added leaderboard

neurips23/README.md

@@ -252,27 +252,6 @@ There are several ways to get help as you develop your algorithm using this fram
 * You can submit an issue at this github repository.
 * Send en email to the competition's googlegroup, [email protected]
 
-### Leaderboard
-
-This leaderboard is based on the standard recall@10 vs throughput benchmark that has become a standard benchmark when evaluating and comparing approximate nearest neighbor algorithms. 
-The recall of the baselines at this QPS threshold is listed [above](#measuring_your_algorithm). 
-
-For tasks "Filter", "Out-of-Distribution" and "Sparse" tracks, algorithms were ranked on the QPS they achieve on the track dataset, as long as the recall@10 is at least 90%. 
-These results files for [Azure D8lds_v5](Azure_D8lds_v5_table.md) and [AWS EC2 c6i.2xlarge](ec2_c6i.2xlarge_table.md) list the maximum QPS measured for each algorihtm with at least 90% recall@10.
-
-For the Streaming track, algorithms will be ranked on recall@10, as long as each algorithm completes the runbook within the alloted 1 hour. 
-The [result file](streaming/res_final_runbook_AzureD8lds_v5.csv) lists measurements on Azure D8lds_v5.
-
-QPS vs recall@10 plots for tracks based on public queries on Azure D8lds_v5:
-**Filter track**
-![yfcc-10M](filter/plot_public_queries_AzureD8lds_v5.png)
-
-**OOD track**
-![text2image-10M](ood/plot_public_queries_AzureD8lds_v5.png)
-
-**Sparse track**
-![sparse-full](sparse/plot_public_queries_AzureD8lds_v5.png)
-
 ## Custom_Setup
 
 While we encourage using our framework for all steps of the evaluation, we consider open-source submissions that diverge from the proposed setup. 

neurips23/leaderboard.md

@@ -0,0 +1,20 @@
+### Leaderboard
+
+This leaderboard is based on the standard recall@10 vs throughput benchmark that has become a standard benchmark when evaluating and comparing approximate nearest neighbor algorithms. 
+The recall of the baselines at this QPS threshold is listed [above](#measuring_your_algorithm). 
+
+For tasks "Filter", "Out-of-Distribution" and "Sparse" tracks, algorithms were ranked on the QPS they achieve on the track dataset, as long as the recall@10 is at least 90%. 
+These results files for [Azure D8lds_v5](Azure_D8lds_v5_table.md) and [AWS EC2 c6i.2xlarge](ec2_c6i.2xlarge_table.md) list the maximum QPS measured for each algorihtm with at least 90% recall@10.
+
+For the Streaming track, algorithms will be ranked on recall@10, as long as each algorithm completes the runbook within the alloted 1 hour. 
+The [result file](streaming/res_final_runbook_AzureD8lds_v5.csv) lists measurements on Azure D8lds_v5.
+
+QPS vs recall@10 plots for tracks based on public queries on Azure D8lds_v5:
+**Filter track**
+![yfcc-10M](filter/plot_public_queries_AzureD8lds_v5.png)
+
+**OOD track**
+![text2image-10M](ood/plot_public_queries_AzureD8lds_v5.png)
+
+**Sparse track**
+![sparse-full](sparse/plot_public_queries_AzureD8lds_v5.png)