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Unverified Commit 56657c21 authored by Sun Jiahao's avatar Sun Jiahao Committed by GitHub
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[Docs] Update readme of lidar segmentation methods (#2559) 

* add readme

* fix spvcnn memory

* add fps and training time

* resolve typo

* fix typo
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configs/cylinder3d/README.md
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......@@ -18,9 +18,10 @@ We implement Cylinder3D and provide the result and checkpoints on Semantickitti
### SemanticKITTI
| Method | Lr schd | Mem (GB) | mIOU | Download |
| :--------: | :-----: | :------: | :------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Cylinder3D | 3x | 10.2 | 63.1±0.5 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/cylinder3d/cylinder3d_4xb4_3x_semantickitti/cylinder3d_4xb4_3x_semantickitti_20230318_191107-822a8c31.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/cylinder3d/cylinder3d_4xb4_3x_semantickitti/cylinder3d_4xb4_3x_semantickitti_20230318_191107.json) |
| Method | Lr schd | Laser-Polar Mix | Mem (GB) | mIoU | Download |
| :-----------------------------------------------------------------: | :-----: | :-------------: | :------: | :------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [Cylinder3D](./cylinder3d_8xb2-laser-polar-mix-3x_semantickitti.py) | 3x | ✗ | 10.2 | 63.1±0.5 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/cylinder3d/cylinder3d_4xb4_3x_semantickitti/cylinder3d_4xb4_3x_semantickitti_20230318_191107-822a8c31.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/cylinder3d/cylinder3d_4xb4_3x_semantickitti/cylinder3d_4xb4_3x_semantickitti_20230318_191107.json) |
| [Cylinder3D](./cylinder3d_8xb2-laser-polar-mix-3x_semantickitti.py) | 3x | ✔ | 12.8 | 67.0 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/cylinder3d/cylinder3d_8xb2-amp-laser-polar-mix-3x_semantickitti_20230425_144950-372cdf69.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/cylinder3d/cylinder3d_8xb2-amp-laser-polar-mix-3x_semantickitti_20230425_144950.log) |
Note: We reproduce the performance comparable with its [official repo](https://github.com/xinge008/Cylinder3D). It's slightly lower than the performance (65.9 mIOU) reported in the paper due to the lack of point-wise refinement and shorter training time.
......
configs/cylinder3d/metafile.yml
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......@@ -15,7 +15,7 @@ Collections:
Version: v1.1.0
Models:
- Name:
- Name: cylinder3d_4xb4-3x_semantickitti
In Collection: Cylinder3D
Config: configs/cylinder3d/cylinder3d_4xb4_3x_semantickitti.py
Metadata:
......@@ -25,5 +25,18 @@ Models:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIOU: 63.1
Weights:
mIoU: 63.1
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/cylinder3d/cylinder3d_4xb4_3x_semantickitti/cylinder3d_4xb4_3x_semantickitti_20230318_191107-822a8c31.pth
- Name: cylinder3d_8xb2-laser-polar-mix-3x_semantickitti
In Collection: Cylinder3D
Config: configs/cylinder3d/cylinder3d_8xb2-laser-polar-mix-3x_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 12.8
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 67.0
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/cylinder3d/cylinder3d_4xb4_3x_semantickitti/cylinder3d_4xb4_3x_semantickitti_20230318_191107-822a8c31.pth
configs/minkunet/README.md
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......@@ -14,22 +14,32 @@ In many robotics and VR/AR applications, 3D-videos are readily-available sources
## Introduction
We implement MinkUNet with [TorchSparse](https://github.com/mit-han-lab/torchsparse) backend and provide the result and checkpoints on SemanticKITTI datasets.
We implement MinkUNet with [TorchSparse](https://github.com/mit-han-lab/torchsparse) / [Minkowski Engine](https://github.com/NVIDIA/MinkowskiEngine) / [Spconv](https://github.com/traveller59/spconv) backend and provide the result and checkpoints on SemanticKITTI datasets.
