246

SOLA, 2023, Vol. 19, 246−252, doi:10.2151/sola.2023-032

SolaCam: A Deep Learning Model for Solar Radiation Estimation
Using Consumer Cameras
Daisuke Sugiyama1, 2, Ryo Onishi3, and Hironori Fudeyasu4
1Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
2
Kagoshima University, Kagoshima, Japan
3
Tokyo Institute of Technology, Tokyo, Japan
4
Yokohama National University, Yokohama, Japan(Manuscript received 17 May 2023, accepted 31 August 2023)

Abstract This study proposes a deep learning approach called SolaCam to accurately estimate solar radiation fromthe images captured by cameras. The proposed SolaCam performs deep learning by utilizing both image features andtheoretical maximum solar radiation that vary with time and location. The trained model is capable of accurately estimating solar radiation on the ground surface from sky images captured by smartphones, fixed-point cameras, and otherdevices. The developed SolaCam can use a remote sensing function, which estimates solar radiation, on inexpensive
camera-equipped devices.
Citation: Sugiyama, D., R. Onishi, and H. Fudeyasu, 2023: SolaCam: A deep learning model for solar radiation estimation using consumer cameras. SOLA, 19, 246−252, doi:10.2151/sola.2023-032.

1. Introduction
To achieve a smart society, the use of high-density Internet of Things (IoT) sensors has become important in collecting information on cities at higher densities, which can be utilized for monitoring and predicting phenomena. Remotesensors, such as radiometers and solar radiation meters, are expensive. In contrast, digital cameras connected to theinternet are inexpensive, and images of the sky and clouds taken by IoT sensors can be analyzed to extract useful information.
Recent advances in visual pattern recognition using deep learning (DL) methods have resolved many tasks that werepreviously challenging using machine learning (ML) methods (LeCun et al. 1989; Krizhevsky et al. 2012; Howard et al.
2019; Tan et al. 2019; Dosovitskiy et al. 2021). Image analysis using DL has also been applied in earth sciences; forexample, estimating cloud optical thickness from images captured by sky cameras (Masuda et al. 2019).
This study targets camera images from inexpensive consumer webcams and mobile devices to build a DL model.
Owing to the use of inexpensive camera devices rather than specialized IoT weather instruments, comprehensive observations would be obtained, which would contribute to urban or micro weather forecast simulations at an urban districtlevel. Similar research on extracting weather observation data from consumer camera images includes the estimation of
0–10 cloud coverage (Onishi and Sugiyama 2017), and atmospheric temperature estimation (Chu 2018).
This study proposes a new DL-based method for accurately estimating solar radiation using information fromcamera equipment. The method involves a regression task using consumer camera images and shooting time information input to perform a real-value estimation of solar radiation directed at the ground surface (0–1.32 kW/m2). The
image could be captured in any direction, roughly horizontal to the ground, where over 75% of the image should be of
the sky. To achieve high estimation accuracy, the learning process included meteorological domain knowledge, such as
the maximum amount of solar radiation outside the atmosphere, which can be calculated from the latitude and longitudeof the camera location and the time of day.

2. Data and methods
2.1 Data augmentation
Sky images in five directions were captured over a six-year period using several fixed-point cameras installed on theroof of Yokohama National University (YNU), and data from the pyranometer on the same roof were obtained. Figure 1
shows an example of the observation environment and data collected. The images and solar radiation data were recorded every minute, taking a total of approximately five million images. However, images from fixed cameras only may
be insufficient for learning diverse image features because of limited variations in shooting directions, lighting sources,and background locations. Therefore, a technique called data augmentation (Shorten et al. 2019), which increases thediversity of training data, was effectively used.
Corresponding author: Daisuke Sugiyama, Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku,
Yokohama-city, Kanagawa, 236-0001, Japan. E-mail: sugiyamad@jamstec.go.jp.
©The Author(s) 2023. This is an open access article published by the Meteorological Society of Japan
under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/license/by/4.0).

