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Detecting and Counting Sheep with a Convolutional Neural Network


Abstract and Figures

Counting livestock is generally done only during major events, such as drenching, shearing or loading, and thus farmers get stock numbers sporadically throughout the year.More accurate and timely stock information would enable farmers to manage their herds better. Additionally, prompt response to any stock in distress is extremely valuable, both in terms of animal welfare and the avoidance of financial loss. In this regard, the evolution of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) is forging anew research area for remote monitoring and counting of different animal species under various climatic conditions.In this paper, we focus on detecting and counting sheep in a paddock from UAV video. Sheep are counted using a model based on Region-based Convolutional Neural Networks and the results are then compared with other techniques to evaluate their performance.
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Detecting and Counting Sheep with a Convolutional Neural Network
Farah Sarwar1,2, Anthony Griffin1,2, Priyadharsini Periasamy2, Kurt Portas3and Jim Law3
1High Performance Computing Research Lab, 2Electrical and Electronic Engineering Department
School of Engineering, Computer and Mathematical Sciences
Auckland University of Technology, New Zealand,,
3Palliser Ridge Limited, Pirinoa, New Zealand
Counting livestock is generally done only during major
events, such as drenching, shearing or loading, and thus
farmers get stock numbers sporadically throughout the year.
More accurate and timely stock information would enable
farmers to manage their herds better. Additionally, prompt
response to any stock in distress is extremely valuable, both
in terms of animal welfare and the avoidance of financial
loss. In this regard, the evolution of deep learning algo-
rithms and Unmanned Aerial Vehicles (UAVs) is forging a
new research area for remote monitoring and counting of
different animal species under various climatic conditions.
In this paper, we focus on detecting and counting sheep in a
paddock from UAV video. Sheep are counted using a model
based on Region-based Convolutional Neural Networks and
the results are then compared with other techniques to eval-
uate their performance.
1. Introduction
Many industries want more data about the operation of
their business, and livestock farmers are no different. With
the advent of precision agriculture [9], many farmers have
more data than ever before, such as ground temperatures,
soil moisture, pasture covers, and individual stock iden-
tification to monitor growth rates and welfare. However,
precise and timely information about the numbers and lo-
cation of their animals is hard to come by, particularly on
larger farms. Indeed, counting livestock still remains a man-
ual task that is labour-intensive and prone to minor—but
significant—errors. It can also be disruptive to the animals,
as they must be run through a drafting race or a narrow
choke point. Currently, many farmers only count their ani-
mals once a month or so [14], and some farmers only count
their stock when they are being loaded on or off a truck, as
they are arriving or leaving the farm. Thus, many farmers
are interested in monitoring their livestock using a robust
automated system. Monitoring the distribution and popula-
tion of animal species with the passage of time is also a key
ingredient to successful nature conservation [21]. Animal
observation can be used for multiple tasks such as moni-
toring animal growth, animal distress, conducting surveys,
loading and unloading from holding pens, etc. This variety
of applications has motivated researches to develop some
sophisticated—yet faster—ways to achieve this target, but
this is still an emerging field as little work has been done
so far. Relevant to this research area, many challenges such
as diversity of background, species-specific characteristics,
spatial clustering of animals [18] arise. Researchers use
different statistical and biological methods [6, 10] to cope
with these challenges. Some have adopted different ap-
proaches and tried to solve similar tasks using techniques
like a template matching algorithm [22], AdaBoost classi-
fier [1], power spectral based techniques [13], Deformable
Part-based Model (DPM), Support Vector Machine (SVM)
[21] and Convolutional Neural Networks (CNNs) [2] for au-
tomatic processing and showed some good results. How-
ever, all of them have tested their techniques on images
where the animals are few in number and occupy a major-
ity of the image with high resolution. We wish to deal with
images having hundreds of small animals per image.
In this work we focus on counting sheep from Unmanned
Aerial Vehicle (UAV) video. Although this is easier than
counting cattle or other animals—due to the relatively uni-
form colour of sheep—it is not a trivial task, particularly
when dealing with hundreds or even thousands of sheep.
