and mAP over fine-tuning with clean labels.
This shows the importance of carefully designing the label
cleaning network, which results in over 1% improvement
both in APall and mAP compared to the original methodof Veit et al. Notably, ours achieved best mAP . The difference in improved models and ours are not so remarkable.
This suggests that the order of the training is less important.
In Table 3, the example results of classification are shown.
We perform an analysis on a harder setting, where we
only have access to only 1,000 cleaned annotations. The gap
between fine-tuning with clean labels and the label cleaning
network based methods are clear as shown in Table 4. Notably, the performance improvement by the proposed meth-

Table 4: Comparison of the models evaluated by thetest subset of Fashion550k when we are restricted touse only 1000 cleaned annotations for the training.
(noisy:clean ≃ 407 : 1)
Model

APall

mAP

Baseline (Fashion550k)
Fine-tuning with clean labels
Improved Model (pretrain)
Improved Model (joint)
Ours

69.18
78.03
78.83
78.70
78.58

58.68
62.48
62.75
62.85
62.60

Table 5: Comparison of the models in the test subset of
Fashion550k when we are restricted to use all 3000 images
with cleaned labels and noisy labels only from Fashion144k
for the training. (noisy:clean ≃ 39 : 1)
Model

APall

mAP

Baseline (Fashion144k)
Fine-tuning with clean labels
Improved Model (pretrain)
Improved Model (joint)
Ours

62.23
74.90
74.99
74.45
74.79

49.66
57.79
57.36
56.53
57.28

ods, i.e., +0.6% ∼ +0.8% in APall is not subtle comparedto the improvement when we use 3 times larger number ofcleaned labels , i.e., +1.4% in APall , as shown in Table 2.
We perform another analysis, where we only have accessto Fashion144k for noisy labels in Table 5. Here, APall andmAP get worse compared to fine-tuning with clean labels
in almost all proposed methods. This result suggests thatthe label cleaning network-based approach does not work if
we have relatively enough cleaned annotations.

6. Conclusion
We have presented a new large-scale weakly-labelled
dataset for multi-label classification of garments of full poseimages, providing a small subset of validated annotations
for evaluation. We provide experimental evaluation of theeffect of the number of images and number of clean annotations, along with various variants of multi-task classification
networks that make use of the clean and noisy annotations.
Results show that a few simple modifications are able to improve the performance of previous approaches significantly,
and larger amounts of noisy data are useful for improving
classification results.
Acknowledgements: This research was supported by
GCL program of The Univ. of Tokyo by JSPS. N. Inoue andT. Yamasaki are partially supported by the Grants-in-Aid
for Scientific Research (no. 26700008) from JSPS, JSTCREST (JPMJCR1686), and Microsoft IJARC core13. E.

Simo-Serra and H. Ishikawa are partially supported by JSTCREST Grant Number JPMJCR14D1, JST ACT-I Grant
Number JPMJPR16UD,

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