Sugiyama et al., Deep Learning Solar Radiation Estimation Using Consumer Cameras

247

Fig. 1. Left: Camera and pyranometer installed on the roof of Yokohama National University. Images and solar radiation aremeasured every minute. Five cameras cover directions of Mt. Fuji (west), Machida (northwest), Tokyo (northeast), Minatomirai(southeast), and Kamakura (south). Right: Camera images (southeast direction) at 12:00 AM in July 2017, arranged for 30 days.
The number in the upper left of each image shows the solar radiation in kW/m2.

To verify the need for data augmentation in fixed-point cameras, a simple experiment was conducted using a convolutional neural network (CNN), which is effective for learning image features.
In this base experiment, only one camera oriented towards Mt. Fuji (west) was used, and the CNN estimation accuracy was measured by cross-validation in the time direction. Specifically, the first five years of the six-year period wereused for training, and the resulting trained model was evaluated for the remaining year. The results showed that the root
mean square error (RMSE) for the untrained year was 0.064 kW/m2. However, when data from a camera in a different
direction that was not used for learning was used for estimation with CNN, the accuracy was extremely low, with an
RMSE of 0.4 kW/m2 or higher. Consequently, it was assumed that the system overfitted the features that depended on
the fixed-point camera images of the learning target, such as brightness, contrast, ground position, and ground shadow.
Based on this assumption, the generalization of fixed-camera learning to general cameras is necessary in addressing thisproblem.
Therefore, data augmentation (Advanced Setting S1) was performed on the images to be learned. The confusion
matrix (Fig. S2) obtained from the CNN trained by the augmented dataset showed that the RMSE improved with data
augmentation, becoming as low as 0.12 kW/m2 for target solar radiations less than 0.7 kW/m2 (7-th 8-quantile of dataset), and as low as 0.25 kW/m2 for targets higher than 0.7 kW/m2.
The lower accuracy for larger radiation values may be attributed to seasonal variations in solar radiation. Maximum
monthly solar radiation observed at the camera location (contained reflections by clouds and ground surfaces) was 0.7
kW/m2 in December and 1.3 kW/m2 in July. Alternatively, the number of images with solar radiation higher than 0.7
kW/m2 was small due to seasonal variations.
2.2 SolaCam model
The developed SolaCam model learned seasonal variations in solar radiation by calculating the theoretical maximum solar radiation from the latitude, longitude, and time, and conducting learning with images (Fig. 2). A neuralnetwork architecture was developed using PyTorch (Adam et al. 2019).
The architecture consisted of two parts: one for inputting images and the other for values. Sky images were processed by clipping and resizing to 128 × 128 pixels for input into the CNN. The CNN used in this study was similar to
those used in previous studies (LeCun et al. 1999; Onishi and Sugiyama 2017). The detailed architecture and formulaeof the CNN are presented in Advanced Setting S3.
Input values crucial for calculating the solar radiation were latitude, longitude, and time. When using SolaCam estimation, location and time can be obtained from camera devices. A general geometric formula for meteorology was used

248

SOLA, 2023, Vol. 19, 246−252, doi:10.2151/sola.2023-032

Fig. 2. Arrows refer to the data flow. The number next to the arrow indicates the size of each dimension of the output: Conv: convolutional layer; Pool: max pooling layer; and Fc: fully connected layer. The output of the convolutional neural network (CNN)
is 502 nodes, and the output of Fc, the theoretical maximum solar radiation, is 10 nodes; the two outputs are concatenated. The
Red Green Blue (RGB) values of the input image and the theoretical maximum solar radiation are normalized to 0.0–1.0.