The goal of this work is to provide farmers with extremely
accurate information about how many sheep are in each of
their paddocks, using a commonly-available UAV to count
the sheep in situ. This involves no disruption to the animals,
the UAV is so high that they rarely notice it.
978-1-5386-9294-3/18/$31.00 ©2018 IEEE
Figure 1. Example test image, taken at 80 m from the ground con-
taining 139 sheep.
Training Testing
Data No. of Total No. of Total
Set Images Sheep Images Sheep
Sunny 2 267 4 582
Overcast 2 319 4 520
Mixed 4 586 8 1102
Table 1. Details of data sets used for both methods. All images are
(2048 ×1080 ×3) pixels in size.
Counting different objects in images was initially per-
formed using hand-crafted representations that need fea-
ture extraction for respective items [19] and then shifted
to deep learning algorithms to increase the speed and ac-
curacy. Currently, the researchers are focusing on solving
object detection using CNN [5, 11, 12, 17]. In the last
few years, many researchers have provided the modified
structures of CNN based models and delivered outstand-
ing results in classification tasks such as Ciresan [3], which
demonstrated very good performance on the NORB and
CIFAR-10 datasets. Krizhevsky [7] beat the record of clas-
sification on ImageNet 2012. Since then extended versions
of CNNs are being proposed such as, Region-based CNN
(R-CNN) [5], Fast R-CNN [4], Faster R-CNN [16], You
Only Look Once (YOLO) [15] and Single Shot MultiBox
Detector (SSD) [8]. Amongst all these techniques, YOLO
is the fastest but makes more localization errors as com-
pared to Faster R-CNN and is not suitable for detection of
tiny objects [15]. Faster R-CNN provides best results so far
but is prone to false positives in the background.
We now discuss our methodologies in Section 2, and
Section 3 will discuss the experimental results and compare
two different methods to detect and count sheep in aerial
images. Conclusions are presented in Section 4.
2. Methodology
In this section we present two different methods for
sheep detection in a frame. Method A is based on a basic
architecture of R-CNN, while Method B uses hand crafted
techniques. Results of both these techniques are compared
Data No. of Total
set Images Sheep
Sunny 146 884
Overcast 169 811
Mixed 318 1706
Table 2. Details of augmented training data sets for method A. All
images are (250 ×250 ×3) pixels in size.
and discussed in later sections.
Our main task is to detect and count the sheep using im-
ages of a paddock, which were taken from a UAV recorded
video in Palliser Ridge Ltd, a sheep and beef farm in the
Pirinoa region of New Zealand.
There are commonly hundreds of sheep in a 2048×1080
RGB video frame, and each sheep is roughly 10 ×20 pix-
els. The video was recorded at a height of 80 m above the
ground. Both methods use the data sets shown in Table 1,
for training and testing, where there a total of 4 images used
for training and 8 for testing. There is no overlap between
the images used for training and testing. Figure 1 shows an
example of one of the training images.
2.1. Method A: R-CNN
Method A is mainly composed of 2 parts: designing
training data, and applying R-CNN for detection.
2.1.1 Designing Training Data
The training images were very similar to each other, so in
order to augment the training data for Method A we cropped
out sub-images of 250 ×250 pixels and rotated them to dif-
ferent angles. From the 4 full-size images, we designed al-
most 400 small images to complete the training data set.
Among these 400 images, 82 images contained no object
of interest and have only background. We then designed
three different training data sets using rest of the 318 im-
ages; sunny, overcast and a mix of overcast and sunny im-
ages. Table 2 shows the number of images and sheep per
image in the augmented training data sets for Method A.
Each training image is of 250 ×250 ×3size and all
the sheep are labelled manually in them. This was quite a
lengthy process as the number of sheep per training image
varies in the range of [0, 18]. We used RGB colour im-
ages to keep this algorithm applicable for different animal
species for our later research. There are a total of 1706 ob-
jects (sheep) or Region of Interests (ROIs) in our complete
training data set. Those objects of interest which were not
completely in an image or were at the boundaries were not
labelled. If we had kept all such objects then it would only
increase the false positive detection on the background and
thus degrade our results. Each ground truth bounding box
has four components: (x, y, w, h), where xand yrepresents
Figure 2. Annotated training images.
the starting coordinates of the respective ROI and wand h
gives its dimension in terms of width and height respec-
tively. Example of overcast, sunny and partly-sunny images
with labelled sheep are shown in Figure 2.
