2026· International journal of research and scientific innovation· 0 citations
TL;DR
This study compares a custom CNN with three pre-trained models on the CIFAR-10 dataset, which comprises 60,000 colour images (32×32 pixels) across 10 categories, and shows that pre-trained models performed much better than the customised model.
Abstract
Convolutional Neural Networks (CNNs) have significantly changed image classification over the years by allowing computers to learn features directly from raw pixel data. However, deciding between building a customised model and using a pre-trained one can be a difficult task, especially when working with small datasets. In this study, we compare a custom CNN with three pre-trained models—VGG16, ResNet50, and MobileNetV2—on the CIFAR-10 dataset, which comprises 60,000 colour images (32×32 pixels) across 10 categories. We measured model performance using accuracy, precision, recall, F1-score, and training time. The results show that pre-trained models performed much better than the customised model. ResNet50 had the highest accuracy at 92.4%. However, MobileNetV2 gave the best mix of speed (1,800 seconds to train) and accuracy (90.2%). The custom CNN reached 82.3% accuracy, used less memory, and did not need image resizing. These results offer clear benchmarks for choosing models in the face of limited resources. They also demonstrate that transfer learning can achieve strong performance, while showing that custom CNNs remain useful for learning and simple tasks.
The results demonstrate that transfer learning significantly improves convergence speed, generalization, and computational efficiency, making it a promising approach for AI applications across domains such as healthcare, NLP, and autonomous systems.
Abdul Sttar Ismail Wdaa, Iraq Ali Hussein, A. Ahmed· Future Technology· 0 citations
Deeper modern networks outperform the older AlexNet by a wide margin on CIFAR-10, and even a relatively compact ResNet can nearly match the accuracy of a much larger VGG16 in far less time.
A comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures is presented, showing that deeper networks provide better representation, while residual connections and compound scaling improve training stability and efficiency.
Riyaz Mohammed· International Journal of App...· 0 citations
In this paper, we perform a systematic empirical study of deep learning techniques on the CIFAR-10 image classification benchmark. We study three inter-related aspects of neural network design: (1) the relative impact of nine gradient descent optimisation algorithms on a baseline Multi-Layer Perceptron (MLP); (2) the effectiveness of six regularisation techniques namely; L2 weight decay, Dropout, Data Augmentation, Gaussian Noise Injection, Early Stopping, and Ensemble Learning; on model generalisation; and (3) a benchmark comparison of five landmark Convolutional Neural Network (CNN) architectures alongside Inception V3 trained from scratch. We also investigate autoencoder-based image reconstruction and a side-by-side comparison of linear Principal Component Analysis (PCA) with non-linear autoencoder representations. Nesterov Accelerated Gradient achieves the lowest training loss (1.5814) in short-run experiments. VGGNet achieves the highest test accuracy (75.09%) among CNN architectures trained from scratch for five epochs. Early Stopping yields the best regularisation outcome, and non-linear autoencoders outperform PCA in reconstruction quality. All experiments use TensorFlow 2.x and Keras on CIFAR-10.
Akber Hussain, M. Sajid, Abdul Raheem et al.· 2026 4th International Confe...· 0 citations
The results show that larger models and larger pretraining datasets do not automatically lead to better downstream performance, and transfer effectiveness in medical imaging is driven primarily by architectural inductive biases, pretraining strategy, and domain relevance.
Dina A. Elkholy, Mohamed S. Shehata, John W. Braun· Journal of imaging informati...· 0 citations
The results indicate the successful implementation of the proposed AL-CNN model for reliable and accurate image classification, with results reported on a large-scale benchmark dataset that is widely accepted for performance evaluation.
M. Chawla, Rashmi Agrawal, Bharat Bhushan· Bulletin of Electrical Engin...· 0 citations
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