to calculate the theoretical maximum solar radiation using the input values. The calculated value was normalized to arange of 0.0–1.0 and inputted into a fully connected (FC) layer. This FC layer was concatenated with the CNN FC layer
and learned simultaneously. The output parameters from the output layer do not directly use solar radiation but rather
use solar radiation divided by the maximum solar radiation. In other words, the product of the model’s output value andmaximum solar radiation is the solar radiation. This implies that the model learned the attenuation rate of the maximum
solar radiation based on the sky conditions in the image. Generally, the observed solar radiation data were imbalanced,
with exponentially fewer samples as the solar radiation approached high levels (the histogram of solar radiation usedin this experiment is shown in Fig. S4). When optimizing a neural network using this type of imbalanced data, lowsolar radiation data are more heavily learned, resulting in substantially reduced accuracy at high solar radiation levels.
Therefore, SolaCam applied a sampling method called weighted random sampling (Efraimidis et al. 2008). First, thetraining dataset were divided into groups of solar radiation at 0.1 kW/m2 units and then weighted random sampling was
performed, so that the data selected for the mini batch had uniform probability between groups. This enabled uniform
probability learning to be achieved for each solar radiation group from 0.0 to 1.3 kW/m2 in the data selected for mini
batch stochastic gradient descent.
2.3 Dataset for training
To evaluate generalized performance of SolaCam, the YNU trained dataset was used, which consisted of sky images
captured at one-minute intervals by four of the five cameras installed on the university roof (35.473°N, 139.591°E)
from 2011 to 2016. The remaining one year of data from the unused camera facing south was used as the YNU test
dataset. This allowed the generalized performance of SolaCam to be measured using unseen cameras, unexplored directions, unobserved landscapes, and future data (see Advanced Setting S5 for details on hyper­parameter tuning).
2.4 Implementation of training
SolaCam was trained using approximately 3.5 million images from the YNU training dataset with Graphics Processing Units (GPU). Training was performed on supercomputers in Japan Agency for Marine-Earth Science and Technology(JAMSTEC) using the data-parallel method (Krizhevsky 2014) on eight GPUs (refer to Advanced Setting S6 for details
on distributed learning and training hours). Although the training required significant amount of time, the inference timefor SolaCam was less than one second, even on a Central Processing Unit (CPU). In addition, estimation can occur inless than 10 seconds even on a weak CPU installed onto an inexpensive device such as a Raspberry Pi.
2.5 Baseline analysis
The solar radiation data was analyzed to determine the optimal coefficient (k), for the theoretical maximum solar
radiation (M) multiplied by k using a simple linear regression. The optimal coefficient (k) was found to be 0.46 for all
the YNU training dataset. This M × k model was used as a baseline for comparison. Furthermore, the functionalities ofmultiple models were compared to reveal the effects of learning physical information.

Sugiyama et al., Deep Learning Solar Radiation Estimation Using Consumer Cameras

249

Table 1. Comparisons of mean absolute errors (MAE), root mean square errors (RMSE), coefficient of determination called R2
score and balanced root mean square errors (Balanced-RMSE) of estimated solar radiation parameters per 1-minute for test dataset at Yokohama National University.
Methoda) Baselineb) SolaCam (Proposed Model)c) SolaCam without physics knowledged) SolaCam without weighted random samplere) EfficientNet-B3

MAE [kW/m2]

RMSE [kW/m2]

R2 score

Balanced-RMSE [kW/m2]

0.095
0.045
0.055
0.047
0.079

0.16
0.11
0.11
0.11
0.16

0.6
0.83
0.81
0.83
0.64

0.28
0.13
0.3
0.32
0.4

3. Results and discussion
3.1 Estimation results using SolaCam
Table 1 lists the accuracy of SolaCam, and with model comparison results are shown in Table 1 (b)–(d). The resultsin Table 1 were analyzed to compare the accuracy and performance of the models. Generally, lower values of mean
absolute error (MAE), RMSE and R2 score indicate lower overall errors and higher accuracy. Notably, the solar radiation data in this study were imbalanced with a few high solar radiation values; therefore, even if the RMSE values weregood, poor accuracy in the high solar radiation range could possibly be observed with a small sample size. To evaluate
performance, the average accuracy for high and low solar radiations was used as a balanced-RMSE metric, which canbe defined as follows:n

