We lifted 10 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| opendatalab/unimernet | canonical | 8 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| apply_to_sample | Ran | opendatalab/unimernet/unimernet/datasets/data_utils.py code served (permissive licence) · get_code("992489a59b9e08d0") |
| getAttMap | Ran | opendatalab/unimernet/unimernet/common/gradcam.py code served (permissive licence) · get_code("7d214655954e2bc6") |
| is_url | Ran | opendatalab/unimernet/unimernet/common/utils.py code served (permissive licence) · get_code("4d1d9c47619920ba") |
| main_process | Ran | opendatalab/unimernet/unimernet/common/dist_utils.py code served (permissive licence) · get_code("98589643273920ba") |
| move_to_cuda | Ran | opendatalab/unimernet/unimernet/datasets/data_utils.py code served (permissive licence) · get_code("f0d0d2447408bb09") |
| node_to_dict | Ran | opendatalab/unimernet/unimernet/common/config.py code served (permissive licence) · get_code("791c72070c0b1cfb") |
| prepare_sample | Ran | opendatalab/unimernet/unimernet/datasets/data_utils.py code served (permissive licence) · get_code("9d94c9c2524f4a1b") |
| tile | Ran | opendatalab/unimernet/unimernet/models/base_model.py code served (permissive licence) · get_code("cb33571427334815") |
| all_gather_with_grad | Not yet run | opendatalab/unimernet/unimernet/models/base_model.py code served (permissive licence) · get_code("0ec9fc2025c16f65") |
| download_cached_file | Not yet run | opendatalab/unimernet/unimernet/common/dist_utils.py code served (permissive licence) · get_code("643539116e049080") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Formula recognition presents significant challenges due to the complicated structure and varied notation of mathematical expressions. Despite continuous advancements in formula recognition models, the evaluation metrics employed by these models, such as BLEU and Edit Distance, still exhibit notable limitations. They overlook the fact that the same formula has diverse representations and is highly sensitive to the distribution of training data, thereby causing unfairness in formula recognition evaluation. To this end, we propose a Character Detection Matching (CDM) metric, ensuring the evaluation objectivity by designing an image-level rather than a LaTeX-level metric score. Specifically, CDM renders both the model-predicted LaTeX and the ground-truth LaTeX formulas into image-formatted formulas, then employs visual feature extraction and localization techniques for precise character-level matching, incorporating spatial position information. Such a spatially-aware and character-matching method offers a more accurate and equitable evaluation compared with previous BLEU and Edit Distance metrics that rely solely on text-based character matching. Experimentally, we evaluated various formula recognition models using CDM, BLEU, and ExpRate metrics. Their results demonstrate that the CDM aligns more closely with human evaluation standards and provides a fairer comparison across different models by eliminating discrepancies caused by diverse formula representations. Code is available at https://github.com/opendatalab/UniMERNet/tree/main/cdm.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2409.03643")
get_code_for_paper("2409.03643")
have("2409.03643")
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