We lifted 8 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 |
|---|---|---|
| LADI-Dataset/ladi-overview | pwc_unofficial | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| convert_GPS_coord | Ran | LADI-Dataset/ladi-overview/inference/metadata_utils.py code served (permissive licence) · get_code("df28586862688bea") |
| get_optimizer | Ran | LADI-Dataset/ladi-overview/training/config_optimizer.py code served (permissive licence) · get_code("e175c428da9ee888") |
| parse_exif | Ran | LADI-Dataset/ladi-overview/inference/metadata_utils.py code served (permissive licence) · get_code("8eb8d51b3c79cdcb") |
| parse_gps | Ran | LADI-Dataset/ladi-overview/inference/metadata_utils.py code served (permissive licence) · get_code("cc098f3969a8eb0f") |
| postprocess_output | Ran | LADI-Dataset/ladi-overview/inference/aws_list_infer.py code served (permissive licence) · get_code("c08aa98802151ca1") |
| postprocess_output | Ran | LADI-Dataset/ladi-overview/inference/file_list_infer.py code served (permissive licence) · get_code("15730804c3ee9ec0") |
| get_dataloaders | Not yet run | LADI-Dataset/ladi-overview/training/config_dataloader.py code served (permissive licence) · get_code("4cf20d9e7e99b470") |
| get_datasets | Not yet run | LADI-Dataset/ladi-overview/training/config_dataset.py code served (permissive licence) · get_code("42c4f691d6a4bb77") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Video applications and analytics are routinely projected as a stressing and significant service of the Nationwide Public Safety Broadband Network. As part of a NIST PSCR funded effort, the New Jersey Office of Homeland Security and Preparedness and MIT Lincoln Laboratory have been developing a computer vision dataset of operational and representative public safety scenarios. The scale and scope of this dataset necessitates a hierarchical organization approach for efficient compute and storage. We overview architectural considerations using the Lincoln Laboratory Supercomputing Cluster as a test architecture. We then describe how we intelligently organized the dataset across LLSC and evaluated it with large scale imagery inference across terabytes of data.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1908.09006")
get_code_for_paper("1908.09006")
have("1908.09006")
Connect an agent — have() is free.