RMSEa =

1( yi − yi ) 2 , Y < 0.7n∑i=1

RMSEb =

1( yi − yi ) 2 , Y ≥ 0.7
n∑
i=1

n

Balanced RMSE =RMSEa + RMSEb
2

The threshold of Y = 0.7 kW/m2 was chosen based on the discussion in Subsection 2.1.
Based on Table 1, SolaCam is more accurate than the baseline model in all the four metrics, i.e., MAE, RMSE, R2
score and Balanced RMSE. Specifically, in the Balanced RMSE, SolaCam is more accurate than the baseline model by
54%. It should be noted that, from the MAE, RMSE and R2 score values, the balanced-RMSE increased for all the fivemethods, indicating that the model does not perform well in estimating high solar radiation. A comparison of (b) and(c) shows that incorporating physics knowledge into SolaCam can improve the accuracy of the MAE, RMSE and R2
score. The value of the balanced RMSE shows that the estimation accuracy for high solar radiation is also less likely todeteriorate. Comparison of (b) and (d) shows that weighted random sampling considerably improves the accuracy of
estimating high solar radiation. Additionally, a comparison of (b) and (e) shows that even using a model with manylayers, such as EffecientNet, there is an overfitting tendency, resulting in lower accuracy. A confusion matrix for analyzing the estimation accuracy in each solar radiation range is presented in more detail in Fig. S7.
Figure 3 shows examples of images and SolaCam estimations for the YNU test dataset. Figure S8 shows visualexplanations using Score-CAM of the estimation in Fig. 3 (Wang et al. 2020). When examining the estimation resultsfor each image in Fig. 3 and Fig. S8, solar radiation can be estimated reliably even if changes exist in image featuresdue to weather conditions, such as when raindrops are visible (b), or when the sun is captured (c). Moreover, as shownin (c), estimation is possible even when the sun is visible, and expensive equipment to conceal the sun is unnecessary.
Additionally, images with higher brightness values are commonly thought to correspond to higher solar radiation
values; however, images with rain or thin clouds, such as in (b) and (e), have higher brightness values than those inclear weather. Therefore, image features such as clouds, thickness and type, and weather-related changes are consideredmore critical than brightness for estimating solar radiation. Figure 3f shows an example where the error becomes large
in a pattern with thick clouds, but the sun is visible. This error is probably caused because solar radiation changes overa short period of time when the sun is locally hidden by thick clouds.
These results indicate that incorporating physical knowledge into DL substantially improves accuracy. Additionally,
the inclusion of weighted random sampling, which is effective for learning imbalanced data, resulted in the highest
accuracy of SolaCam in estimating high levels of solar radiation.
3.2 Estimation accuracy at different locations from YNU training dataset
To further confirm the generalized accuracy, the accuracy of solar radiation estimation was measured for camera
images obtained from locations different from the training dataset. A fixed-point camera (facing southeast) and a pyra-

250

SOLA, 2023, Vol. 19, 246−252, doi:10.2151/sola.2023-032

Fig. 3. Examples of SolaCam estimation model for the YNU test dataset: a) clear sky, b) rain, c) sun light, d) early morning cirrocumulus, e) high solar radiation near noon, and f) a pattern of large error. Maximum value is the theoretical maximum solar
radiation. Brightness is the pixel-wise average of the brightness values (0.0 to 1.0) of the image.
Table 2. Comparisons for the dataset in Japan Agency for Marine-Earth Science and Technology(JAMSTEC) at Yokohama Institute for Earth Sciences, and Yokohama National University (YNU)
from November to December. For comparison, a) and b) are the same as in Table 1.
Methoda) Baselineb) SolaCamf) Baselineg) SolaCam

TargetMAE [kW/m2]

RMSE [kW/m2]

Balanced-RMSE [kW/m2]

YNU
YNU
JAMSTEC
JAMSTEC

0.059
0.026
0.06
0.041

0.11
0.06
0.11
0.08

0.18
0.08
0.18
0.11

nometer were installed on the JAMSTEC roof (35.380°N, 139.625°E). The distance between JAMSTEC and YNU isapproximately 12 km. The input was a camera image captured roughly horizontal to the ground, such that over 75% of
the image was of the sky.
The JAMSTEC solar radiation data were obtained for November to December 2022. SolaCam, trained with the
YNU training dataset, was used to estimate solar radiation. Table 2 lists the estimation errors for the YNU test dataset
and the JAMSTEC dataset for the same month. Figure S9 illustrates examples of these images.
The results showed similar estimation errors for SolaCam for both YNU and JAMSTEC data, and the errors were
smaller than the baseline errors. This confirms the generality of SolaCam, i.e., it can estimate highly accurate solar radiation from camera images even at locations where the training dataset was not obtained.
3.3 Analysis of seasonal trends in results and detection of observation errors
To analyze the trend of results by season and identify observation errors, the daily cumulative transition of estimatedsolar radiation was shown by SolaCam, and the seasonal errors in the estimation are shown using a boxplot (Fig. 4).
Consequently, it was found that SolaCam had the largest errors in July, particularly during the summer months whenSugiyama et al., Deep Learning Solar Radiation Estimation Using Consumer Cameras

