Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset

Abstract

This study evaluates the performance of various deep learning models, specifically DenseNet, ResNet, VGGNet, and YOLOv8, for wildlife species classification on a custom dataset. The dataset comprises 575 images of 23 endangered species sourced from reputable online repositories. The study utilizes transfer learning to fine-tune pre-trained models on the dataset, focusing on reducing training time and enhancing classification accuracy. The results demonstrate that YOLOv8 outperforms other models, achieving a training accuracy of 97.39% and a validation F1-score of 96.50'%. These findings suggest that YOLOv8, with its advanced architecture and efficient feature extraction capabilities, holds great promise for automating wildlife monitoring and conservation efforts.

Full text

Journal of Artificial Intelligence and Capsule Networks (ISSN: 2582-2012)
www.irojournals.com/aicn/
Journal of Artificial Intelligence and Capsule Networks, December 2024, Volume 6, Issue 4, Pages 415-435 415
DOI: https://doi.org/10.36548/jaicn.2024.4.003
Received: 11.09.2024, received in revised form: 18.10.2024, accepted: 02.11.2024, published: 11.11.2024
© 2024 Inventive Research Organization. This is an open access article under the Creative Commons Attribution-NonCommercial International (CC BY-NC 4.0) License
Transfer Learning for Wildlife
Classification: Evaluating YOLOv8 against
DenseNet, ResNet, and VGGNet on a
Custom Dataset
Subek Sharma1, Sisir Dhakal2, Mansi Bhavsar3
1,2Department of Electronics and Computer Engineering, Institute of Engineering (IOE)
Pashchimanchal Campus, Tribhuvan University, Pokhara, Gandaki, Nepal
3Department of Computer Information Science, Minnesota State University, Mankato
Email: 1subeksharma11@gmail.com
Abstract
This study evaluates the performance of various deep learning models, specifically
DenseNet, ResNet, VGGNet, and YOLOv8, for wildlife species classification on a custom
dataset. The dataset comprises 575 images of 23 endangered species sourced from reputable
online repositories. The study utilizes transfer learning to fine-tune pre-trained models on the
dataset, focusing on reducing training time and enhancing classification accuracy. The results
demonstrate that YOLOv8 outperforms other models, achieving a training accuracy of 97.39%
and a validation F1-score of 96.50‘%. These findings suggest that YOLOv8, with its advanced
architecture and efficient feature extraction capabilities, holds great promise for automating
wildlife monitoring and conservation efforts.
Keywords: Convolutional Neural Network (CNN), Endangered Species Detection, Image
Classification, Transfer Learning, YOLO.
1. Introduction
Each living creature in the world plays a vital role in maintaining the balance of the
ecosystem. However, activities like poaching and illegal trade, reduction of prey base, habitat
loss and degradation, and human-wildlife conflict have led to a rapid decline in the number of

Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
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some species, including critically endangered ones [1]. Currently, wildlife monitoring is done
using camera traps that capture the images of animals, which are then analyzed and studied by
humans. This is time-consuming, tedious, and prone to errors.
The introduction of various deep learning techniques and their use in computer vision
shows optimistic results. Convolutional Neural Networks (CNNs), introduced by LeCun et al.
in 1998 [2], have been a significant milestone in image classification tasks [3]–[5]. In these
networks, images are fed through convolutional and pooling layers for feature extraction,
followed by fully connected layers. Transfer learning, highlighted by Pan and Yang [6], is fine-
tuning pre-trained models on large datasets for specified tasks that significantly reduce training
time with increased performance. Densely connected convolutional networks, popularized by
Huang et al. in 2017 [7], and residual networks, put forward by He et al. in 2016 [8], are deep
and complex networks that facilitate effective feature extraction. However, VGGNet, proposed
by Simonyan and Zisserman in 2014 [9], is somewhat computationally expensive, although its
results are excellent in some cases. YOLOv8 is an evolution in Redmon’s object detection and
has shown remarkable results in various computer vision tasks [10] .
El Abbadi et al.[11], achieved a classification accuracy of 97.5% using a deep
convolutional neural network model for the automated classification of vertebrate animals,
thereby demonstrating the effectiveness of deep learning in animal recognition tasks. Similarly,
Villa et al. [12], demonstrated in their study that very deep convolutional neural networks
achieved 88.9% accuracy in a balanced dataset and 35.4% in an unbalanced one for automatic
species identification in camera-trap images, marking a notable advancement in non-intrusive
wildlife monitoring. In 2020 Ibraheam et al. [13], reported in their research that their deep
learning-based system achieved 99.8% accuracy in distinguishing between animals and
humans, and 97.6% in identifying specific animal species, significantly improving safety in
wildlife-human and wildlife-vehicle encounters.
Brust et al.[14], have illustrated that ResNet50 outperformed VGG16 and Inception v3
on a wildlife image dataset, with the highest accuracy of 90.3 %. It provided insights into the
strengths and weaknesses of different CNN architectures for wildlife classification [15].
Similarly, Beery et al. [16], validated the same on a challenging dataset of wildlife images with
occlusions, varying lighting conditions, and motion blur. They found the Faster R-CNN model
achieved the highest accuracy of 88.7 %, indicating the importance of model robustness in real-
world applications. Similarly, Yilmaz et al. [17], demonstrated that the YOLOv4 algorithm

Subek Sharma, Sisir Dhakal, Mansi Bhavsar
Journal of Artificial Intelligence and Capsule Networks, December 2024, Volume 6, Issue 4 417
achieved a high classification accuracy of 92.85 % for cattle breed detection, emphasizing its
effectiveness in wildlife classification tasks. The research in [18] evaluates deep learning-based
models, including SSD, YOLOv4, and YOLOv5, on the CUB-200-2011 dataset. The YOLOv4
model outperforms state-of-the-art methods with high accuracy and precision. Hung Nguyen
et al. [19] achieved 96.6 % accuracy in detecting animal images and 90.4 % accuracy in species
identification using deep learning, highlighting its potential for automatically monitoring
wildlife.
This research addresses some of the questions regarding the selection of deep learning
models for practical wildlife conservation tasks, such as which models provide the best
accuracy and efficiency, how YOLOv8 compares to DenseNet, ResNet, and VGGNet, and
what challenges and limitations exist in applying these models to wildlife conservation. The
results show that YOLOv8 is best suited for automated wildlife monitoring with much better
accuracy and efficiency than models such as DenseNet, ResNet, and VGGNet. This work will
supply essential guidance to researchers and practitioners on the choice of appropriate models
for endangered species conservation. Addressing these research questions is crucial as the
current methodologies for wildlife monitoring are labor-intensive and error-prone. This work
aims to fill this gap by systematically evaluating different deep-learning models and providing
essential guidance to researchers and practitioners on the choice of appropriate models for
endangered species conservation.
The remaining work is organized with Section 2 that describes the dataset,
preprocessing steps, and methodologies used in this study. Section 3 which elaborates on the
performance and evaluation metrics for the machine learning models. Section 4 presenting and
discussing the experimental results. Finally, Section 5 with a summary of findings and
suggestions for future work.
2. Methodology
2.1 Dataset Description
2.1.1 Species Coverage: The dataset includes 23 carefully selected species based on its
conservation status and the need for monitoring. These species cover many endangered
animals, including mammals, reptiles, and amphibians as shown in Table 1.

Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
ISSN: 2582-2012 418
Table 1. Different Classes of Species in the Dataset with their Common and Scientific Names.
Scientific Name
Common Name
Quantity
Ailurus fulgens
Red Panda
25
Aonyx cinerea
Asian Small-Clawed Otter
25
Arctictis binturong
Binturong
25
Bubalus arnee
Wild Water Buffalo
25
Catopuma temminckii
Asian Golden Cat
25
Cervus duvaucelii
Swamp Deer (Barasingha)
25
Elephas maximus
Asian Elephant
25
Rhinoceros unicornis
Indian Rhinoceros
25
Indotestudo elongata
Elongated tortoise
25
Lutrogale perspicillata
Smooth-Coated Otter
25
Macaca assamensis
Assam Macaque
25
Manis pentadactyla
Chinese Pangolin
25
Melanochelys tricarinata
Three-keeled land turtle
25
Melursus ursinus
Sloth Bear
25
Neofelis nebulosa
Clouded Leopard
25
Panthera pardus
Leopard
25
Panthera tigris
Bengal Tiger
25
Prionailurus viverrinus
Fishing Cat
25
Ratufa bicolor
Black Giant Squirrel
25
Uncia uncia
Snow Leopard
25
Ursus thibetanus
Asian Black Bear
25
Varanus flavescens
Yellow Monitor Lizard
25
Viverra zibetha
Large Indian Civet
25

Subek Sharma, Sisir Dhakal, Mansi Bhavsar
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2.1.2 Data Collection
The study began by collecting the data from different sources on the internet. Each class
is represented by 25 filtered images, resulting in 575 images in the dataset. We maintained a
balanced dataset to reduce the bias towards any particular species and to perform the balanced
model training. The internet houses a huge amount of data, but finding and gathering useful
ones is still challenging. To collect images of various animal species, we utilized online
repositories like iNaturalist and ZooChat [20], [21]. The good thing about using such sites is
their authenticity and fair use policy. For the same reason, we did not use the images shown in
regular Google searches or try to automate the process.
2.2 Data Preprocessing
We divided the preprocessing of the image data into the following steps.
2.2.1 Aspect Ratio Standardization: We preprocessed all the images utilized for our study
to have an aspect ratio of 1:1 and a resolution of 400 x 400. We added padding whenever
required, ensuring we did not lose any information from the images during resizing
2.2.2 Data Normalization: We normalized every image to increase accuracy and speed up
the model's convergence. It also reduced the variance in the training data.
2.2.3 Splitting: We split the dataset into train (80%) and val (20%) sets. We used the split-
folders Python package to split the dataset while maintaining the original distribution
[22]. Babu et al.'s study examines the impact of image data splitting on the performance
of machine learning models [23].
2.2.4 Data Augmentation: For better generalization, we increased the diversity of data by
applying different augmentation techniques, as suggested by Shorten and Khoshgoftaar
in 2019 [24]. Data augmentation improves the performance of deep learning systems
by artificially inflating the size of their training dataset through various data
manipulation methods, including rotation, flipping, translation, and other color-oriented
transformations. Such transformations help the model become robust against overfitting
to specific patterns in the training data and instead enable the model to generalize better
on new data. Through augmentation, the model learns more informative features that
are stable across various real-world variations. Hence, it becomes insensitive to the

Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
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variations in input conditions. Table 2 displays the final set of parameters for data
augmentation.
Table 2. Different Augmentation Parameters used for Training.
Augmentation
Description
Shearing
Random shearing of the image by a maximum of 20%.
Zooming
Random zooming in or out of the image by a maximum of
20%.
Horizontal flip
Random horizontal flipping of the image.
Rotation
Randomly rotating the image by up to 20 degrees.
Width shifting
Randomly shifting the image horizontally by 10% of the image
width.
Height shifting
Randomly shifting the image vertically by a maximum of 10%
of the image height.
2.3 Convolutional Neural Network
Convolutional Neural Networks (Figure1) are a special kind of neural network designed
to work with grid data images. They learn by extracting the features from input data through
convolution and pooling operations followed by fully connected layers [2]. CNNs have played
a pivotal role in the advancement of computer vision and related tasks. They are especially
effective in performing tasks such as image recognition, object detection, and classification
[25-27]. The methodology is depicted in Figure 2.
Figure 1. Convolutional Neural Network. [27]