## Results and models
### SemanticKITTI
| Method | Lr schd | Mem (GB) | mIoU | Download |
| :----------: | :-----: | :------: | :--: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| MinkUNet-W16 | 15e | 3.4 | 60.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737-0d8ec25b.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737.log) |
| MinkUNet-W20 | 15e | 3.7 | 61.6 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718-c3b92e6e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718.log) |
| MinkUNet-W32 | 15e | 4.9 | 63.1 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710-7fa0a6f1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710.log) |
| Method | Backend | Lr schd | Amp | Laser-Polar Mix | Mem (GB) | Training Time (hours) | FPS | mIoU | Download |
| :-------------------------------------------------------------------------------------------: | :--------------: | :-----: | :-: | :-------------: | :------: | :-------------------: | :----: | :--: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [MinkUNet18-W16](./minkunet18_w16_torchsparse_8xb2-amp-15e_semantickitti.py) | torchsparse | 15e | ✔ | ✗ | 3.4 | - | - | 60.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737-0d8ec25b.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737.log) |
| [MinkUNet18-W20](./minkunet18_w20_torchsparse_8xb2-amp-15e_semantickitti.py) | torchsparse | 15e | ✔ | ✗ | 3.7 | - | - | 61.6 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718-c3b92e6e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718.log) |
| [MinkUNet18-W32](./minkunet18_w32_torchsparse_8xb2-amp-15e_semantickitti.py) | torchsparse | 15e | ✔ | ✗ | 4.9 | - | - | 63.1 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710-7fa0a6f1.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710.log) |
| [MinkUNet34-W32](./minkunet34_w32_minkowski_8xb2-laser-polar-mix-3x_semantickitti.py) | minkowski engine | 3x | ✗ | ✔ | 11.5 | 6.5 | 12.2 | 69.2 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_minkowski_8xb2-laser-polar-mix-3x_semantickitti_20230514_202236-839847a8.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_minkowski_8xb2-laser-polar-mix-3x_semantickitti_20230514_202236.log) |
| [MinkUNet34-W32](./minkunet34_w32_spconv_8xb2-amp-laser-polar-mix-3x_semantickitti.py) | spconv | 3x | ✔ | ✔ | 6.7 | 2 | 14.6\* | 68.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_spconv_8xb2-amp-laser-polar-mix-3x_semantickitti_20230512_233152-e0698a0f.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_spconv_8xb2-amp-laser-polar-mix-3x_semantickitti_20230512_233152.log) |
| [MinkUNet34-W32](./minkunet34_w32_spconv_8xb2-laser-polar-mix-3x_semantickitti.py) | spconv | 3x | ✗ | ✔ | 10.5 | 6 | 14.5 | 3 | 69.3 |
| [MinkUNet34-W32](./minkunet34_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti.py) | torchsparse | 3x | ✔ | ✔ | 6.6 | 3 | 12.8 | 69.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti_20230512_233511-bef6cad0.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti_20230512_233511.log) |
| [MinkUNet34-W32](./minkunet34_w32_torchsparse_8xb2-laser-polar-mix-3x_semantickitti.py) | torchsparse | 3x | ✗ | ✔ | 11.8 | 5.5 | 15.9 | 68.7 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_torchsparse_8xb2-laser-polar-mix-3x_semantickitti_20230512_233601-2b61b0ab.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_torchsparse_8xb2-laser-polar-mix-3x_semantickitti_20230512_233601.log) |
| [MinkUNet34v2-W32](minkunet34v2_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti.py) | torchsparse | 3x | ✔ | ✔ | 8.9 | - | - | 70.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34v2_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti_20230510_221853-b14a68b3.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34v2_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti_20230510_221853.log) |
**Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels.
**Note:** Due to TorchSparse backend, the model performance is unstable with TorchSparse backend and may fluctuate by about 1.5 mIoU for different random seeds.
