We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| copy not recorded | — | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| calculate_accuracy | Ran | this paper's copy was not recorded; identical code first harvested from zrui94/insight_mx pointer only · get_code("f975336b5d5f1a80") |
| calculate_roc | Ran | this paper's copy was not recorded; identical code first harvested from zrui94/insight_mx pointer only · get_code("db4008738f6c3eae") |
| calculate_val | Ran | this paper's copy was not recorded; identical code first harvested from zrui94/insight_mx pointer only · get_code("c7c89ea2508beb10") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world applications, such as image captioning and news video analysis. Associated with this task, we design and provide concrete measurement set, evaluation protocol, as well as training data. We also present in details our experiment setup and report promising baseline results. Our benchmark task could lead to one of the largest classification problems in computer vision. To the best of our knowledge, our training dataset, which contains 10M images in version 1, is the largest publicly available one in the world.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1607.08221")
get_code_for_paper("1607.08221")
have("1607.08221")
Connect an agent — have() is free.