We lifted 24 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| kskim-phd/mfcn | pwc_unofficial | 1 of 6 |
| ankit-vaghela30/Cilia-Segmentation | pwc_unofficial | 0 of 9 |
| copy not recorded | — | 0 of 3 |
| petko-nikolov/pysemseg | reimplementation | 0 of 3 |
| 0bserver07/One-Hundred-Layers-Tiramisu | pwc_unofficial | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| center_crop | Ran | kskim-phd/mfcn/MFCN_SMC/code_MFCN/layers.py code served (permissive licence) · get_code("9d5555c9ff725b7f") |
| FCDenseNet103 | Not yet run | this paper's copy was not recorded; identical code first harvested from bfortuner/pytorch_tiramisu pointer only · get_code("a31003fad13ae67c") |
| FCDenseNet57 | Not yet run | this paper's copy was not recorded; identical code first harvested from bfortuner/pytorch_tiramisu pointer only · get_code("f1271d09e9ba1ad7") |
| FCDenseNet67 | Not yet run | this paper's copy was not recorded; identical code first harvested from bfortuner/pytorch_tiramisu pointer only · get_code("cfa9b6051f647903") |
| blur | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/preprocessing.py code served (permissive licence) · get_code("606293610ee73f36") |
| bn | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/tiramisu_model.py code served (permissive licence) · get_code("011e9d1f4a18f0c1") |
| center_crop | Not yet run | kskim-phd/mfcn/MFCN_SMC/code_MFCN/mydataset.py code served (permissive licence) · get_code("2b32f51f5f7b51cc") |
| center_crop_mask | Not yet run | kskim-phd/mfcn/MFCN_SMC/code_MFCN/mydataset.py code served (permissive licence) · get_code("799d1ba8540920a4") |
| dropout | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/tiramisu_model.py code served (permissive licence) · get_code("f60d7b457f163df3") |
| fcdensenet103 | Not yet run | petko-nikolov/pysemseg/pysemseg/models/densenet.py pointer only (licence: GPL-3.0) · get_code("a8205d6e6712fbf5") |
| fcdensenet56 | Not yet run | petko-nikolov/pysemseg/pysemseg/models/densenet.py pointer only (licence: GPL-3.0) · get_code("fc05fafe6352aa0c") |
| fcdensenet67 | Not yet run | petko-nikolov/pysemseg/pysemseg/models/densenet.py pointer only (licence: GPL-3.0) · get_code("71b9ef5924a9d9be") |
| get_JI | Not yet run | kskim-phd/mfcn/MFCN_SMC/code_MFCN/stage1_conventional_model.py code served (permissive licence) · get_code("f19ea4f954b9e8d0") |
| get_JI | Not yet run | kskim-phd/mfcn/MFCN_SMC/code_MFCN/stage2_patch_wise.py code served (permissive licence) · get_code("36a022450e439192") |
| gray2rgb | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/preprocessing.py code served (permissive licence) · get_code("229fb6fceb2b565a") |
| load_all_video | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/io_support.py code served (permissive licence) · get_code("ba25286a61fa1d4f") |
| load_data | Not yet run | 0bserver07/One-Hundred-Layers-Tiramisu/camvid_data_loader.py code served (permissive licence) · get_code("ee76790f2e23db5a") |
| load_img | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/io_support.py code served (permissive licence) · get_code("71d2f0c11ca47487") |
| load_video | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/io_support.py code served (permissive licence) · get_code("6b7485606e8ec4b4") |
| normalized | Not yet run | 0bserver07/One-Hundred-Layers-Tiramisu/helper.py code served (permissive licence) · get_code("600dd344d089e843") |
| one_hot_it | Not yet run | 0bserver07/One-Hundred-Layers-Tiramisu/helper.py code served (permissive licence) · get_code("8eb260280ed17f0f") |
| post_processing | Not yet run | kskim-phd/mfcn/MFCN_SMC/code_MFCN/stage1_conventional_model.py code served (permissive licence) · get_code("65d0d9510aa373d4") |
| relu | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/tiramisu_model.py code served (permissive licence) · get_code("a5b43c4ed0ec9709") |
| threshold | Not yet run | ankit-vaghela30/Cilia-Segmentation/hastings/preprocessing.py code served (permissive licence) · get_code("cc07c5438fa01645") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions. Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. Code to reproduce the experiments is available here : https://github.com/SimJeg/FC-DenseNet/blob/master/train.py
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1611.09326")
get_code_for_paper("1611.09326")
have("1611.09326")
Connect an agent — have() is free.