The rapid development in deep learning has brought significant advances to semantic segmentationLong et al. (2015); Chen et al. (2017); Zhao et al. (2017)
which is one of the most fundamental tasks in computer vision. Existing methods often heavily rely on numerous pixel-wise annotated data, which is labor-exhausting and expensive. Towards this burden, great interests have been aroused in Semi-Supervised Semantic Segmentation, which attempts to train a semantic segmentation model with limited labeled data and a large amount of unlabeled data.
The key challenge in semi-supervised learning is to effectively leverage the abundant unlabeled data. One widely adopted strategy is pseudo labelingLee and others (2013). As shown in Figure 1
, the model assigns pseudo labels to unlabeled data based on the model predictions on-the-fly. These data with pseudo labels will be taken as auxiliary supervision during training to boost performance. To further facilitate semi-supervised learning, the teacher-student frameworkTarvainen and Valpola (2017); Xu et al. (2021); Wang et al. (2022) is incorporated. The teacher model, which is the Exponential Moving Average (EMA) of the student model, is responsible for generating smoothly updated pseudo labels. Via jointly supervised by limited data with ground-truth labels and abundant data with pseudo labels, the student model can learn more representative features, leading to significant performance gains.
Although shown to be effective, the pseudo labeling paradigm suffers from unreliable pseudo labels, leading to inaccurate mask predictions. Previous research work alleviates this problem by filtering out predictions that are lower than a threshold of classification scores Berthelot et al. (2019); Sohn et al. (2020); Zhang et al. (2021). However, this mechanism can not perfectly filter out wrong predictions, because some wrong predictions may have high classification scores, named over-confidence or mis-calibration Guo et al. (2017) phenomenon. Moreover, a high threshold will heavily reduce the number of generated pseudo labels, limiting the effectiveness of semi-supervised learning.
Towards the aforementioned challenge, it is necessary to propose a new pseudo labeling paradigm that can learn representative features from unlabeled data as well as avoid negative influences caused by unreliable pseudo labels. Delving into the semantic segmentation framework, it is composed of a feature extractor and a mask predictor. Previous works ask the feature extractor and the mask predictor to learn from both ground-truth labels and pseudo labels simultaneously. As a result, the accuracy of the model is harmed by incorrect pseudo labels. To better leverage the unlabeled data with pseudo labels, a viable solution is to let the feature extractor learn feature representation from both ground-truth labels and pseudo labels, while the mask predictor only learns from ground-truth labels to predict accurate segmentation results.
Accordingly, we propose a novel framework, Semi-Supervised Semantic Segmentation via Gentle Teaching Assitant (GTA-Seg), which attaches an additional gentle teaching assistant (GTA) module to the original teacher-student framework. Figure 1 compares our method with previous frameworks. In our method, the teacher model generates pseudo labels for unlabeled data and the gentle teaching assistant (GTA) learns from these unlabeled data. Only knowledge of the feature extractor in the gentle teacher assistant (GTA) is conveyed to the feature extractor of the student model via Exponential Moving Average (EMA). We coin this process as representation knowledge transmission. Meanwhile, the student model also learns from the reliable ground-truth labels to optimize both the feature extractor and mask predictor. The gentle teaching assistant (GTA) is called gentle since it not only transfers the beneficial feature representation knowledge to the student model, but also protects the student model from the negative influences caused by unreliable pseudo labels in the mask predictor. Furthermore, a re-weighting mechanism is further adopted for pseudo labels to suppress unreliable pixels.
Extensive experiments have validated that our method shows competitive performance on mainstream benchmarks, proving that it can make better utilization of unlabeled data. In addition, we can observe from the visualization results that our method boasts clearer contour and more accurate classification for objects, which indicates better segmentation performance.
2 Related Work
Semantic Segmentation, aiming at predicting the label of each pixel in the image, is one of the most fundamental tasks in computer vision. In order to obtain the dense predictions, FCN Long et al. (2015) replaces the original fully-connected layer in the classification model with convolution layers. The famous encoder-decoder structure is borrowed to further refine the pixel-level outputs Noh et al. (2015); Badrinarayanan et al. (2017). Meanwhile, intensive efforts have been made to design network components that are suitable for semantic segmentation. Among them, dilated convolution Yu and Koltun (2016) is proposed to enhance receptive fields, global and pyramid pooling Liu et al. (2015); Chen et al. (2017); Zhao et al. (2017) are shown to be effective in modeling context information, and various attention modules Zhang et al. (2018); Zhao et al. (2018); Fu et al. (2019); Huang et al. (2019); Sun et al. (2019) are adopted to capture the pixel relations in images. These works mark milestones in this important computer vision task, but they pay rare attention to the data-scarce scenarios.
