-
Create a conda virtual environment and activate it.
conda create -n open-mmlab python=3.7 -y conda activate open-mmlab
-
Install PyTorch and torchvision
conda install pytorch torchvision -c pytorch
Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.
E.g
If you have CUDA 10.0 installed under/usr/local/cuda
nvcc --version
conda install pytorch cudatoolkit=10.0 torchvision -c pytorch
-
Install mmcv-full
pip install mmcv-full
-
Install requirements
pip install cython && pip install -r requirements.txt
-
Install mmdetection
python setup.py develop
```
mkdir data
wget https://download.openmmlab.com/mmdetection/data/kitti_tiny.zip -P ./data
unzip -o ./data/kitti_tiny.zip -d ./data/
wget https://s3.eu-central-1.amazonaws.com/avg-kitti/devkit_object.zip -P ./data
unzip -o ./data/devkit_object.zip -d ./data/
```
```
apt-get -q install tree
tree kitti_tiny
```
```
#Values Name Description
----------------------------------------------------------------------------
1 type Describes the type of object: 'Car', 'Van', 'Truck',
'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',
'Misc' or 'DontCare'
1 truncated Float from 0 (non-truncated) to 1 (truncated), where
truncated refers to the object leaving image boundaries
1 occluded Integer (0,1,2,3) indicating occlusion state:
0 = fully visible, 1 = partly occluded
2 = largely occluded, 3 = unknown
1 alpha Observation angle of object, ranging [-pi..pi]
4 bbox 2D bounding box of object in the image (0-based index):
contains left, top, right, bottom pixel coordinates
3 dimensions 3D object dimensions: height, width, length (in meters)
3 location 3D object location x,y,z in camera coordinates (in meters)
1 rotation_y Rotation ry around Y-axis in camera coordinates [-pi..pi]
1 score Only for results: Float, indicating confidence in
detection, needed for p/r curves, higher is better.
```
```
makdir checkpoint
wget -c http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth \
-O checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth
```
```
vim mmedet/dataset/__init__.py
kitti_tiny.py
```
python kitti/kitti_config.py
- method 1
pytbon kitti/kitti_train.py
- method 2
python tools/train.py configs/kitti/cascade_r50_fpn_1x.py
python voc