Subek Sharma, Sisir Dhakal, Mansi Bhavsar
Journal of Artificial Intelligence and Capsule Networks, December 2024, Volume 6, Issue 4 421
Figure 2. Methodology
2.4 Transfer Learning
Transfer learning is an approach to deep learning that enables researchers and
developers to use the previously trained model in a huge dataset and implement it in
downstream tasks [3]. It is especially useful when we have limited data to train the model.
Also, it significantly reduces the training time because the feature extraction part remains
unchanged. However, it may fail when there is a significant mismatch between the target
domain task and the source task.
These are some of the transfer learning models (Figure 3) used with our dataset.
2.4.1 DenseNet: It was introduced by Gao Huang and colleagues in 2017 [4]. It connects
each layer with all other layers densely, meaning each layer receives input from the
preceding layers. By efficiently cutting the parameters' requirements and boosting the
network’s ability by reusing the features extracted, it allows for a deeper network.
However, the dense connectivity may lead to increased computational cost and memory
consumption.

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2.4.2 ResNetV2: It was introduced by Kaiming He et al. in 2016 [5]. It utilizes residual
blocks, which are shortcut connections that bypass one or multiple layers. It also allows
the network to learn residual functions instead of direct mapping. This architecture
supports very deep networks without degradation problems.
2.4.3 VGG: It was introduced by Karen Simonyan and Andrew Zisserman in 2014 [6]. It is
known for its simplicity and depth, achieved by stacking small 3*3 convolutional
layers, increasing the model's depth and parameters. The max pooling layers follow the
convolutional layers to handle the volume size and end with the fully connected layers.
Figure 3. Architecture of Different Models used in this Study
2.4.4 YOLOv8: YOLOv8 is built upon the object detection framework introduced by Joseph
Redmon, with contributions from many researchers over successive versions. It
enhances speed and accuracy through an advanced backbone architecture, refined loss

Subek Sharma, Sisir Dhakal, Mansi Bhavsar
Journal of Artificial Intelligence and Capsule Networks, December 2024, Volume 6, Issue 4 423
functions, and anchor-free detections [28]. Figure 4 shows the architecture of the
YOLOv8.
Figure 4. Reference Image for YOLOv8 Architecture.
2.5 Model building
We loaded the pre-trained models with their respective weights, made these weights
untrainable(frozen), and replaced the last layer with custom, fully connected layers
corresponding to the number of classes in our dataset. We added a GlobalAveragePooling2D
layer to reduce the feature maps' spatial dimensions and prevent overfitting. This layer is
followed by a Dense layer with 128 neurons and activated by ReLU to introduce non-linearity
and learn more complex features. Finally, we added a Dense layer with 23 units activated by
softmax to match the number of classes in our dataset, enabling the model to output the class
probabilities, as shown in Figure 5. We then trained the models using Adam as the optimizer
and cross-entropy as the loss function for 100 epochs with a batch size of 32 images.
Meanwhile, we used a validation set to monitor the progress to avoid plateauing and adjusting
the learning rate dynamically.

Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
ISSN: 2582-2012 424
Figure 5. Illustration of Transfer Learning
2.6 Hyperparameters
We experimented with multiple sets of hyperparameters, including learning rates,
optimizers, batch sizes, and schedulers, thereby selecting the most favorable settings of
hyperparameters that showed optimal performance. Table 3 lists the final set of
hyperparameters.
2.6.1 Categorical Cross Entropy: Categorical Cross Entropy (CCE) is used for multi-class
classification tasks. It measures the difference between the true class labels and the predicted
class probabilities. The formula sums the negative log-likelihood of the true class's predicted
probability across all classes. We have used CCE as our loss function.
  

