Jointly human semantic parsing and attribute recognition with feature pyramid structure in EfficientNets

Moghaddam, Mahnaz; Charmi, Mostafa; Hassanpoor, Hossein

doi:10.1049/ipr2.12195

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…EfficientNet architecture is designed by Tan and Le [16] as a demonstration of their proposed compound scaling method. Scaling CNN model is usually done by increasing depth, width, and resolution which could become tedious and computationally expensive [18]. However, the proposed compound scaling method by Tan and Le [16] uses a compound coefficient to balance the network depth, width, and resolution so that a baseline CNN can be effectively scaled up.…”

Section: Modelingmentioning

confidence: 99%

“…By using the compound scaling method, baseline EfficientNet-B0 is scaled up to obtain EfficientNet-B1 to EfficientNet-B7. All of them are able to perform well with relatively smaller and faster networks [16], [18]. All EfficientNet models achieved high accuracy and efficiency on several widely used datasets, such as ImageNet, CIFAR-100, and Flowers [16].…”

Section: Modelingmentioning

confidence: 99%

“…The EfficientNet architecture itself also has many types starting from EfficientNet-B0 to EfficientNet-B7. EfficientNet architecture has been proven capable of producing better performance compared with several other architectures [16]- [18]. There are several previous studies related to skin cancer classification.…”

Section: Introductionmentioning

confidence: 99%

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Skin cancer classification using EfficientNet architecture

Harahap,

Husein,

Kwok

et al. 2024

Bulletin EEI

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Skin cancer is one of the most common deadly diseases worldwide. Hence, skin cancer classification is becoming increasingly important because treatment in the early stages of skin cancer is much more effective and efficient. This study focuses on the classification of three common types of skin cancer, namely basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma using EfficientNet architecture. The dataset is preprocessed and each image in the dataset is resized to 256×256 pixels prior to incorporation in later stages. We then train all types of EfficientNet starting from EfficientNet-B0 to EfficientNet-B7 and compare their performances. Based on the test results, all trained EfficientNet models are capable of producing good accuracy, precision, recall, and F1-score in skin cancer classification. Particularly, our designed EfficientNet-B4 model achieves 79.69% accuracy, 81.67% precision, 76.56% recall, and 79.03% F1-score as the highest among others. These results confirm that EfficientNet architecture can be utilized to classify skin cancer properly.

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Section: Modelingmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

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Skin cancer classification using EfficientNet architecture

Harahap,

Husein,

Kwok

et al. 2024

Bulletin EEI

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“…This model, which uses the EffiCientNet model as the backbone network, has a good performance in image classification by scaling the network width, depth, and input resolution together. As the number of parameters of the model is optimized, it is also advantageous in terms of using system resources [ 60 – 62 ].…”

Section: Real Environment Application With Hsp-dqnmentioning

confidence: 99%

Mobile Robot Application with Hierarchical Start Position DQN

Erkan

Arseri̇m

2022

Computational Intelligence and Neuroscience

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Advances in deep learning significantly affect reinforcement learning, which results in the emergence of Deep RL (DRL). DRL does not need a data set and has the potential beyond the performance of human experts, resulting in significant developments in the field of artificial intelligence. However, because a DRL agent has to interact with the environment a lot while it is trained, it is difficult to be trained directly in the real environment due to the long training time, high cost, and possible material damage. Therefore, most or all of the training of DRL agents for real-world applications is conducted in virtual environments. This study focused on the difficulty in a mobile robot to reach its target by making a path plan in a real-world environment. The Minimalistic Gridworld virtual environment has been used for training the DRL agent, and to our knowledge, we have implemented the first real-world implementation for this environment. A DRL algorithm with higher performance than the classical Deep Q-network algorithm was created with the expanded environment. A mobile robot was designed for use in a real-world application. To match the virtual environment with the real environment, algorithms that can detect the position of the mobile robot and the target, as well as the rotation of the mobile robot, were created. As a result, a DRL-based mobile robot was developed that uses only the top view of the environment and can reach its target regardless of its initial position and rotation.

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“…Human parsing has attracted considerable attention in recent years, because it is the core technology that supports many research studies and applications in the fields of retailing [1][2][3], social science [4,5], medicine [6,7], and even security [8,9]. The aim of human parsing is to segment the pixels of an input image into regions according to different labels of body parts and clothes.…”

Section: Introductionmentioning

confidence: 99%

Enhancing human parsing with region‐level learning

Zhou

Mok

2023

IET Computer Vision

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Human parsing is very important in a diverse range of industrial applications. Despite the considerable progress that has been achieved, the performance of existing methods is still less than satisfactory, since these methods learn the shared features of various parsing labels at the image level. This limits the representativeness of the learnt features, especially when the distribution of parsing labels is imbalanced or the scale of different labels is substantially different. To address this limitation, a Region‐level Parsing Refiner (RPR) is proposed to enhance parsing performance by the introduction of region‐level parsing learning. Region‐level parsing focuses specifically on small regions of the body, for example, the head. The proposed RPR is an adaptive module that can be integrated with different existing human parsing models to improve their performance. Extensive experiments are conducted on two benchmark datasets, and the results demonstrated the effectiveness of our RPR model in terms of improving the overall parsing performance as well as parsing rare labels. This method was successfully applied to a commercial application for the extraction of human body measurements and has been used in various online shopping platforms for clothing size recommendations. The code and dataset are released at this link https://github.com/applezhouyp/PRP.

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Jointly human semantic parsing and attribute recognition with feature pyramid structure in EfficientNets

Cited by 9 publications

References 33 publications

Skin cancer classification using EfficientNet architecture

Skin cancer classification using EfficientNet architecture

Mobile Robot Application with Hierarchical Start Position DQN

Enhancing human parsing with region‐level learning

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