Mainstream methods in Semi-Supervised Learning Zhu (2005) (SSL) fall into two lines of work, self-training Grandvalet and Bengio (2004); Lee and others (2013) and consistency reguralization Laine and Aila (2017); Sajjadi et al. (2016); Miyato et al. (2018); Xie et al. (2020); Tarvainen and Valpola (2017). The core spirit of self-training is to utilize the model predictions to learn from unlabeled data. Pseudo Labeling Lee and others (2013), which converts model predictions on unlabeled data to one-hot labels, is a widely-used technique Berthelot et al. (2019); Sohn et al. (2020); Zhang et al. (2021) in semi-supervised learning. Another variant of self-training, entropy minimization Rényi (1961), is also proved to be effective both theoretically Wei et al. (2021) and empirically Grandvalet and Bengio (2004). Consistency Regularization Sajjadi et al. (2016); Xie et al. (2020) forces the model to obtain consistent predictions when perturbations are imposed on the unlabeled data. Some recent works unveil that self-training and consistency regularization can cooperate harmoniously. MixMatch Berthelot et al. (2019) is a pioneering holistic method and boasts remarkable performance. On the basis of MixMatch, Fixmatch Sohn et al. (2020) further simplify the learning process while FlexMatch Zhang et al. (2021) introduces a class-wise confidence threshold to boost model performance.
Semi-Supervised Semantic Segmentation
Semi-Supervised Semantic Segmentation aims at pixel-level classification. Borrowing the spirit of Semi-Supervised Learning, self-training and consistency regularization gives birth to various methods. One line of work Zou et al. (2021); Chen et al. (2021); Hu et al. (2021); Wang et al. (2022) applies pseudo labeling in self-training to acquire auxiliary supervision, while methods based on consistency Mittal et al. (2019) pursue stable outputs at both feature Lai et al. (2021); Zhong et al. (2021) and prediction level Ouali et al. (2020)
. Apart from them, Generative Adversarial Networks (GANs)Goodfellow et al. (2014) or adversarial learning are often leveraged to provide additional supervision in relatively early methods Souly et al. (2017); Hung et al. (2018); Mendel et al. (2020); Ke et al. (2020). Various recent methods tackles this problem from other perspectives, such as self-correcting networks Ibrahim et al. (2020) and contrastive learning Alonso et al. (2021). Among them, some works Yuan et al. (2021) unveil another interesting phenomenon that the most fundamental training paradigm, equipped with strong data augmentations, can serve as a simple yet effective baseline. In this paper, we shed light on semi-supervised semantic segmentation based on pseudo labeling and strives to alleviate the negative influence caused by noisy pseudo labels.
Semi-Supervised Semantic Segmentation
In Semi-Supervised Semantic Segmentation, we train a model with limited labeled data and a large amount of unlabeled data , where is often much larger than . The semantic segmentation network is composed of the feature extractor and the mask predictor . The key challenge of Semi-Supervised Semantic Segmentation is to make good use of the numerous unlabeled data. And one common solution is pseudo labeling Lee and others (2013); Yang et al. (2022).
Pseudo Labeling is a widely adopted technique for semi-supervised segmentation, which assigns pseudo labels to unlabeled data according to model predictions on-the-fly. Assuming there are categories, considering the pixel on the image, the model prediction and the corresponding confidence will be
where denotes the category, larger indicates that the model is more certain on this pixel, which is consequently, more suitable for generating pseudo labels. Specifically, we often keep the pixels whose confidence value is greater than one threshold, and generate pseudo labels as
where is the confidence threshold at the iteration. We note that can be a constant or a varied value during training. The pixel on image with a confidence value larger than will be assigned with pseudo label . The unlabeled data that are assigned with pseudo labels will be taken as auxiliary training data, while the other unlabeled data will be ignored.
