Hang Su, Jun Zhu, Xiao Yang, Lei Zhang, Hao Zhang, Shilong Liu, Feng Li, Xianbiao Qi
We lifted 11 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| IDEA-Research/DAB-DETR | canonical | 1 of 1 |
| helq2612/biadt | — | 5 of 8 |
| idea-research/dn-detr | — | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| GradientReversal | Ran | helq2612/biadt/models/dn_dab_deformable_detr/dab_deformable_detr.py code served (permissive licence) · get_code("764f03e6f6c10909") |
| GradientReversalFunction | Ran | helq2612/biadt/models/dn_dab_deformable_detr/dab_deformable_detr.py code served (permissive licence) · get_code("6e1a6cc4327acf17") |
| Mutual_loss | Ran | helq2612/biadt/models/dn_dab_deformable_detr/dab_deformable_detr.py code served (permissive licence) · get_code("79c4cf758452c514") |
| NestedTensor | Ran | helq2612/biadt/models/dn_dab_deformable_detr/dab_deformable_detr.py code served (permissive licence) · get_code("d336c6095b11629a") |
| nested_tensor_from_tensor_list | Ran | helq2612/biadt/models/dn_dab_deformable_detr/dab_deformable_detr.py code served (permissive licence) · get_code("6442da4f34dd152c") |
| sigmoid_focal_loss | Ran | IDEA-Research/DAB-DETR/models/DAB_DETR/DABDETR.py code served (permissive licence) · get_code("5c0711aada67957e") |
| DABDETR | Not yet run | idea-research/dn-detr/models/DN_DAB_DETR/DABDETR.py code served (permissive licence) · get_code("530114cee9263694") |
| DABDeformableDETR | Not yet run | helq2612/biadt/models/dn_dab_deformable_detr/dab_deformable_detr.py code served (permissive licence) · get_code("fe79de37d598acb3") |
| _onnx_nested_tensor_from_tensor_list | Not yet run | helq2612/biadt/models/dn_dab_deformable_detr/dab_deformable_detr.py code served (permissive licence) · get_code("50ec3e702e11eebb") |
| prepare_for_dn | Not yet run | helq2612/biadt/models/dn_dab_deformable_detr/dab_deformable_detr.py code served (permissive licence) · get_code("af4a2db04af02c0a") |
| prepare_for_dn | Not yet run | idea-research/dn-detr/models/DN_DAB_DETR/DABDETR.py code served (permissive licence) · get_code("daa15d8205b6349a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer-by-layer. Using box coordinates not only helps using explicit positional priors to improve the queryto-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer-by-layer in a cascade manner. As a result, it leads to the best performance on MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.12329")
get_code_for_paper("2201.12329")
have("2201.12329")
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