**Note:** Referring to [PCSeg](https://github.com/PJLab-ADG/PCSeg), MinkUNet34v2 is modified based on MinkUNet34.
**Note\*:** Training Time and FPS are measured on NVIDIA A100. The versions of Torchsparse, Minkowski Engine and Spconv are 0.5.4, 1.4.0 and 2.3.6 respectively. Since spconv 2.3.6 has a bug with fp16 on in the inference stage, the actual FPS measurement using fp32.
## Citation
```latex
......
configs/minkunet/metafile.yml
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......@@ -14,9 +14,9 @@ Collections:
Version: v1.1.0
Models:
- Name: minkunet_w16_8xb2-15e_semantickitti
- Name: minkunet18_w16_torchsparse_8xb2-amp-15e_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet_w16_8xb2-15e_semantickitti.py
Config: configs/minkunet/minkunet18_w16_torchsparse_8xb2-amp-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 3.4
......@@ -28,9 +28,9 @@ Models:
mIoU: 60.3
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w16_8xb2-15e_semantickitti/minkunet_w16_8xb2-15e_semantickitti_20230309_160737-0d8ec25b.pth
- Name: minkunet_w20_8xb2-15e_semantickitti
- Name: minkunet18_w20_torchsparse_8xb2-amp-15e_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet_w20_8xb2-15e_semantickitti.py
Config: configs/minkunet/minkunet18_w20_torchsparse_8xb2-amp-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 3.7
......@@ -42,9 +42,9 @@ Models:
mIoU: 61.6
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w20_8xb2-15e_semantickitti/minkunet_w20_8xb2-15e_semantickitti_20230309_160718-c3b92e6e.pth
- Name: minkunet_w32_8xb2-15e_semantickitti
- Name: minkunet18_w32_torchsparse_8xb2-amp-15e_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet_w32_8xb2-15e_semantickitti.py
Config: configs/minkunet/minkunet18_w32_torchsparse_8xb2-amp-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 4.9
......@@ -55,3 +55,87 @@ Models:
Metrics:
mIoU: 63.1
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet_w32_8xb2-15e_semantickitti/minkunet_w32_8xb2-15e_semantickitti_20230309_160710-7fa0a6f1.pth
- Name: minkunet34_w32_minkowski_8xb2-laser-polar-mix-3x_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet34_w32_minkowski_8xb2-laser-polar-mix-3x_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 11.5
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 69.2
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_minkowski_8xb2-laser-polar-mix-3x_semantickitti_20230514_202236-839847a8.pth
- Name: minkunet34_w32_spconv_8xb2-amp-laser-polar-mix-3x_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet34_w32_spconv_8xb2-amp-laser-polar-mix-3x_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 6.7
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 68.3
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_spconv_8xb2-amp-laser-polar-mix-3x_semantickitti_20230512_233152-e0698a0f.pth
- Name: minkunet34_w32_spconv_8xb2-laser-polar-mix-3x_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet34_w32_spconv_8xb2-laser-polar-mix-3x_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 10.5
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 69.3
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_spconv_8xb2-laser-polar-mix-3x_semantickitti_20230512_233817-72b200d8.pth
- Name: minkunet34_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet34_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 6.6
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 69.3
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti_20230512_233511-bef6cad0.pth
- Name: minkunet34_w32_torchsparse_8xb2-laser-polar-mix-3x_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet34_w32_torchsparse_8xb2-laser-polar-mix-3x_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 11.8
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 68.7
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34_w32_torchsparse_8xb2-laser-polar-mix-3x_semantickitti_20230512_233601-2b61b0ab.pth