Teacher-Student Croitoru et al. (2017); Tarvainen and Valpola (2017); Wang et al. (2022) framework is a currently widely applied paradigm in Semi-Supervised Segmentation, which consists of one teacher model and one student model. The teacher model is responsible for generating pseudo labels while the student model learns from both the ground-truth labels and pseudo labels. Therefore, the loss for the student model is
In Semi-Supervised Semantic Segmentation, and are the cross-entropy loss on labeled data and unlabeled data with pseudo labels, respectively Wang et al. (2022), and is a loss weight to adjust the trade-off between them. The optimization of the student model can be formulated as
where denotes the learning rate. In the Teacher-Student framework, after the parameters of the student model are updated, the parameters of the teacher model will be updated by the student parameters in an Exponential Moving Average (EMA) manner.
where and denote the parameters of the teacher and student model at -th iteration, respectively. is a hyper-parameter in EMA, where .
3.2 Gentle Teaching Assistant
In this section, we will introduce our Gentle Teaching Assistant framework for semi-supervised semantic segmentation (GTA-Seg), as shown in Figure 2, which consists of the following three steps.
Step 1: Pseudo Label Generation and Re-weighting.
Similar to previous work Wang et al. (2022), the teacher model is responsible for generating pseudo labels. A confidence threshold is also adopted to filter out the pseudo labels with low confidence. For the kept pixels, instead of treating all of them equally, we propose a re-weighting mechanism according to the confidence of each pixel as follows,
In our re-weighting strategy, the pixel with higher confidence will be highlighted while the other will be suppressed. As a result, the negative influence caused by unreliable pseudo labels can be further alleviated. We adopt Laplace Smoothing Manning et al. (2010) to avoid over penalization where is a predefined coefficient. With this re-weighting mechanism, the unsupervised loss on unlabeled data becomes
Step 2: Representation Knowledge Transmission via Gentle Teaching Assistant (GTA).
Gentle Teaching Assistant (GTA) plays a crucial role in our framework. Previous works force the student model to learn from both labeled and unlabeled data simultaneously. We argue that it is dangerous to treat ground-truth labels and pseudo labels equally since the incorrect pseudo labels will mislead the mask prediction. Therefore, we want to disentangle the effects of pseudo labels on feature extractor and mask predictor of the student model. Concretely, our solution is to introduce one additional gentle teaching assistant, which learns from the unlabeled data and only transfers the beneficial feature representation knowledge to the student model, protecting the student model from the negative influences caused by unreliable pseudo labels.
After optimized on unlabeled data with pseudo labels as in Eq. 8, the gentle teaching assistant model is required to convey the learned representation knowledge in feature extractor to the student model via Exponential Moving Average (EMA) as in Eq. 9,
where is the parameters of the gentle teaching assistant model at -th iteration, is the parameters of the student model at -th iteration, and denotes the parameters of the feature extractor. Through our representation knowledge transmission, the unlabeled data is leveraged to facilitate feature representation of the student model, but it will not train the mask predictor.
Step 3: Optimize student model with ground truth labels and update teacher model.
With the gentle teaching assistant module, the student model in our framework is only required to learn from the labeled data,
Here, the whole model, including the feature extractor as well as the mask predictor, is updated according to the supervised loss computed by the ground-truth labels of labeled data.
Then the teacher model is updated by taking the EMA of the student model according to the traditional paradigm in the teacher-student framework.
Finally, the teacher model, which absorbs the knowledge of both labeled and unlabeled data from the student model, will be taken as the final model for inference.
We evaluate our method on 1) PASCAL VOC 2012 Everingham et al. (2010): a widely-used benchmark dataset for semantic segmentation, with images for training and images for validation. Some researches Chen et al. (2021); Yang et al. (2022) augment the training set by incorporating the coarsely annotated images in SBD Hariharan et al. (2011) to the original training set, obtaining labeled training images, which is called the augmented training set. In our experiments, we consider both the original training set and the augmented training set, taking , , , , and images from the labeled images in the original training set, and , and images from the labeled training images in the augmented training set. 2) Cityscapes Cordts et al. (2016), a urban scene dataset with images for training and images for validation. We sample , , , images from the labeled images in the training set. We take the split in Zou et al. (2021) and report all the performances in a fair comparison.
