metadata
library_name: pytorch
YOLOP is an efficient multi-task network for panoptic driving perception that jointly performs car detection, drivable area segmentation, and lane line segmentation with a shared encoder and three task-specific decoders, delivering strong accuracy–efficiency trade-offs for real-time onboard vision.
Original paper: YOLOP: You Only Look Once for Panoptic Driving Perception
YOLOP
This model is the end-to-end YOLOP checkpoint trained for joint detection and segmentation on BDD100K. It is well suited for on-device panoptic driving perception where low latency and power efficiency are critical.
Model Configuration:
- Reference implementation: YOLOP
- Original Weight: End-to-end.pth
- Resolution: 3x384x640
- Support Cooper version:
- Cooper SDK: [2.5.4]
- Cooper Foundry: [2.3]
| Model | Device | Compression | Model Link |
|---|---|---|---|
| YOLOP | N1-655 | Amba_optimized | Model_Link |
| YOLOP | N1-655 | Activation_fp16 | Model_Link |
| YOLOP | X7 | Amba_optimized | Model_Link |
| YOLOP | X7 | Activation_fp16 | Model_Link |
| YOLOP | CV7 | Amba_optimized | Model_Link |
| YOLOP | CV7 | Activation_fp16 | Model_Link |
| YOLOP | CV72 | Amba_optimized | Model_Link |
| YOLOP | CV72 | Activation_fp16 | Model_Link |
| YOLOP | CV75 | Amba_optimized | Model_Link |
| YOLOP | CV75 | Activation_fp16 | Model_Link |