We lifted 23 functions out of this paper's own repositories and ran 19 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 |
|---|---|---|
| 0jason000/S-GhostNet | pwc_unofficial | 7 of 7 |
| ozora-ogino/efficient_backbones | pwc_unofficial | 5 of 7 |
| 0jason000/ghostnet | pwc_unofficial | 2 of 3 |
| yangyucheng000/ghostnet | pwc_unofficial | 2 of 2 |
| copy not recorded | — | 2 of 2 |
| KopiSoftware/Ghost_ResNet56 | reimplementation | 1 of 1 |
| iamhankai/ghostnet.pytorch | reimplementation | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| conv1x1 | Ran | ozora-ogino/efficient_backbones/efficient_backbones/g_ghost_regnet.py code served (permissive licence) · get_code("d9def42110729a85") |
| conv1x1 | Ran | ozora-ogino/efficient_backbones/efficient_backbones/resnet.py code served (permissive licence) · get_code("6f0d1921e77f5311") |
| conv3x3 | Ran | ozora-ogino/efficient_backbones/efficient_backbones/g_ghost_regnet.py code served (permissive licence) · get_code("160bb14bd76201b4") |
| conv3x3 | Ran | ozora-ogino/efficient_backbones/efficient_backbones/resnet.py code served (permissive licence) · get_code("7e71d6f8569b6b34") |
| cutout | Ran | KopiSoftware/Ghost_ResNet56/train_Ghost_ResNet56.py pointer only (licence: GPL-3.0) · get_code("ce3aa75b714c404e") |
| float_parameter | Ran | 0jason000/S-GhostNet/src/autoaug.py code served (permissive licence) · get_code("40d3c39a403ae1f7") |
| gen_cfgs_1x | Ran | 0jason000/S-GhostNet/src/big_net.py code served (permissive licence) · get_code("7022c26a18d085ec") |
| gen_cfgs_large | Ran | 0jason000/S-GhostNet/src/big_net.py code served (permissive licence) · get_code("c83dff446ca0238d") |
| get_lr | Ran | 0jason000/S-GhostNet/src/utils.py code served (permissive licence) · get_code("9dfb4495e7fb2efb") |
| get_lr | Ran | yangyucheng000/ghostnet/src/lr_generator.py code served (permissive licence) · get_code("78ea6999b2c97419") |
| get_lr_tinynet_c | Ran | 0jason000/S-GhostNet/src/utils.py code served (permissive licence) · get_code("e1258604a87c9da4") |
| get_top5_acc | Ran | yangyucheng000/ghostnet/postprocess.py code served (permissive licence) · get_code("fca5ffc375803fa4") |
| hard_sigmoid | Ran | ozora-ogino/efficient_backbones/efficient_backbones/ghostnet.py code served (permissive licence) · get_code("ca43d9eb44b430e5") |
| init_group_params | Ran | 0jason000/ghostnet/src/optim.py code served (permissive licence) · get_code("698131638ff8d4ad") |
| int_parameter | Ran | 0jason000/S-GhostNet/src/autoaug.py code served (permissive licence) · get_code("e5a370ee6856bd8d") |
| is_model | Ran | 0jason000/ghostnet/src/model.py code served (permissive licence) · get_code("b85a6569fd4f3890") |
| round_filters | Ran | this paper's copy was not recorded; identical code first harvested from seermer/TensorFlow2-EfficientNetV2 pointer only · get_code("e60387e9529f63ff") |
| round_repeats | Ran | this paper's copy was not recorded; identical code first harvested from seermer/TensorFlow2-EfficientNetV2 pointer only · get_code("1a9d7e65e0a9fbc9") |
| str2bool | Ran | 0jason000/S-GhostNet/src/utils.py code served (permissive licence) · get_code("2dedef59d5e447a2") |
| conv_1x1_bn | Not yet run | ozora-ogino/efficient_backbones/efficient_backbones/efficientnet_v2.py code served (permissive licence) · get_code("f76badd0c6766f8a") |
| conv_3x3_bn | Not yet run | ozora-ogino/efficient_backbones/efficient_backbones/efficientnet_v2.py code served (permissive licence) · get_code("3d6f0eda22ace06f") |
| depthwise_conv | Not yet run | iamhankai/ghostnet.pytorch/ghost_net.py pointer only (licence: NONE) · get_code("680d03183fe725d4") |
| model_entrypoint | Not yet run | 0jason000/ghostnet/src/model.py code served (permissive licence) · get_code("ac95eb88d9b993fd") |
More are linked than shown here.
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. $75.7\%$ top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1911.11907")
get_code_for_paper("1911.11907")
have("1911.11907")
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