4.2 Implementation Details
We take ResNet-101 He et al. (2016)
pre-trained on ImageNetDeng et al. (2009) as the network backbone and DeepLabv3+ Chen et al. (2018) as the decoder. The segmentation head maps the 512-dim features into pixel-wise class predictions.
We take SGD as the optimizer, with an initial learning rate of 0.001 and a weight decay of 0.0001 for PASCAL VOC. The learning rate of the decoder is 10 times of the network backbone. On Cityscapes, the initial learning rate is 0.01 and the weight decay is 0.0005. Poly scheduling is applied to the learning rate with , where is the initial learning rate, is the current iteration and is the total iteration. We take GPUs to train the model on PASCAL VOC, and GPUs on Cityscapes. We set the trade-off between the loss of labeled and unlabeled data , the hyper-parameter in our re-weighting strategy and the EMA hyper-parameter
in all of our experiments. At the beginning of training, we train all three components (the gentle teaching assistant, the student and the teacher) on labeled data for one epoch as a warm-up following conventionsTarvainen and Valpola (2017), which enables a fair comparison with previous methods. Then we continue to train the model with our method. For pseudo labels, we abandon the data with lower confidence. We run each experiment
times with random seed = 0, 1, 2 and report the average results. Following previous works, input images are center cropped in PASCAL VOC during evaluation, while on Cityscapes, sliding window evaluation is adopted. The mean of Intersection over Union (mIoU) measured on the validation set serves as the evaluation metric.
|MT Tarvainen and Valpola (2017)||51.72||58.93||63.86||69.51||70.96|
|CutMix French et al. (2019)||52.16||63.47||69.46||73.73||76.54|
|PseudoSeg Zou et al. (2021)||57.60||65.50||69.14||72.41||73.23|
|PC2Seg Zhong et al. (2021)||57.00||66.28||69.78||73.05||74.15|
|ST++ Yang et al. (2022)||65.23||71.01||74.59||77.33||79.12|
|U2PL Wang et al. (2022)||67.98||69.15||73.66||76.16||79.49|
|GTA-Seg (Ours)||70.02 0.53||73.16 0.45||75.57 0.48||78.37 0.33||80.47 0.35|
|MT Tarvainen and Valpola (2017)||70.51||71.53||73.02||76.58|
|CutMix French et al. (2019)||71.66||75.51||77.33||78.21|
|CCT Ouali et al. (2020)||71.86||73.68||76.51||77.40|
|GCT Ke et al. (2020)||70.90||73.29||76.66||77.98|
|CPS Chen et al. (2021)||74.48||76.44||77.68||78.64|
|AEL Hu et al. (2021)||77.20||77.57||78.06||80.29|
|GTA-Seg (Ours)||77.82 0.31||80.47 0.28||80.57 0.33||81.01 0.24|
|DMT Feng et al. (2022)||54.82||-||63.01||-|
|CutMix French et al. (2019)||55.73||60.06||65.82||68.33|
|ClassMix Olsson et al. (2021)||-||59.98||61.41||63.58|
|Pseudo-Seg Zou et al. (2021)||60.97||65.75||69.77||72.42|
|DCC* Lai et al. (2021)||61.15||67.74||70.45||73.89|
|GTA-Seg (Ours)||62.95 0.32||69.38 0.24||72.02 0.32||76.08 0.25|
4.3 Experimental Results
Pascal Voc 2012
We first evaluate our method on the original training set of PASCAL VOC 2012. The results in Table 1 validate that our method surpasses previous methods by a large margin. Specifically, our method improves the supervised-only (SupOnly) model by , , , , in mIoU when , , , , of the data is labeled, respectively. When compared to the readily strong semi-supervised semantic segmentation method, our method still surpasses it by , , , , respectively. We note that in the original training set, the ratio of labeled data is relatively low ( to ). Therefore, the results verify that our method is effective in utilizing unlabeled data in semi-supervised semantic segmentation.
We further compare our method with previous methods on the augmented training set of PASCAL VOC 2012, where the annotations are relatively low in quality since some of labeled images come from SBD Hariharan et al. (2011) dataset with coarse annotations. We can observe from Table 3, our method consistently outperforms the previous methods in a fair comparsion.
For Cityscapes, as shown in Table 3, our method still shows competitive performance among previous methods, improving the existing state-of-the-art method by , , , in mIoU when , , , of the data is labeled.
