We lifted 4 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Megvii-BaseDetection/YOLOX | canonical | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| create_yolox_model | Not yet run | Megvii-BaseDetection/YOLOX/yolox/models/build.py code served (permissive licence) · get_code("7111f135eb8bfff5") |
| get_activation | Not yet run | Megvii-BaseDetection/YOLOX/yolox/models/network_blocks.py code served (permissive licence) · get_code("6e3d5f4c3ce305d7") |
| yolox_nano | Not yet run | Megvii-BaseDetection/YOLOX/yolox/models/build.py code served (permissive licence) · get_code("706978d51729ca8d") |
| yolox_tiny | Not yet run | Megvii-BaseDetection/YOLOX/yolox/models/build.py code served (permissive licence) · get_code("af7e14a4cec878b8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YOLOX. On the Argoverse-HD dataset, our system achieves 41.0 streaming AP, which surpassed second place by 7.8/6.1 on detection-only track/fully track, respectively. Moreover, equipped with TensorRT, our model achieves the 30FPS inference speed with a high-resolution input size (e.g., 1440-2304). Code and models will be available at https://github.com/Megvii-BaseDetection/YOLOX
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2108.04230")
get_code_for_paper("2108.04230")
have("2108.04230")
Connect an agent — have() is free.