/mmdetection-demo

pascal voc object detect

Primary LanguagePythonApache License 2.0Apache-2.0

mmdetection-demo

install and compile

  1. Create a conda virtual environment and activate it.

    conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

  2. 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

  3. Install mmcv-full

    pip install mmcv-full

  4. Install requirements

    pip install cython && pip install -r requirements.txt

  5. Install mmdetection

    python setup.py develop

Dataset

download dataset

```
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/
```

check dataset

```
apt-get -q install tree
tree kitti_tiny

```

dataset description

```
#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.
```

Train

download checkpoint

```
 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

```

Register Dataset

```
vim mmedet/dataset/__init__.py

kitti_tiny.py

```

Config

python kitti/kitti_config.py

training

  • method 1
pytbon kitti/kitti_train.py
  • method 2
python tools/train.py  configs/kitti/cascade_r50_fpn_1x.py

Test

python voc

TODO

Reference