  (1)
2.6.2 Binary Cross Entropy for One Neuron: Binary Cross Entropy (BCE) is used for binary
classification tasks. The binary cross-entropy loss measures the dissimilarity between predicted
probability distributions and the ground truth labels. The formula is the negative log-likelihood
of the predicted probability if the true label is 1 and 1 minus the predicted probability if the
true label is 0. The BCE is utilized in each output layer neuron and used in the YOLOv8
classification task.
      󰇛  󰇜   (2)
2.6.3 AdamW Optimizer: AdamW stands for Adaptive Moment Estimation with Weight
Decay. It is an extension of the Adam optimizer, including weight decay, to improve
generalization by preventing overfitting. AdamW adjusts the learning rate based on the

Subek Sharma, Sisir Dhakal, Mansi Bhavsar
Journal of Artificial Intelligence and Capsule Networks, December 2024, Volume 6, Issue 4 425
gradients’ first and second moments and consists of a weight decay term to penalize large
weights.
  󰞎  󰞎

       󰞎 (3)
Where:
● 󰞎 is the parameter at time step t
●  is a learning rate
●  is the biased first-moment estimate
● 
 is the biased second raw moment estimate
●  is a small constant to prevent division by zero
● weight_decay is the weight decay term [24].
Table 3. Hyperparameters.
Hyperparameters
Value
Learning Rate
Adaptive
Batch Size
32
Epochs
100
Early Stopping
{ReduceLROnPlateau,
Validation Accuracy}
Optimizers
{Adam, AdamW}
3. Performance and Evaluation Metrics
The performance of the models is not examined by accuracy alone. In addition to
accuracy, other metrics like f1-score, precision, recall, and loss were employed for the
evaluation. Precision is an indicator of the accuracy of model predictions i.e., the ratio of the
true positive predictions to the total number of positive predictions made by the model. The
recall is an indicator of the model’s ability to identify all the relevant classes i.e., ratio of true
positive predictions to the total number of actual positive instances. The F1 score provides the
single value for the evaluation of the model and is the harmonic mean of precision and recall.

Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
ISSN: 2582-2012 426
The loss gives insights into how well the model’s prediction matches the true outcomes and is
the difference between the predicted values and the actual values.
   
 (4)
 
 (5)
   
 (6)
   
 (7)
4. Results and Discussion
4.1 Results
This study evaluated multiple deep-learning models for identifying endangered animal
species from wildlife images. The results show the varying performance across the models.
YOLOv8 outperformed the other models, and the DenseNet and Resnet models also did well,
as their results were close to those of YOLOv8. In contrast, the VGG and Vanilla CNN models
faced significant challenges. Table 4 shows the performances of different models.
Table 4. Performance of Different Deep Learning Models on the Dataset
S.N
Model
Name
Accuracy(%)
Loss
F1-
Score(%)
Precision(
%)
Recall(%)
1
DenseNet
169
Train = 98.27
Val = 93.91
Train =
0.0265
Val =
0.0482
Train =
95.22
Val = 93.95
Train =
99.46
Val = 98.94
Train =
83.76
Val = 80.87
2
DenseNet
201
Train = 98.80
Val = 92.17
Train =
0.0161
Val =
0.0386
Train =
96.36
Val = 92.22
Train =
99.95
Val = 94.62
Train =
89.97
Val = 76.52

