srmsoumya
commented
1 month ago @patriksabol There isn't a specific meaning assigned to each layer, but typically, earlier layers tend to capture simpler features like lines, edges, and basic shapes, while later layers identify more complex structures like field boundaries and road networks. When working on segmentation-style modeling, it's beneficial to select a balanced mix of both simple and complex features. You might consider using layers like 1, 3, 7, and 9 or 2, 4, 6, and 8. For a deeper understanding, I recommend checking out this paper: https://arxiv.org/pdf/2212.06727.
Hello,
Are there any best practices or guidelines for selecting feature maps from the intermediate layers (out of the 12 available)? Specifically, what do these feature maps represent, and in what scenarios should they be used?
For example, in downstream training with NAIP (RGBN) data, feature maps from layers 3, 5, 7, and 11 were utilized. For RGB data, would layers 3, 5, and 7 be appropriate? Why not layers 2, 6, or 8, for instance?
Cheers.