2.1.2 Applying R-CNN for Detection
The R-CNN based object detection algorithm consists of
three different modules [5]:
1. Region proposals
2. Convolutional Neural Network
3. Class-specific linear Support Vector Machines (SVMs)
Girshick et al. [5] used the selective search algorithm [20]
for defining region proposals. The input images of training
data set are warped to 277×277×3pixel size internally be-
fore the computation of region proposals. Afterwards, fea-
tures are extracted from each region proposal using CNN.
The output of this layer is then fed to an SVM classifier
as well as a simple linear bounding box regressor to get a
confidence value of classification for each bounding box.
The centroids of each sheep are given by the centres of the
bounding boxes.
The network used by Girshick [5] has 4 convolutional
layers and 2 fully connected (FC) layers. We have used dif-
ferent architectures to check the impact of different combi-
nations of convolutional and FC layers. The main algorithm
for processing the input data remains the same but is applied
to different network architectures. Figure 3 shows the start-
ing network structure. We then systematically eliminated
Figure 3. Main architecture of R-CNN network.
convolutional layers (from right to left) to analyze the im-
pact of smaller architecture on our results. And surprisingly
the results were not affected to a very large extent. A de-
crease in precision of 5% and increase in recall of 6% was
observed when going down to a network with 1 initial con-
volutional layer and 1 fully connected layer of 2 neurons
followed by soft max layer.
2.2. Method B: Expert System
Method B is a hand-crafted technique that takes advan-
tage of the fact that in all the images, the sheep are quite
uniformly white, and generally the brightest objects in an
image. We used blob analysis on a brightness thresholded
greyscale image. Our detection algorithm generates ˆ
ci, the
estimated centroids of the sheep in the i-th image, as fol-
1. Generate the greyscale image Gibased on the RGB
image from the video.
2. Generate a binary image Biwhose pixels are 1 if the
corresponding pixel in Giis above a minimum bright-
ness threshold βand 0 otherwise.
3. Find and label all the 8-connected blobs in Bi.
4. Remove the blobs with an area less than a minimum
area threshold α.
5. Record the centroids of the remaining blobs as ˆ
6. Calculate the mean of the blobs’ areas as ¯
7. Record the blobs whose area is greater than γ¯
Aas two-
sheep blobs, by duplicating these centroids.
Note that βranges from 0 to 1, αis an integer number of
pixels, γis a number a little less than 2, and ˆ
ciis an Ni×2
matrix, whose j-th row is denoted by ˆ
iand contains the
xand ypixel locations of the centroid of the j-th sheep.
Obviously, Niis the number of sheep detected in the i-th
Although sheep tend to spread out when left alone in a
paddock, often there will be a group of two or more sheep
that are quite close together. Computing the centroids us-
ing only first five steps of the algorithm can fail to identify
multiple sheep for such groups. So, the last two steps of the
algorithm were added to improve the counting accuracy.
In order to test the accuracy of this algorithm we man-
ually labelled the centroids of sheep in the training image.
Let these ground truth centroids be denoted by ci. We then
wish to compare ciand ˆ
cito get a measure of the error,
however ciand ˆ
cimay contain differing numbers of rows
and their rows are likely to be in different orders. Further-
more, the most similar rows of ciand ˆ
ciare likely to not
be exactly equal due to differences in human and machine
For each row of ciwe calculate the following:
d= min
k0= arg min
And we say that ˆ
imatches c(k0)
iif d<δ, where δis
an error distance threshold in pixels, set at 10 pixels. We
must ensure that we don’t match c(k0)
ito another row of ˆ
so we remove the k0-th row of ci, before proceeding to the
next row of ˆ
ci. Our error metric is the number of unmatched
rows of ciplus the number of unmatched rows of ˆ
In order to test the sensitivity of this detection method
to the two parameters αand β, we varied them both over a
large range, and calculated the error described above. Fig-
ure 4 shows the error for the example frame in Figure 1,
which has bright, white sheep, and varying illumination. It
is clear that there are quite wide ranges of αand βthat per-
fectly detect the sheep.