251

Fig. 4. a) Line plots of daily amountof solar radiation estimated by the
SolaCam for YNU test dataset. The
accuracy of SolaCam is mean absolute error: 0.402 kW/m2 and root
mean square error: 0.796 kW/m2.
Note that the latter half of December
is missing. b) Boxplot for errors ofeach month. Both ends of the box
are quartiles; the outer lines are 1.5
times quartiles, and the orange line
is median. c) Examples of camera
images suggesting observation error
on July 15, 2017, as measured solar
radiations were 0 kW/m2.

solar radiation is the highest. To investigate the cause of these errors, the estimated results for July were examined indetail, and the estimated cumulative solar radiation on several days were found to be substantially different from theactual value. Despite the presence of the sun in the images for these days, the observed data were 0 kW/m2, indicating
observational errors in the solar radiation sensors of the YNU test dataset or local changes in the observation conditions
that were not captured in the images.
Therefore, it can be concluded that errors in estimating solar radiation during the summer months when there wasa high amount of solar radiation were large, and some of these errors were due to missing observational data on certain
days. This result highlights the potential for detecting observation errors that were previously unknown before using the
SolaCam. For example, by comparing the estimated solar radiation from camera images with the actual data measured
by a solar radiation sensor, observation errors could be realized that would otherwise be unnoticed.
3.4 Influence of change in time window
The amount of solar radiation depends on whether the
Table 3. Estimation errors of SolaCam for the YNU test datasun is covered by clouds. Even on a clear day, a small,
set with moving averaging for different time windows.
isolated cloud covering the sun can significantly decrease
BalancedMAE Improvement
solar radiation, meaning that the observed solar radiation
MethodRMSE
Rate From 1-min.
2[kW/m ]
could change markedly locally within a few minutes.
However, the estimated solar radiation from a cameraa) Baseline 1-min.
0.25—a-1) Baseline 10-min.
0.24
4.00%
image that captures a wide sky area from a distance isa-2) Baseline 30-min.
0.23
8.00%
considered the average solar radiation over space. Sincea-3) Baseline 1-hour
0.23
8.00%
space and time averaging are closely related, SolaCamb)
SolaCam
1-min.
0.13—
captures time-averaged solar radiation. Table 3 shows theb-1) SolaCam 10-min.
0.094
20.00%
estimation errors for solar radiation averaged over differb-2) SolaCam 30-min.
0.074
31.10%
ent time windows, including 1 min, 10 min, 30 min, andb-3) SolaCam 1-hour
0.064
40.00%
1 h moving averages. The results demonstrate that theestimation error for the 1 min average was large, while
it was notably lower (by over 30%) for the 30 min and 1 h averages. It should be noted that SolaCam estimate is moresensitive to the time windows than the baseline. If the space and time scales are related with a representative velocity of
10 m/s, a 30 min or 1 h time window corresponds to a spatial window of several tens of kilometers. This indicates thatthe present SolaCam captures solar radiation averaged over an area of several tens of kilometers.

4. Conclusion
A DL-based SolaCam that accurately estimates solar radiation based on camera images, latitude, longitude, and timeis proposed. The SolaCam is more accurate in estimating 1 min average solar radiations than the baseline model by

252

SOLA, 2023, Vol. 19, 246−252, doi:10.2151/sola.2023-032

54% in terms of the balanced RMSE, which evaluates the accuracy for high and low solar radiations. The learned physical information enabled the system to estimate solar radiation with high accuracy even for locations that were different
from those where the training data were acquired.
SolaCam has high accuracy for the estimation of 1 h and 30 min average radiations rather than 1 min average ones.
This means that SolaCam captures solar radiation averaged over an area of several tens of kilometers.
Camera images used for solar radiation estimation should be preprocessed (e.g., trimmed and aligned) to match thespecific format. Relaxation of this restriction can be a topic for future investigations. Other future investigations that
can improve SolaCam are described in Future works S10.

