update

.gitattributes

@@ -30,6 +30,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
@@ -57,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+/workspace/cogview_intern/hy/VRDB-Video/videos filter=lfs diff=lfs merge=lfs -text

README.md

@@ -101,7 +101,7 @@ dataset_info:
   - name: regression
     num_examples: 1795
 
-- config_name: monetbench
+- config_name: test
   features:
   - name: internal_id
     dtype: string
@@ -114,7 +114,7 @@ dataset_info:
   - name: video2_path
     dtype: string
   splits:
-  - name: monetbench
+  - name: test
     num_examples: 1000
 
 configs:
@@ -126,10 +126,10 @@ configs:
   data_files:
   - split: regression
     path: regression/*.parquet
-- config_name: monetbench
+- config_name: test
   data_files:
-  - split: monetbench
-    path: monetbench/*.parquet
+  - split: test
+    path: test/*.parquet
 
 license: apache-2.0
 ---
@@ -142,7 +142,7 @@ This dataset is a comprehensive collection of video evaluation data designed for
 The dataset is structured to facilitate both model training and standardized evaluation:
 - `Train`: A primary training set with detailed multi-dimensional annotations
 - `Regression`: A regression set with paired preference data
-- `MonetBench`: A benchmark test set for standardized performance evaluation
+- `Test`: A video preference test set for standardized performance evaluation
 
 This holistic approach enables the development and validation of sophisticated video quality assessment models that can evaluate AI-generated videos across multiple critical dimensions, moving beyond simple aesthetic judgments to encompass technical accuracy, semantic consistency, and dynamic performance.
 
@@ -234,7 +234,7 @@ The `meta_mask` feature is used for balanced sampling during model training:
 cd videos
 tar -xvzf train.tar.gz
 tar -xvzf regression.tar.gz
-tar -xvzf monetbench.tar.gz
+tar -xvzf test.tar.gz
 ```
 
 We provide `extract.py` for processing the `train` dataset into JSONL format. The script can optionally extract the balanced positive/negative QA pairs used in VisionReward training by processing `meta_result` and `meta_mask` fields.

{monetbench → test}/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c22ed17de85575ba44863458692f2a34cf9e055732d4a2e0720c84b4b71f26cd
-size 96016
+oid sha256:f852e71c86e82346ff18a1a15cf24c448d6f93ddda17110434ba7e82897b699e
+size 95923

videos/{monetbench.tar.gz → test.tar.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:be9b2061ef1b7519982ecb0c041b451fd6e2f6a665c9c9d2c4258caf244aa821
-size 2551183879
+oid sha256:589531845cd550770e524740d577db92f51fe413ad564012039f0317b2139995
+size 2551171968