3. Results
In order measure the performance of the two detection
methods, we followed the procedure in Section 2.2, to com-
pute the overlap between the true centroids and the esti-
mated centroids of the sheep. We calculated the precision
and recall, defined as:
Precision =NTP
Recall =NTP
where NTP, NFP , NFN are the number of true positive, false
positive and false negative sheep detections, respectively.
Obviously, the ideal is a system that has a precision and
recall of 100%. However, in a counting context, recall is
more important as it measures how many of the sheep were
detected, whereas precision measures how confident you
can be that a detected sheep is actually a sheep.
3.1. Method A
Tuning most of the hyper parameters for successful im-
plementation of R-CNN deals with pragmatic evidence and
testing. We experimented with different settings to deter-
mine the combination of parameters to provide the best re-
sults. While testing with different training options we ob-
served that a mini batch size beyond a certain range did not
Figure 4. Sensitivity matrix for the frame in Figure 1 for Method B,
the colours represent the number of detection errors, ranging from
white being no errors, to black being 10 or more errors.
give good results for our data set. The algorithm took too
long to find a minimum error for batch size more than 32
and needed too many epochs below the value of 10. Simi-
larly, different variations in results were observed when we
tried a negative overlap region between 0.1 and 0.5. Gir-
shick et al. [5] used the value of 0.3 in their experiments
as it best suited their data set, but it did not work for our
case. When we decreased the value to 0.2, it improved the
results with 7% recall, which was a huge difference. An-
other important parameter is the initial learning rate, which
also depends on the training data set and the type of object
under observation. Our network works well with this value
being around 105but above this value it gets stuck in a
local minimum and below this value it just oscillates and
takes too long to settle down somewhere. So, we used a
mini batch size of 15, an initial learning rate of 105and
negative overlap region of 0.2 while training on our data
Along with adjusting the options for training, we also
checked results using different architectures for the net-
work. These architectures are as follows:
1. 96 filters with different kernel sizes of 5×5,11 ×
11,21 ×21,25 ×25 and 29 ×29 in a network of 1
convolution layer followed by 1 FC and softmax layer.
2. R-CNN network with 2, 3, 4, 5 and 6 network layers
using a 6-layer network structure as shown in Figure 3.
3. A 6-layer R-CNN network using different kernel sizes
in each convolutional layer.
The graphs of precision and recall using previously dis-
cussed hyper parameters and above mentioned architectures
are shown in Figure 5. It can be seen that the use of larger
kernel size of around 21 ×21 and 25 ×25 give good results
when compared to other options. These filters are tested
only in the smallest network as different filter combinations
in larger networks did not give significant variations, as can
be observed from the bottom right graph in the same figure.
Smaller filters usually capture minute details of objects and
Figure 5. Precision and Recall curves using different settings. All figures show three curves of precision and three curves of recall for three
test data sets. The top left figure was obtained using a 2-layer R-CNN network (1 conv + 1 FC + softmax) with the shown kernel sizes for
the convolutional layer. The bottom left figure shows the respective curves for 2-, 3-, 4-, 5- and 6-layer R-CNN networks. The bottom right
shows results with a 6-layer R-CNN network using different kernel sizes in each network.
our current object is an oval shaped white blob. Thus, hav-
ing only one oval shape white blob removes the necessity
for larger network structures.
Different training data sets also contribute towards the
accuracy of the results. It was observed that although the
training set with only sunny images has half the number of
images when compared to the full data set, it was able to
train the network in almost the same way. The results were
quite poor while using training data set of only overcast im-
ages. The details of test images were presented in Table 1,
which were used for testing all discussed networks. Results
presented in Figure 5 were computed using the training data
set only.
It can be seen that best recall is observed for cloudy test
images as there is less illumination variations in them. And
highest recall for both types of test data sets is observed
using network of 2 layers and 96 (21 ×21) filters in the
convolutional layer. One such resultant test image is shown
in Figure 6.