We analyze the effectiveness of different components in our method, , the original teacher-student framework, gentle teaching assistant and re-weighted pseudo labeling as in Table 5. According to the results in Table 5, the carefully designed gentle teaching assistant mechanism (the third row) helps our method outperform the previous methods, pushing the performance about higher than the original teacher-student model (the second row). Further, the re-weighted pseudo labeling brings about performance improvements. With all of these components, our method outperforms the teacher-student model by over and SupOnly by over in mIoU.
|Teacher-Student||Gentle Teaching Assistant||Re-weighted||mIoU|
|Original EMA (all parameters)||64.07|
|Unbiased ST Chen et al. (2022)||65.92|
|EMA (Encoder) (Ours)||72.10|
Gentle Teaching Assistant
As mentioned in Table 5, our proposed gentle teaching assistant framework brings about remarkable performance gains. Inspired by this, we delve deeper into the gentle teaching assistant model in our framework. We first consider the representation knowledge transmission mechanism. In Table 5, we compare our mechanism with other methods such as the original EMA Tarvainen and Valpola (2017) that updates all of the parameters via EMA and Unbiased ST Chen et al. (2022) that introduces an additional agent to convey representation knowledge. We can observe that all these mechanisms boost SupOnly remarkably, while our mechanism is superior to other methods.
We next pay attention to the three models in our framework, gentle teaching assistant model, student model, and teacher model. Table 7 reports the evaluation performance of them. All of them show relatively competitive performance. For the teacher assistant model, it is inferior to the student model. This is reasonable since it is only trained on pseudo labels, while the student model inherits the representation knowledge of unlabeled data from the gentle teaching assistant as well as trained on labeled data. In addition, the teacher model performs best, which agrees with previous works Tarvainen and Valpola (2017).
|Gentle Teaching Assistant||70.10|
|Gentle Teaching Assistant||Student||mIoU|
|Labeled Data||Pseudo Labels||66.71|
|Labeled Data + Pseudo Labels||Labeled Data||72.28|
|Pseudo Labels||Labeled Data||73.16|
In our method, we train GTA with pseudo labels and the student model with labeled data. It is interesting to explore the model performance of other designs. Table 9 shows that 1) training the student model with pseudo labels will cause significant performance drop, which is consistent with our statement that the student model shall not learn from the pseudo labels directly. 2) Incorporating labeled data in training GTA is not beneficial to model performance. We conjecture that when we transmit the knowledge of labeled data from GTA to the student model, as well as supervise the student model with labeled data, the limited labels overwhelm the updating of the student model, which possibly leads to overfitting and harms the student model’s performance. Then since the teacher model is purely updated by the student model via EMA, the performance of the teacher model is also harmed. Considering the ultimate goal is a higher performance of the teacher model, we choose to train GTA with pseudo labels alone.
In our method, we design the re-weighting strategy for pseudo labels as Eq. 6, which contains 1) confidence-based re-weighting, 2) Laplace Smoothing. Here we conduct further ablation study on our design. Table 9 shows that though effective in other tasks such as semi-supervised object detection Xu et al. (2021), in our framework, adopting confidence-base re-weighting is harmful, dropping the performance from to . On the contrary, our strategy, with the help of Laplace Smoothing Manning et al. (2010) which alleviates over-penalization, pushes the readily strong performance to a higher level.
|0.99 (Reported)||73.16||1 (Reported)||73.16|
|Confidence-based Re-weighting||Laplace Smoothing||mIoU|
We evaluate the performance of our method under different EMA hyper-parameters and various warmup epochs. Results in Table 9 demonstrates that our method performs steadily under different hyper-parameters. In addition, the performance can still be slightly enhanced if the hyper-parameters are tuned carefully.
Besides quantitative results, we present the visualization results to further analyze our method. We note that the model is trained on as few as labeled samples and about unlabeled samples. As shown in Figure 3, facing such limited labeled data, training the model merely in the supervised manner (SupOnly) appears to be vulnerable. Under some circumstances, the model is even ignorant of the given images (the third and the fourth row). While methods that utilize unlabeled data (teacher-student model and our method), show stronger performance. Further, compared with the original teacher-student model, our method shows a stronger ability in determining a clear contour of objects (the first row) and recognizing the corresponding categories (the second row). Our method is also superior to previous methods in distinguishing objects from the background (the third and fourth row).
