This is a segmentation network to classify each pixel into 20 classes:
- road
- sidewalk
- building
- wall
- fence
- pole
- traffic light
- traffic sign
- vegetation
- terrain
- sky
- person
- rider
- car
- truck
- bus
- train
- motorcycle
- bicycle
- ego-vehicle
| Metric | Value |
|---|---|
| Image size | 2048x1024 |
| GFlops | 58.572 |
| MParams | 6.686 |
| Source framework | PyTorch* |
The quality metrics calculated on 2000 images:
| Label | IOU |
|---|---|
| mean | 0.6907 |
| Road | 0.910379 |
| Sidewalk | 0.630676 |
| Building | 0.860139 |
| Wall | 0.424166 |
| Fence | 0.592632 |
| Pole | 0.559078 |
| Traffic Light | 0.654779 |
| Traffic Sign | 0.648217 |
| Vegetation | 0.882593 |
| Terrain | 0.620521 |
| Sky | 0.976889 |
| Person | 0.711653 |
| Rider | 0.612787 |
| Car | 0.877892 |
| Truck | 0.674829 |
| Bus | 0.743752 |
| Train | 0.358641 |
| Motorcycle | 0.600701 |
| Bicycle | 0.622246 |
| Ego-Vehicle | 0.852932 |
IOU=TP/(TP+FN+FP), where:TP- number of true positive pixels for given classFN- number of false negative pixels for given classFP- number of false positive pixels for given class
Link to performance table
The blob with BGR image in format: [B, C=3, H=1024, W=2048], where:
- B - batch size,
- C - number of channels
- H - image height
- W - image width
- The net outputs a blob with the shape [B, H=1024, W=2048]. It can be treated as a one-channel feature map, where each pixel is a label of one of the classes.
[*] Other names and brands may be claimed as the property of others.