- Name: minkunet34v2_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti
In Collection: MinkUNet
Config: configs/minkunet/minkunet34v2_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 8.9
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIoU: 70.3
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/minkunet/minkunet34v2_w32_torchsparse_8xb2-amp-laser-polar-mix-3x_semantickitti_20230510_221853-b14a68b3.pth
configs/spvcnn/README.md
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......@@ -20,11 +20,12 @@ We implement SPVCNN with [TorchSparse](https://github.com/mit-han-lab/torchspars
### SemanticKITTI
| Method | Lr schd | Mem (GB) | mIoU | Download |
| :--------: | :-----: | :------: | :--: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| SPVCNN-W16 | 15e | 3.9 | 61.8 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645.log) |
| SPVCNN-W20 | 15e | 4.2 | 62.6 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649.log) |
| SPVCNN-W32 | 15e | 5.4 | 64.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/pvcnn_w32_8xb2-15e_semantickitti/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324.log) |
| Method | Lr schd | Laser-Polar Mix | Mem (GB) | mIoU | Download |
| :---------------------------------------------------------------------: | :-----: | :-------------: | :------: | :--: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [SPVCNN-W16](./spvcnn_w16_8xb2-amp-15e_semantickitti.py) | 15e | ✗ | 3.9 | 61.8 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645.log) |
| [SPVCNN-W20](./spvcnn_w20_8xb2-amp-15e_semantickitti.py) | 15e | ✗ | 4.2 | 62.6 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649.log) |
| [SPVCNN-W32](./spvcnn_w32_8xb2-amp-15e_semantickitti.py) | 15e | ✗ | 5.4 | 64.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324.log) |
| [SPVCNN-W32](./spvcnn_w32_8xb2-amp-laser-polar-mix-3x_semantickitti.py) | 3x | ✔ | 7.2 | 68.7 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-amp-laser-polar-mix-3x_semantickitti_20230425_125908-d68a68b7.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-amp-laser-polar-mix-3x_semantickitti_20230425_125908.log) |
**Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels.
......
configs/spvcnn/metafile.yml
View file @ 56657c21
......@@ -14,9 +14,9 @@ Collections:
Version: v1.1.0
Models:
- Name: spvcnn_w16_8xb2-15e_semantickitti
- Name: spvcnn_w16_8xb2-amp-15e_semantickitti
In Collection: SPVCNN
Config: configs/spvcnn/spvcnn_w16_8xb2-15e_semantickitti.py
Config: configs/spvcnn/spvcnn_w16_8xb2-amp-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 3.9
......@@ -28,9 +28,9 @@ Models:
mIOU: 61.7
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth
- Name: spvcnn_w20_8xb2-15e_semantickitti
- Name: spvcnn_w20_8xb2-amp-15e_semantickitti
In Collection: SPVCNN
Config: configs/spvcnn/spvcnn_w20_8xb2-15e_semantickitti.py
Config: configs/spvcnn/spvcnn_w20_8xb2-amp-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 4.2
......@@ -42,9 +42,9 @@ Models:
mIOU: 62.9
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth
- Name: spvcnn_w32_8xb2-15e_semantickitti
- Name: spvcnn_w32_8xb2-amp-15e_semantickitti
In Collection: SPVCNN
Config: configs/spvcnn/spvcnn_w32_8xb2-15e_semantickitti.py
Config: configs/spvcnn/spvcnn_w32_8xb2-amp-15e_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 5.4
......@@ -55,3 +55,17 @@ Models:
Metrics:
mIOU: 64.3
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth
- Name: spvcnn_w32_8xb2-amp-laser-polar-mix-3x_semantickitti
In Collection: SPVCNN
Config: configs/spvcnn/spvcnn_w32_8xb2-amp-laser-polar-mix-3x_semantickitti.py
Metadata:
Training Data: SemanticKITTI
Training Memory (GB): 7.2
Training Resources: 8x A100 GPUs
Results:
- Task: 3D Semantic Segmentation
Dataset: SemanticKITTI
Metrics:
mIOU: 64.3
Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-amp-laser-polar-mix-3x_semantickitti_20230425_125908-d68a68b7.pth
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