Acknowledgements
This research was partially supported by Grant-in-Aid for Transformative Research Areas, Grant Number JP20B207
and KAKENHI Grant 21K03658. SolaCam were trained using supercomputers in JAMSTEC.
Edited by: K. Ito

Supplements
In the auxiliary material, settings for training SolaCam; data augmentation, neural network architecture, validation
dataset and multi-GPUs training, are shown in Advanced Settings S1, S3, S5 and S6. Confusion matrix for results ofSubsection 2.1, Tables 1 and 3 are shown in Fig. S2, S7. Histogram of solar radiation in the YNU dataset is shown in
Fig. S4. Visualizations of basis of estimation are shown in Fig. S8. Examples of sky images at JAMSTEC are shown in
Fig. S9. Future works of SolaCam are described in S10.

References
Chu, W. T., K. C. Ho, and A. Borji, 2018: Visual weather temperature prediction. 2018 IEEE Winter Conf. on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 234–241, doi:10.1109/WACV.2018.00032.
Dosovitskiy, A., L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G.
Heigold, S. Gelly, T. Unterthiner, and X. Zhai, 2021: An Image is Worth 16x16 Words: Transformers for Image
Recognition at Scale, ICLR2021 Oral, https://doi.org/10.48550/arXiv.2010.11929.
Efraimidis, P., and P. Spirakis, 2008. Weighted random sampling. Encyclopedia of Algorithms, M.-Y. Kao, Ed., Springer
US, Boston, MA, 1024–1027, https://doi.org/10.1007/978-0-387-30162-4_478.
Howard, A., M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q. V. Le, and
H. Adam, 2019: Searching for MobileNetV3. Proc. the IEEE/CVF International Conference on Computer Vision,
1314–1324.
Kingma, D. P., and Ba J. Adam, 2015: A method for stochastic optimization. Proc. the 3rd international conference onlearning representations (ICLR2015), Hilton San Diego Resort & Spa, Sandiego.
LeCun, Y., P. Haffner, L. Bottou, and Y. Bengio, 1999: Object recognition with gradient-based learning. Shape, Contour
and Grouping in Computer Vision. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, 1681.
Masuda, R., H. Iwabuchi, K. S. Schmidt, A. Damiani, and R. Kudo, 2019: Retrieval of cloud optical thickness fromsky-view camera images using a deep convolutional neural network based on three-dimensional radiative transfer.
Remote Sens., 11, 1962, doi:10.3390/rs11171962.
Onishi, R., and D. Sugiyama, 2017: Deep convolutional neural network for cloud coverage estimation from snapshot
camera images. SOLA, 13, 235–239, doi:10.2151/sola.2017-043.
Paszke, A., S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A.
Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and
S. Chintala, 2019: PyTorch: An Imperative Style, High-Performance Deep Learning Library. Neural Information
Processing Systems.
Shorten, C., and T. M. Khoshgoftaar, 2019: A survey on Image Data Augmentation for Deep Learning. J Big Data 6, 60
2019, https://doi.org/10.1186/s40537-019-0197-0.
Sujan, G., C. Deo Ravinesh, D. Casillas-Pérez, S. Salcedo-Sanz, E. Sharma, and M. Ali, 2022: Deep learning CNNLSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction. Measurement,
202, 111759, ISSN 0263-2241, doi:10.1016/j.measurement.2022.111759.
Tan, M., and Q. Le, 2019: EfficientNet: Rethinking model scaling for convolutional neural networks. Proc. the 36th
International Conf. on Machine Learning, PMLR, 97, 6105–6114.
Wang, H., Z. Wang, M. Du, F. Yang, Z. Zhang, S. Ding, P. Mardziel, and X. Hu, 2020: Score-CAM: Score-weighted
visual explanations for convolutional neural networks. Proc. the IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, 24–25.