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README.md
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@ -29,38 +29,38 @@ Please refer to [installation guide](docs/installation.md).
Please refer to [data preparation](dataset/README.md) for preparing the S3DIS and ScanNet v2 dataset.
## Pretrained models
| Dataset | AP | AP_50 | AP_25 | Bbox AP_50 | Bbox AP_25 | Download |
|:----------:|:----:|:-----:|:-----:|:-----:|:-----:|:-------------------------------------------------------------------------------------------:|
| S3DIS | 51.4 | 66.5 | 75.4 | - | - | [model](https://drive.google.com/file/d/1RodfMTUC-0YWs47kx8lj-i0jbDyM9PO6/view?usp=sharing) |
| ScanNet v2 | 46.0 | 67.6 | 78.9 | 59.4 | 71.6 | [model](https://drive.google.com/file/d/1Gt1JUXXB-sBtAeuot29crAUnBwcXW7rN/view?usp=sharing) |
| S3DIS | 51.4 | 66.5 | 75.4 | - | - | [model](https://drive.google.com/file/d/1-f7I6-eIma4OilBON928N6mVcYbhiUFP/view?usp=sharing) |
| ScanNet v2 | 46.0 | 67.6 | 78.9 | 59.4 | 71.6 | [model](https://drive.google.com/file/d/1XUNRfred9QAEUY__VdmSgZxGQ7peG5ms/view?usp=sharing) |
## Training
We use the checkpoint of [HAIS](https://github.com/hustvl/HAIS) as pretrained backbone.
Download the pretrained HAIS model at [here](https://drive.google.com/file/d/1XGNswNrbjm33SwpemYxVEoK4o46EOazd/view) at put it in ``SoftGroup/`` directory.
We use the checkpoint of [HAIS](https://github.com/hustvl/HAIS) as pretrained backbone. **We have already converted the checkpoint to work on ``spconv2.x``**. Download the pretrained HAIS-spconv2 model and put it in ``SoftGroup/`` directory.
Converted hais checkpoint: [model](https://drive.google.com/file/d/1FABsCUnxfO_VlItAzDYAwurdfcdK-scs/view?usp=sharing)
### Training S3DIS dataset
The default configs suppose training on 4 GPU. If you use smaller number of GPUs, you should reduce the learning rate linearly.
First, finetune the pretrained HAIS point-wise prediction network (backbone) on S3DIS.
```
python train.py --config config/softgroup_fold5_backbone_s3dis.yaml
./tools/dist_train.sh config/softgroup_s3dis_backbone_fold5.yaml 4
```
Then, train model from frozen backbone.
```
python train.py --config config/softgroup_fold5_default_s3dis.yaml
./tools/dist_train.sh config/softgroup_s3dis_fold5.yaml 4
```
### Training ScanNet V2 dataset
Training on ScanNet doesnot require finetuning the backbone. Just freeze pretrained backbone and train the model.
```
python train.py --config config/softgroup_default_scannet.yaml
./tools/dist_train.sh --config config/softgroup_scannet.yaml 4
```
## Inference
### Testing for S3DIS dataset.
```
CUDA_VISIBLE_DEVICES=0 python test_s3dis.py --config config/softgroup_fold5_default_s3dis.yaml --pretrain $PATH_TO_PRETRAIN_MODEL$
```
### Testing for ScanNet V2 dataset.
```
CUDA_VISIBLE_DEVICES=0 python test.py --config config/softgroup_default_scannet.yaml --pretrain $PATH_TO_PRETRAIN_MODEL$
./tools/dist_test.sh $CONFIG_FILE $CHECKPOINT $NUM_GPU
```
### Bounding box evaluation of ScanNet V2 dataset.
We provide script to evaluate detection performance on axis-aligned boxes from predicted/ground-truth instance.
@ -77,11 +77,6 @@ python eval_det.py
## Visualization
Please refer to [visualization guide](docs/visualization.md) for visualizing ScanNet and S3DIS results.
## TODO
- [x] Benchmark on spconv 2.x for better speed. (In progress)
- [x] Code refactor (In progress)
- [ ] Distributed training
## Citation
If you find our work helpful for your research. Please consider citing our paper.