We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| zhangxiaosong18/FreeAnchor | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| focal_loss | Ran | zhangxiaosong18/FreeAnchor/maskrcnn_benchmark/modeling/rpn/free_anchor_loss.py code served (permissive licence) · get_code("b4392a86bb773ddd") |
| positive_bag_loss | Ran | zhangxiaosong18/FreeAnchor/maskrcnn_benchmark/modeling/rpn/free_anchor_loss.py code served (permissive licence) · get_code("ccf87e6c1da6f696") |
| smooth_l1_loss | Ran | zhangxiaosong18/FreeAnchor/maskrcnn_benchmark/modeling/rpn/free_anchor_loss.py code served (permissive licence) · get_code("13099e0dd83850a8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner. Our approach, referred to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization. FreeAnchor is implemented by optimizing detection customized likelihood and can be fused with CNN-based detectors in a plug-and-play manner. Experiments on COCO demonstrate that FreeAnchor consistently outperforms their counterparts with significant margins.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1909.02466")
get_code_for_paper("1909.02466")
have("1909.02466")
Connect an agent — have() is free.