3.2. Method B
Once the thresholds α,βand γhad been tuned on the 4
training images, Method B was applied to the 8 test images.
The values chosen were
α= 35, β = 0.75, γ = 1.6(5)
The precision and recall for Method B are shown in Table 3.
This method performs perfectly on the training images, and
Data set Precision Recall
Training 100.0% 100.0%
Testing 95.6% 99.5%
Table 3. Precision and Recall for Method B
only shows only slight degradation on the test images.
3.3. Discussion
While it is clear that the Method B outperforms Method
A, the CNN method does show some promise. Indeed there
is perhaps a way to combine the two methods to achieve
even better detection performance. When Method B fails, it
is due to an increase in false negatives, particularly around
background objects such as trees. Method A tends to have
very low false negatives, so perhaps this information can be
used to help Method B improve its discrimination.
4. Conclusion
In this work we have taken an important step forward
in the previously unexplored area of computer vision tech-
niques to detect and count sheep from UAV video. We have
used two totally different approaches to handle the task,
which both work well, although it can be seen from results
that more work is required for deep learning algorithms to
perform effective object detection, especially if the objects
are of very small size as compared to the background. The
Figure 6. 189 detected sheep with 9 false positive results in an
overcast test image having true count of 199, for method A.
hand-crafted method proposed here shows great promise for
sheep detection and counting.
Nonetheless, there are many areas still open for further
work, such as speeding up the algorithms, detecting the
fence lines, improving the robustness of the algorithms to
parameter selection, and the use of tracking to cope with
small mistakes in object detection. CNN-based techniques
may be of use in background discrimination, as well as de-
tecting less uniform livestock such as cattle, which can be
a variety of colours, or even multi-coloured. We hope that
this work will inspire other researchers to tackle some of
these challenges.
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... In the deep learning based object detection method, deep convolutional neural networks (CNNs) are capable of learning features from training-image data automatically, thus boosting accuracy without relying on artificial features. Sarwar et al. proposed a model based on region-based CNN to detect and count sheep in a paddock from unmanned aerial vehicle (UAV) video [6]. Soares et al. proposed a method for counting cattle in aerial images obtained by UAVs, based on CNNs and a graph-based optimization to remove duplicated animals detected in overlapped images [7]. ...
Conference Paper
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... In contrast, object detection identifies objects of the same type in an image and pinpoints their locations [7]. The main purpose of object counting applications is to calculate the number of cars on a road or parking lot [8][9][10][11], the number of people in a crowded area [12][13][14], the number of goats and sheep in an livestock farm [15,16], and the number of apples in an cultivated area [17]. ...
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Simple Summary: This study employs Fully Convolutional Regression Networks (FCRN) and U-Shaped Convolutional Network for Image Segmentation (U-Net) architectures tailored to the da-taset containing dropping images of dairy cows collected from three different private dairy farms in Nigde. The main purpose of this study is to detect the number of undigested grains in dropping images in order to give some useful feedback to raiser. It is a novel study that uses two different regression neural networks on object counting in dropping images. To our knowledge, it is the first study that counts objects in dropping images and provides information of how effectively dairy cows digest their daily rations. Abstract: Deep learning algorithms can now be used to identify, locate, and count items in an image thanks to advancements in image processing technology. The successful application of image processing technology in different fields has attracted much attention in the field of agriculture in recent years. This research was done to ascertain the number of indigestible cereal grains in animal feces using an image processing method. In this study, a regression-based way of object counting was used to predict the number of cereal grains in the feces. For this purpose, we have developed two different neural network architectures based upon Fully Convolutional Regression Networks (FCRN) and U-Net. The images used in the study were obtained from three different dairy cows enterprises operating in Nigde Province. The dataset consists of the 277 distinct dropping images of dairy cows in the farm. According to findings of the study, both models yielded quite acceptable prediction accuracy with U-Net providing slightly better prediction with a MAE value of 16.69 in the best case, compared to 23.65 MAE value of FCRN with the same batch.