In addition, we present more visualization results about our designed re-weighting strategy. We can observe from Figure 4 that incorporating the re-weighting strategy into our method leads to better performance on contour or ambiguous regions.
One limitation of our method is that it brings about more training costs since it incorporates an extra gentle teaching assistant model. Fortunately, the inference efficiency is not influenced since only the teacher model is taken for inference. On the other hand, our method only attempts at making better use of the unlabeled data, but little attention has been paid to the labeled data. We consider it promising to conduct research on how to better leverage the labeled data in semi-supervised semantic segmentation.
In this paper, we propose a novel framework, Gentle Teaching Assistant, for semi-supervised semantic segmentation (GTA-Seg). Concretely, we attach an additional teaching assistant module to disentangle the effects of pseudo labels on the feature extractor and the mask predictor. GTA learns representation knowledge from unlabeled data and conveys it to the student model via our carefully designed representation knowledge transmission. Through this framework, the model optimizes representation with unlabeled data, as well as prevents it from overfitting on limited labeled data. A confidence-based pseudo label re-weighting mechanism is applied to further boost the performance. Extensive experiment results prove the effectiveness of our method.
This work is supported by GRF 14205719, TRS T41-603/20-R, Centre for Perceptual and Interactive Intelligence, CUHK Interdisciplinary AI Research Institute, and Shanghai AI Laboratory.
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Appendix A Appendix
a.1 More implementation details
We take the images from PASCAL VOC 2012 Everingham et al. (2010)111http://host.robots.ox.ac.uk/pascal/VOC/voc2012/, SBD Hariharan et al. (2011) 222http://home.bharathh.info/pubs/codes/SBD/download.html, and Cityscapes Cordts et al. (2016) 333https://www.cityscapes-dataset.com/. The Cityscapes dataset is processed with these scripts 444https://github.com/mcordts/cityscapesScripts.
a.2 More analysis on representation knowledge transmission
The representation knowledge transmission in our gentle teaching assistant is conducted merely on the feature extractor. In our main paper, we view the network backbone (ResNet-101 He et al. (2016)) in the segmentation model as the feature extractor and the decoder (DeepLabv3++) as the mask predictor. Meanwhile, there are other variants of such a division, , taking fewer or more layers as the feature extractor and all the remaining layers as the decoder. Here, we present the experimental results when taking these divisions.
|Method||Feature Extractor||Mask Predictor||mIoU|
|Structure||Param (M)||Structure||Param (M)|
|Ours||ResNet-101 + Decoder.feature layers||60.9||
|ResNet-101 (main paper)||42.7||Decoder (main paper)||21.8||73.16|
|ResNet-101.layer0,1,2,3||27.7||Decoder + ResNet-101.layer4||36.8||68.41|
|ResNet-101.layer0,1,2||1.5||Decoder + ResNet-101.layer3,4||63.0||66.23|
|ResNet-101.layer0,1||0.3||Decoder + ResNet-101.layer2,3,4||64.2||62.11|
|ResNet-101.layer0||0.1||Decoder + ResNet-101.layer1,2,3,4||64.4||60.88|
|Original EMA||ResNet-101 + Decoder||64.5||-||-||64.07|
|SupOnly||-||-||ResNet-101 + Decoder||64.5||54.92|
We note that when taking the whole ResNet-101 and decoder as the mask predictor (the last row in Table 10), our method shrinks to the model trained only on supervised data (SupOnly). And when they both act as the feature extractor, our representation knowledge transmission boils down to the original EMA update in Tarvainen and Valpola (2017). From Table 10, we can observe that compared to SupOnly, conducting representation knowledge transmission consistently brings about performance gains. And when taking suitable layers (the first four rows in ’Ours’), our method can achieve better performance than the original EMA. Among them, the most straightforward strategy (also the one in our main paper), which considers ResNet-101 as the feature extractor and decoder as the mask predictor, boasts the best performance.
These experimental results demonstrate that 1) utilizing unlabeled data is crucial to semi-supervised semantic segmentation, 2) transmitting all the knowledge learned from the pseudo labels will mislead the model prediction, 3) our method, which only conveys the representation knowledge in the feature extractor, can alleviate the negative influence of unreliable pseudo labels, making use of unlabeled data in a better manner.