Methods › Computer Vision › Convolutional Neural Networks › SPP-Net
SPP-Net
Introduced by Kaiming He et al. in Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
SPP-Net is a convolutional neural architecture that employs spatial pyramid pooling to remove the fixed-size constraint of the network. Specifically, we add an SPP layer on top of the last convolutional layer. The SPP layer pools the features and generates fixed-length outputs, which are then fed into the fully-connected layers (or other classifiers). In other words, we perform some information aggregation at a deeper stage of the network hierarchy (between convolutional layers and fully-connected layers) to avoid the need for cropping or warping at the beginning.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition 18 Jun 2014 · 14 repositories · arXiv:1406.4729Syntology ran 0 of 1 samples · 1 unverified
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| General Classification | 1 |
| Image Classification | 1 |
| Object Detection | 1 |
| Object Recognition | 1 |
| image-classification | 1 |
| object-detection | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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