... Nowadays, one application of the UAVs is in agriculture, for farm and livestock management. UAVs are used in this field for monitoring, behavior recognition, counting, detection, tracking, and livestock identification [1][2][3][4][5][6][7]. These applications are sometimes achieved by harnessing other technologies such as Flying Ad hoc Networks (FANET) [8] and wireless sensor networks [9]. ...
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Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. At the same time, the attitude controller records a settling time of 0.43 s, an overshoot of 2.50%, and a zero steady-state error. The implementation of the obstacle avoidance scheme shows that the distance threshold of 1 m is sufficient for the autonomous navigation of the quadrotor. Hence, the developed method is suitable for managing livestock with the average size of an adult sheep.
... Wang et al. [10] used the acoustic approach, with its capacity to properly distinguish sheep activity, which permits the use of more factors obtained from sheep behavior to estimate intake, their behavior classification, and identification. Moreover, [11] shows that Sheep may be counted using an R-CNN-based system on an unmanned aerial vehicle (UAV). Ma et al. [12] investigated sheep identification location and suggested a Faster-FCNN neural network model based on the Soft-NMS algorithm, which can monitor and identify Sheep in complicated raising situations. ...
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Sheep management and production enhancement are challenging for farmhouses due to the lack of dynamic sheep behaviors. Many researchers conducted machine learning-based studies to automate the sheep behavior monitoring process instead of manual assessment. However, there is a lack of utilization of sheep behaviors, which degrades the model performance after a few months. Moreover, behavior challenges, parameters, and analysis must be considered if one needs to conduct a machine learning-based study. In this paper, we present different challenges; what are the parameters of the sheep behaviors? Furthermore, how to analyze the sheep behaviors for automated machine learning systems to be helpful in the long term. Also, we review different studies for precision-based animal welfare and monitoring to have higher production and management capabilities.
Most sheep farms are established in places far away from urban areas, which inconveniences centralized management, and inventory information is an essential task for centralized management. Still, the traditional way of inventory management mainly uses manual statistics is time-consuming and laborious. To address the above problems, inspired by the crowd density counting model SFANet, we propose a sheep counting algorithm under surveillance video based on VGG16 as the frontend feature extractor and the backend as a dual-path multiscale fusion network for generating density maps and attention maps. Attaching ASPP module and CAN module to the back-end density map path not only extracts the multi-scale features of sheep in the image, but also handles their multi-scale variations based on contextual information. By adding the channel attention module SE to the attention map path, the SE channel attention mechanism enhances the channel where the sheep is located according to the importance of each feature channel, thereby making the network more focused on the target. The results show that the network has a mean absolute error of 1.28 and a mean square error of 1.70 under the outdoor sheep farm dataset, while the indoor environment is more complex with a mean absolute error of 5.82 and a mean square error of 7.36. The experimental results show that is helpful for sheep counting under surveillance video.KeywordsConvolutional neural networksSheep countingSurveillance videos
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This paper is concerned with nature conservation by automatically monitoring animal distribution and animal abundance. Typically , such conservation tasks are performed manually on foot or after an aerial recording from a manned aircraft. Such manual approaches are expensive, slow and labor intensive. In this paper, we investigate the combination of small unmanned aerial vehicles (UAVs or " drones ") with automatic object recognition techniques as a viable solution to manual animal surveying. Since no controlled data is available, we record our own animal conservation dataset with a quadcopter drone. We evaluate two nature conservation tasks: i) animal detection ii) animal counting using three state-of-the-art generic object recognition methods that are particularly well-suited for on-board detection. Results show that object detection techniques for human-scale photographs do not directly translate to a drone perspective, but that lightweight automatic object detection techniques are promising for nature conservation tasks.
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We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of bounding box priors over different aspect ratios and scales per feature map location. At prediction time, the network generates confidences that each prior corresponds to objects of interest and produces adjustments to the prior to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that requires object proposals, such as R-CNN and MultiBox, because it completely discards the proposal generation step and encapsulates all the computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on ILSVRC DET and PASCAL VOC dataset confirm that SSD has comparable performance with methods that utilize an additional object proposal step and yet is 100-1000x faster. Compared to other single stage methods, SSD has similar or better performance, while providing a unified framework for both training and inference.
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