Images Classification of Dogs and Cats using Fine-Tuned VGG Models

Mahardi,; Wang, I-Hung; Lee, Kuang-Chyi; Chang, Shinn‐Liang

doi:10.1109/ecice50847.2020.9301918

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…Digital image classification was introduced to distinguish between cats and dogs using deep learning with the VGG model algorithm. The accuracy evaluation results indicated a high accuracy of 98.56% for the training data and 84.07% for the testing data [25]. Additionally, the classification of digital photographs to discern various groups of dog species employed various deep learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Based on a comprehensive review of literature and research concerning the integration of IoT (Internet of Things) and DIP (Digital Image Processing) in the management of animal husbandry, particularly to mitigate threats posed by dogs to farm animals, two distinct approaches have emerged. The first involves the application of machine learning image processing techniques for the classification of various animal types [22], [23], [24], [25], [26]. This method exhibits the capacity to differentiate animals within digital images, with accuracy levels contingent upon factors such as classification techniques employed, and the volume of data used to construct the model.…”

Section: Introductionmentioning

confidence: 99%

“…This method exhibits the capacity to differentiate animals within digital images, with accuracy levels contingent upon factors such as classification techniques employed, and the volume of data used to construct the model. Notably, the utilization of deep learning, supported by comprehensive and highly accurate training data, achieves accuracy rates surpassing 90% [23], [25]. Nevertheless, relying solely on machine learning techniques lacks real-time surveillance and tracking capabilities, prompting efforts to address these limitations by advocating for the combined use of IoT and machine learning for animal detection and monitoring across diverse applications [27], [28], [29], [30], [31].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

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One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

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“…There are two types of VGG models: VGG16 and VGG19. The difference between them is the number of layers with VGG16 which is designed with 16 layers while VGG19 which is designed with 19 layers [10].…”

Section: Datasetmentioning

confidence: 99%

Fusion of pretrained CNN models for cat breed classification: A comparative study

Hamdi,

Sunaryo,

Prasetyo

2023

E3S Web Conf.

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Cat is one of the most popular domestic animals that human has domesticated for a long time, since then, there have been many breeds that can be difficult to identify with each breed having different health issues and care requirement, to resolve this problem we used Convolutional Neural Network (CNN) a widely used artificial intelligence deep learning model that has been used in many image classification problem, in this study we explored 11 different types of CNN-Based model architecture to be used in a fusion-based technique and fine-tune the model to further increase its performance, our results show that fusion model is a promising technique in classifying cat breeds that outperforms all of the individual CNN- Based model architecture with the 3 fusion model having an accuracy of 0.9053, precision of 0.9075, recall of 0.9053, and F1 score of 0.9016, additionally, fine-tuning only shows a small effect in increasing the fusion model performance.

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“…The app has been trained to recognize 14 types of cats with an average accuracy of the finalized model of 81.74%. Images of dogs and cats were classified using CNN by Mahardi et al [5]. They tried to build an image classifier to recognize various breeds of dogs and cats using retrained VGG models.…”

Section: Introduction Cat (Felis Catusmentioning

confidence: 99%

Cat Breeds Classification Using Compound Model Scaling Convolutional Neural Networks.

Karlita¹,

Choirunisa²,

Asmara³

et al. 2022

Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021)

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Cats are one of the most popular animals in the world. Many cat breeds in the world are only about 1%. Therefore, most are dominated by mixed cats or domestic cats. Nevertheless, there are so many different types of cat breeds in the world that it is sometimes difficult to identify them. Therefore, we need a system that can recognize and classify the types of cat breeds automatically. In this study, we used one of the deep learning methods that can recognize and classify an object, a Convolutional Neural Networks (CNN). The EfficientNet-B0 architecture was used as a model to extract image features automatically. The collection of nine different cat breeds containing 2700 images was used as a working dataset fed into the EfficientNet-B0 architecture. Based on the experiments, the system succeeds in classify cat breeds images, and the best model has achieved classification accuracy of 95%.

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Images Classification of Dogs and Cats using Fine-Tuned VGG Models

Cited by 12 publications

References 4 publications

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Fusion of pretrained CNN models for cat breed classification: A comparative study

Cat Breeds Classification Using Compound Model Scaling Convolutional Neural Networks.

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