Subek Sharma, Sisir Dhakal, Mansi Bhavsar
Journal of Artificial Intelligence and Capsule Networks, December 2024, Volume 6, Issue 4 427
3
Resnet 101
V2
Train = 98.74
Val = 92.17
Train =
0.0083
Val =
0.9217
Train =
97.36
Val = 92.09
Train =
99.42
Val = 98.00
Train =
95.72
Val = 85.22
4
Resnet 152
V2
Train = 98.20
Val = 93.04
Train =
0.0104
Val =
0.0271
Train =
95.79
Val = 93.22
Train =
99.19
Val = 97.14
Train =
94.01
Val = 88.70
5
VGG 16
Train = 46.00
Val = 33.91
Train =
0.5619
Val =
0.5994
Train =
42.89
Val = 28.65
Train = 0.00
Val = 0.00
Train =
0.00
Val = 0.00
6
VGG 19
Train = 32.67
Val = 33.04
Train =
0.6222
Val =
0.6449
Train =
29.82
Val = 31.19
Train = 0.00
Val = 0.00
Train =
0.00
Val = 0.00
7
YOLOV8
Train = 97.39
Val = 99.13
Train =
0.0118
Val = NA
Train =
96.50
Val = 99.12
Train =
96.81
Val = 99.28
Train =
96.52
Val = 99.13
4.1.1 DenseNet: DenseNet architectures showed strong performance across all metrics.
DenseNet 169 achieved a training accuracy of 98.27% and a validation accuracy of 93.91%.
Also, it had F1 scores of 95.22% in training and 93.95% in validation. Impressively, DenseNet
201 outperformed by a slightly better training accuracy of 98.80% and had F1-scores of 96.36%
in training and 92.22% in validation, though its validation accuracy was somewhat lower at
92.17%. Figure 6 shows training and validation set loss curves decreasing rapidly, finally
reaching a plateau, indicating good learning and model convergence. The relatively smooth
curve showed a minimal gap in Figure 7, indicating good generalization and no significant
overfitting.

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Figure 6. Loss curve of DenseNet169 Figure 7. Accuracy curve of DenseNet169.
4.1.2 ResNet: The ResNets architecture also showed promising results. Resnet 101 V2
reached a training accuracy of 98.74% and a validation accuracy of 92.17%. Its F1
scores were 97.36% in training and 92.09% in validation, showing robust performance.
Resnet 152 V2 showed a training accuracy of 98.20% and a validation accuracy of
93.04%, with high F1-scores of 95.79% in training and 93.22% in validation. The
training and validation data loss plunged drastically initially and finally tended towards
low values, similar to DenseNet169, as shown in Figure 8. The very close train and
validation loss curves indicated that the model generalized well without overfitting—a
similar trend in the training and validation accuracy from Figure 9.
Figure 8. Loss curve of ResNet152 Figure 9. Accuracy curve of ResNet152

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Journal of Artificial Intelligence and Capsule Networks, December 2024, Volume 6, Issue 4 429
4.1.3 VGG: In contrast, the VGG architectures underperformed significantly compared to
DenseNet and ResNet models. VGG 16 and VGG 19 showed much lower training
accuracies (46.00% and 32.67%, respectively) and validation accuracies (33.91% and
33.04%, respectively). Their F1 scores were also considerably lower, with VGG 16 at
42.89% for training and 28.65% for validation and VGG 19 at 29.82% for training and
31.19% for validation. The training loss decreased steadily, while the validation loss
followed a different pattern, decreasing even slower and with apparent variance, as
shown in Figure 10. There was a visible gap between training and validation loss,
showing the possible overfitting phenomenon, i.e., a model fitting much better with the
training set than the validation set. Also, Figure 11 shows oscillations in the training
and validation accuracies.
Figure 10: Loss curve of VGG19.
Figure 11:
Figure 10. Loss curve of VGG19 Figure 11. Accuracy curve of VGG19
4.1.4 YOLOV8: The YOLOV8 model achieved an accuracy of 97.39% in training and
99.13% in validation with a shallow loss of 0.01175, which showed that despite being
known for detection purposes, it surprisingly worked well for classification tasks. The
F1 score was 96.5% in training and 99.12% in validation. Figure 12 shows that the loss
decreased rapidly and plateaued early in training, which shows the model’s
effectiveness for fast convergence during transfer learning. Unlike the loss, the
accuracy oscillated slightly before it finally plateaued near 50 epochs, as indicated in
Figure 13.

Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
ISSN: 2582-2012 430
Figure 12. Loss Curve of YOLOv8 Figure 13. Accuracy Curve of YOLOv8
4.1.5 CNN: We validated different architectures for the vanilla CNN with the same dataset.
However, we did not get satisfactory results. Since the dataset contained images of
species across 23 classes, simple, shallow CNN could not converge effectively.
Nevertheless, we tried adjusting various parameters like the number of layers,
regularization techniques, loss functions, and augmentations. Despite these efforts, the
performance metrics remained mediocre.
4.2 Discussions
The experimental analysis shows the metrics of various deep-learning models in
identifying endangered animals on the custom endangered wildlife dataset, as shown in Figure
14. The dataset was carefully created from reputable online databases, ensuring the species’
authenticity and relevance. The motto was to train a model that could recognize vulnerable
species and assist in their proper monitoring. Furthermore, the other side of this study involved
finding the best available architecture for this task. We experimented with various such
architectures and analyzed their performance under different metrics.

Subek Sharma, Sisir Dhakal, Mansi Bhavsar
Journal of Artificial Intelligence and Capsule Networks, December 2024, Volume 6, Issue 4 431
Figure 14. Comparison of Metrics for Different Models
We found that newer version of all the models showed stronger performance. VGG was
the oldest among the models, so it performed poorly. Its accuracy was way below the vanilla
CNN. Conversely, DenseNet and ResNet offered significantly better performance, so we can
easily use them for related tasks. Also, we noted that these networks’ newer and deeper versions
did not provide any impactful difference across various metrics.
In addition, we implemented transfer learning and did not train them from scratch,
benchmarking their ease of use in downstream tasks like this. We had to freeze the feature
extraction layers and only trained the last few fully connected layers. Doing this saved the time
and computing required without compromising their performance.
Alongside standard CNN models like ResNet, DenseNet, and VGGNet, specially
designed for classification tasks, we also experimented with YOLOv8. YOLOs are primarily
designed and used as models for detection and segmentation tasks. To the great surprise, the
metrics surpassed other standard classification models. Thus, YOLO might work well for
classification tasks for custom datasets in similar niches.
YOLOv8’s superior performance might be due to its advanced architecture, which
combines efficient feature extraction with fast processing capabilities by integrating
components like CSPNet (Cross Stage Partial Networks) and PANet (Path Aggregation
Network) [29] [30]. CSPNet reduces the computational cost while maintaining accuracy. It
divides the feature map into two parts and merges them through the cross-stage hierarchy. On
the other hand, PANet enhances the information flow between various layers, which is
excellent for object detection across multi-scales. But, this proved beneficial for classification

Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset
ISSN: 2582-2012 432
tasks, too. Even more importantly, the multiscale capability of YOLOv8 allows handling
images with object sizes that can have significant variations and differing resolutions. It also
has advanced augmentations, such as mosaic augmentation, which places four training images
into one with diverse contexts in one image and hence helps the model generalize better to
varying lighting and environmental conditions in training. All these features, together, carry
out fast processing and effective feature extraction.
5. Conclusion
To sum up, we performed experimental analysis on transfer learning of various deep-
learning CNN architectures on the custom dataset containing images of endangered mammals
from Nepal. The findings featured the superior performance of YOLOv8 compared to other
models like DenseNet, ResNetV2, and VGG. It demonstrated higher accuracy, precision, and
recall, making it practical for similar classification tasks. Although lagging by a narrow margin,
other models like ResNet and DenseNet also performed well. Transfer learning proved
beneficial, drastically reducing training time and data required while maintaining high
performance, which is crucial for tasks with limited data availability.
In the future, we will explore the ensemble methods that combine the strengths of
multiple CNN architectures potentially enhancing classification accuracy and robustness,
especially in diverse and challenging environmental conditions. We will also incorporate real-
time monitoring capabilities in the future to provide feedback for conservation and take timely
action in preventing the loss of endangered species. Hence, this study shows the reliance and
robustness of deep-learning models in monitoring wildlife.
6. Acknowledgement
The authors thank their supervisor, Dr. Mansi Bhavsar, for their invaluable guidance
and support. Both authors contributed equally to this work.
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