Papers › Fully Convolutional Networks for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer, Jonathan Long, Trevor Darrell
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.
Code
37 repositories listed; official and paper-mentioned ones first.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Segmentation | SUN-RGBD | FCN | Mean IoU | 27.39 | #5 of 5 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | FCN | Mean IoU (class) | 65.3% | #95 of 105 | Archive leaderboard | report |
| Semantic Segmentation | NYU Depth v2 | FCN-32s RGB-HHA | Mean Accuracy | 44 | #121 of 121 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2011 test | FCN-VGG16 | Mean IoU | 32 | #2 of 3 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2011 test | FCN-pool4 | Mean IoU | 22.4 | #3 of 3 | Archive leaderboard | report |
| Video Semantic Segmentation | Cityscapes val | FCN-50 [14] | mIoU | 70.1 | #8 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: FCN, Transposed convolution
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