Dog breed identification is essential for many reasons,particularly for understanding individual breeds′conditions,health concerns,interaction behavior,and natural instinct.This paper presents a solution for identify...Dog breed identification is essential for many reasons,particularly for understanding individual breeds′conditions,health concerns,interaction behavior,and natural instinct.This paper presents a solution for identifying dog breeds using their images of their faces.The proposed method applies a deep learning based approach in order to recognize their breeds.The method begins with a transfer learning by retraining existing pretrained convolutional neural networks(CNNs)on the public dog breed dataset.Then,the image augmentation with various settings is also applied on the training dataset,in order to improve the classification performance.The proposed method is evaluated using three different CNNs with various augmentation settings and comprehensive experimental comparisons.The proposed model achieves a promising accuracy of 89.92%on the published dataset with 133 dog breeds.展开更多
The ability to automatically recognize sheep breeds holds significant value for the sheep industry.Sheep farmers often require breed identification to assess the commercial worth of their flocks.However,many farmers s...The ability to automatically recognize sheep breeds holds significant value for the sheep industry.Sheep farmers often require breed identification to assess the commercial worth of their flocks.However,many farmers specifically the novice one encounter difficulties in accurately identifying sheep breeds without experts in the field.Therefore,there is a need for autonomous approaches that can effectively and precisely replicate the breed identification skills of a sheep breed expert while functioning within a farm environment,thus providing considerable benefits the industry-specific to the novice farmers in the industry.To achieve this objective,we suggest utilizing a model based on convolutional neural networks(CNNs)which can rapidly and efficiently identify the type of sheep based on their facial features.This approach offers a cost-effective solution.To conduct our experiment,we utilized a dataset consisting of 1680 facial images which represented four distinct sheep breeds.This paper proposes an ensemble method that combines Xception,VGG16,InceptionV3,InceptionResNetV2,and DenseNet121 models.During the transfer learning using this pre-trained model,we applied several optimizers and loss functions and chose the best combinations out of them.This classification model has the potential to aid sheep farmers in precisely and efficiently distinguishing between various breeds,enabling more precise assessments of sector-specific classification for different businesses。展开更多
基金the Royal Golden Jubilee(RGJ)Ph.D.Programme under the Thailand Research Fund(No.PHD/0053/2561)。
文摘Dog breed identification is essential for many reasons,particularly for understanding individual breeds′conditions,health concerns,interaction behavior,and natural instinct.This paper presents a solution for identifying dog breeds using their images of their faces.The proposed method applies a deep learning based approach in order to recognize their breeds.The method begins with a transfer learning by retraining existing pretrained convolutional neural networks(CNNs)on the public dog breed dataset.Then,the image augmentation with various settings is also applied on the training dataset,in order to improve the classification performance.The proposed method is evaluated using three different CNNs with various augmentation settings and comprehensive experimental comparisons.The proposed model achieves a promising accuracy of 89.92%on the published dataset with 133 dog breeds.
文摘The ability to automatically recognize sheep breeds holds significant value for the sheep industry.Sheep farmers often require breed identification to assess the commercial worth of their flocks.However,many farmers specifically the novice one encounter difficulties in accurately identifying sheep breeds without experts in the field.Therefore,there is a need for autonomous approaches that can effectively and precisely replicate the breed identification skills of a sheep breed expert while functioning within a farm environment,thus providing considerable benefits the industry-specific to the novice farmers in the industry.To achieve this objective,we suggest utilizing a model based on convolutional neural networks(CNNs)which can rapidly and efficiently identify the type of sheep based on their facial features.This approach offers a cost-effective solution.To conduct our experiment,we utilized a dataset consisting of 1680 facial images which represented four distinct sheep breeds.This paper proposes an ensemble method that combines Xception,VGG16,InceptionV3,InceptionResNetV2,and DenseNet121 models.During the transfer learning using this pre-trained model,we applied several optimizers and loss functions and chose the best combinations out of them.This classification model has the potential to aid sheep farmers in precisely and efficiently distinguishing between various breeds,enabling more precise assessments of sector-specific classification for different businesses。
