Benchmark analysis of popular ImageNet classification deep CNN architectures

Muhammed, Mustafa Alghali Elsaid; Ahmed, Ahmed Abdalazeem; Khalid, Tarig Ahmed

doi:10.1109/smarttechcon.2017.8358502

Cited by 21 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As expected, our study verified the feasibility and effectiveness of using DCNNs to automatically identify habitat elements, and the best accuracy rate reached 97.76%. Although we only used ten habitat elements as the objects of recognition in our research, DCNNs have also achieved good results in terms of the recognition of 1000 categories in the ImageNet recognition task [ 65 ], so we have reason to believe that if more categories of images are provided, our method can also identify more habitat elements.…”

Section: Discussionmentioning

confidence: 99%

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Wang

Lin

Yan

et al. 2021

Animals

View full text Add to dashboard Cite

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

show abstract

Section: Discussionmentioning

confidence: 99%

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Wang

Lin

Yan

et al. 2021

Animals

View full text Add to dashboard Cite

show abstract

“…As shown in the figure, our base model pre-trained with the ImageNet data consists of 1000 different classes, with over 14 million images [43]. With this matter, fine-tuning is imperative, as the current weights and structure of the EfficientNetB0 cannot immediately work for our selected task [44].…”

Section: Transfer Learning and Fine-tuningmentioning

confidence: 99%

Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears

2021

KSII TIIS

View full text Add to dashboard Cite

In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and retrained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradientweighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

show abstract

“…(15): lr = base_lr × γ floor (iter /stepsize) (15) where, lr represents the current learning rate, γ the learning rate update weight, iter the current iteration number, stepsize the set learning rate update step size, and floor (•) the round-down operation. In this paper, the momentum value and weight decay value are set to 0.9 and 0.0005, respectively [31].…”

Section: B Model Evaluation Metricsmentioning

confidence: 99%

Bucket Teeth Detection Based on Faster Region Convolutional Neural Network

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The electric shovel is a bucket-equipped mining excavator widely used in open-pit mining today. The prolonged direct impact of the bucket teeth with hard and abrasive materials such as ore during the process of the mining excavation can cause the bucket teeth to break and fall off prematurely, resulting in unplanned downtime and productivity losses. In response to this problem, we have developed a visionbased bucket teeth fault detection algorithm with deep learning. Using a dataset based on the images of both real shovel teeth and 3D-printed models, we trained a Faster Region Convolutional Neural Network (Faster R-CNN) to obtain the number of normal bucket teeth and the positions of the bucket teeth from the images, using the additional bucket dataset from 3D-printed models to pre-train the network for improving its detection accuracy on the real bucket data. We compared the resulting Faster R-CNN model with the ZFNet, the ResNet-50, and the VGG16 and found our Faster R-CNN model to perform best in terms of accuracy and speed.

show abstract

Benchmark analysis of popular ImageNet classification deep CNN architectures

Cited by 21 publications

References 0 publications

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Empirical Analysis of a Fine-Tuned Deep Convolutional Model in Classifying and Detecting Malaria Parasites from Blood Smears

Bucket Teeth Detection Based on Faster Region Convolutional Neural Network

Contact Info

Product

Resources

About