Table 3 Architecture details of ResNet-34 original and modified.
From: A robust deep learning approach for tomato plant leaf disease localization and classification
Layer name | Original | Modified |
|---|---|---|
Conv1 | 7 × 7, 64, 3 × 3 max pool | \(\left[3\times 3, 64\right]\)× 3, 7 × 7 Attention |
Conv2_x | \(\left[\begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array}\right]\times\,3\) | \(\left[\begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array}\right]\times\,3\) |
Conv3_x | \(\left[\begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array}\right]\times\,4\) | \(\left[\begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array}\right]\times\,4\) |
Conv4_x | \(\left[\begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array}\right]\times\,6\) | \(\left[\begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array}\right]\times\,6\) |
Conv5_x | \(\left[\begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array}\right]\times\,3\) | \(\left[\begin{array}{c}3\times 3, 64\\ 3\times 3, 64\end{array}\right]\times\,3\) |
