विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| EfficientNet× | इन्सेप्शन नेटवर्क (गूगलनेट)× | |
|---|---|---|
| क्षेत्र | गहन अधिगम | गहन अधिगम |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2019 | 2015 |
| प्रवर्तक≠ | Tan, M. & Le, Q. V. | Christian Szegedy et al. (Google) |
| प्रकार≠ | Compound-scaled convolutional neural network architecture | Deep CNN with parallel multi-scale convolutions |
| मौलिक स्रोत≠ | Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114. link ↗ | Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. DOI ↗ |
| उपनाम | EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2 | GoogLeNet, Inception v1, Deep Convolutional Neural Network (Google), Başlangıç Ağı |
| संबंधित≠ | 4 | 2 |
| सारांश≠ | EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substantially fewer parameters and FLOPs than prior networks such as ResNet and Inception. | The Inception Network, introduced by Szegedy et al. at Google in 2015 and submitted to CVPR under the name GoogLeNet, is a 22-layer deep convolutional neural network designed for large-scale image recognition. Its defining contribution is the Inception module, which applies convolutions of multiple kernel sizes in parallel and concatenates their outputs, enabling the network to capture spatial features at different scales simultaneously without a proportional increase in computational cost. |
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