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Image Search Results


EfficientnetB7_CNN model architecture.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: EfficientnetB7_CNN model architecture.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

Evaluation of the models using FER13 and FER24_CK + datasets.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Evaluation of the models using FER13 and FER24_CK + datasets.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

EfficientNetB7-CNN (implementation parameters).

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: EfficientNetB7-CNN (implementation parameters).

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

Outlines the EfficientNetB7-CNN performance measure for private testing.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Outlines the EfficientNetB7-CNN performance measure for private testing.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques:

State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

Techniques: Comparison

EfficientnetB7_CNN model architecture.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: EfficientnetB7_CNN model architecture.

Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

Techniques:

Evaluation of the models using FER13 and FER24_CK + datasets.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Evaluation of the models using FER13 and FER24_CK + datasets.

Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

Techniques:

EfficientNetB7-CNN (implementation parameters).

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: EfficientNetB7-CNN (implementation parameters).

Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

Techniques:

Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

Techniques:

Outlines the EfficientNetB7-CNN performance measure for private testing.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: Outlines the EfficientNetB7-CNN performance measure for private testing.

Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

Techniques:

State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

Journal: Heliyon

Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

doi: 10.1016/j.heliyon.2024.e38913

Figure Lengend Snippet: State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

Techniques: Comparison