We lifted 18 functions out of this paper's own repositories and ran 15 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 |
|---|---|---|
| HDETR/H-Deformable-DETR | canonical | 7 of 8 |
| HDETR/H-Detic-LVIS | canonical | 6 of 8 |
| IDEA-Research/detrex | extension | 1 of 1 |
| HDETR/H-PETR-3D | extension | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| window_partition | Ran | HDETR/H-Deformable-DETR/models/swin_transformer.py code served (permissive licence) · get_code("144d10b49baeb8a6") |
| convert_to_xywh | Ran | HDETR/H-Deformable-DETR/datasets/coco_eval.py code served (permissive licence) · get_code("f31a58bf6457ced5") |
| dice_loss | Ran | HDETR/H-Deformable-DETR/models/segmentation.py code served (permissive licence) · get_code("ac8fe530cdad4d8c") |
| evaluate | Ran | HDETR/H-Deformable-DETR/datasets/coco_eval.py code served (permissive licence) · get_code("fe0ddcc2d420c9a0") |
| get_clip_embeddings | Ran | HDETR/H-Detic-LVIS/detic/predictor.py code served (permissive licence) · get_code("1f211df98dc51cee") |
| get_fed_loss_inds | Ran | HDETR/H-Detic-LVIS/detic/modeling/utils.py code served (permissive licence) · get_code("bb583e07209fe58b") |
| get_vit_lr_decay_rate | Ran | IDEA-Research/detrex/detrex/modeling/backbone/eva.py code served (permissive licence) · get_code("08bf7ecdb2fffe84") |
| load_class_freq | Ran | HDETR/H-Detic-LVIS/detic/modeling/utils.py code served (permissive licence) · get_code("1f6e4c3da7b82979") |
| match_name_keywords | Ran | HDETR/H-Detic-LVIS/detic/custom_solver.py code served (permissive licence) · get_code("c15434906f804d7f") |
| pos2posemb3d | Ran | HDETR/H-PETR-3D/projects/mmdet3d_plugin/models/dense_heads/hybrid_petrv2_head.py pointer only (licence: NOASSERTION) · get_code("718ca707584a0928") |
| sigmoid_focal_loss | Ran | HDETR/H-Deformable-DETR/models/segmentation.py code served (permissive licence) · get_code("5c0711aada67957e") |
| train_hybrid | Ran | HDETR/H-Deformable-DETR/engine.py code served (permissive licence) · get_code("8cc99e95f95cb562") |
| window_partition | Ran | HDETR/H-Detic-LVIS/detic/modeling/backbone/swintransformer.py code served (permissive licence) · get_code("f9fd6241d935f07b") |
| window_reverse | Ran | HDETR/H-Deformable-DETR/models/swin_transformer.py code served (permissive licence) · get_code("61bf152e6a42a184") |
| window_reverse | Ran | HDETR/H-Detic-LVIS/detic/modeling/backbone/swintransformer.py code served (permissive licence) · get_code("fb32094c6dbece71") |
| compute_average_precision | Not yet run | HDETR/H-Detic-LVIS/detic/evaluation/oideval.py code served (permissive licence) · get_code("2e0a799ccbd489e7") |
| create_timm_resnet | Not yet run | HDETR/H-Detic-LVIS/detic/modeling/backbone/timm.py code served (permissive licence) · get_code("3634f2208a0255d7") |
| measure_average_inference_time | Not yet run | HDETR/H-Deformable-DETR/benchmark.py code served (permissive licence) · get_code("3e5d34f155e97585") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including DeformableDETR, PETRv2, PETR, and TransTrack, among others. The code is available at: https://github.com/HDETR
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2207.13080")
get_code_for_paper("2207.13080")
have("2207.13080")
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