We lifted 10 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| om-ai-lab/OmDet | canonical | 8 of 8 |
| om-ai-lab/ovdeval | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| apply_deltas | Ran | om-ai-lab/OmDet/omdet/modeling/common.py code served (permissive licence) · get_code("3e006b27eb5131f9") |
| calculate_iou | Ran | om-ai-lab/ovdeval/eval_glip.py code served (permissive licence) · get_code("f37e6f7d03c74102") |
| conv3x3 | Ran | om-ai-lab/OmDet/omdet/modeling/backbone/dlafpn.py code served (permissive licence) · get_code("dd1114865f06f0fd") |
| get_fpn_config | Ran | om-ai-lab/OmDet/omdet/modeling/backbone/bifpn.py code served (permissive licence) · get_code("7e97d239afcab15e") |
| get_model_url | Ran | om-ai-lab/OmDet/omdet/modeling/backbone/dlafpn.py code served (permissive licence) · get_code("738b4871dc26c54f") |
| get_output_shape | Ran | om-ai-lab/OmDet/omdet/inference/base_engine.py code served (permissive licence) · get_code("9c966a0badf6f168") |
| nms_threaded | Ran | om-ai-lab/ovdeval/eval_glip.py code served (permissive licence) · get_code("bdd48ffb812ebe9b") |
| swish | Ran | om-ai-lab/OmDet/omdet/modeling/backbone/bifpn.py code served (permissive licence) · get_code("717e4d22b6d103e6") |
| window_partition | Ran | om-ai-lab/OmDet/omdet/modeling/backbone/swint.py code served (permissive licence) · get_code("f9fd6241d935f07b") |
| window_reverse | Ran | om-ai-lab/OmDet/omdet/modeling/backbone/swint.py code served (permissive licence) · get_code("fb32094c6dbece71") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Object detection (OD) in computer vision has made significant progress in recent years, transitioning from closed-set labels to open-vocabulary detection (OVD) based on large-scale vision-language pre-training (VLP). However, current evaluation methods and datasets are limited to testing generalization over object types and referral expressions, which do not provide a systematic, fine-grained, and accurate benchmark of OVD models' abilities. In this paper, we propose a new benchmark named OVDEval, which includes 9 sub-tasks and introduces evaluations on commonsense knowledge, attribute understanding, position understanding, object relation comprehension, and more. The dataset is meticulously created to provide hard negatives that challenge models' true understanding of visual and linguistic input. Additionally, we identify a problem with the popular Average Precision (AP) metric when benchmarking models on these fine-grained label datasets and propose a new metric called Non-Maximum Suppression Average Precision (NMS-AP) to address this issue. Extensive experimental results show that existing top OVD models all fail on the new tasks except for simple object types, demonstrating the value of the proposed dataset in pinpointing the weakness of current OVD models and guiding future research. Furthermore, the proposed NMS-AP metric is verified by experiments to provide a much more truthful evaluation of OVD models, whereas traditional AP metrics yield deceptive results. Data is available at \url{https://github.com/om-ai-lab/OVDEval}
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.13177")
get_code_for_paper("2308.13177")
have("2308.13177")
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