Update dataset card with paper, project, and GitHub links

README.md

@@ -1,49 +1,51 @@
 ---
+license: cc-by-4.0
+size_categories:
+- n<1K
+task_categories:
+- video-classification
+- visual-question-answering
+pretty_name: PushUpBench
 dataset_info:
   features:
-    - name: id
+  - name: id
+    dtype: int64
+  - name: name
+    dtype: string
+  - name: video_path
+    dtype: string
+  - name: count
+    sequence:
       dtype: int64
-    - name: name
-      dtype: string
-    - name: video_path
-      dtype: string
-    - name: count
-      sequence:
-        dtype: int64
-    - name: fuzzy_action
-      dtype: bool
-    - name: complex_action
-      dtype: bool
+  - name: fuzzy_action
+    dtype: bool
+  - name: complex_action
+    dtype: bool
   splits:
-    - name: test
-      num_examples: 227
+  - name: test
+    num_examples: 227
 configs:
-  - config_name: default
-    data_files:
-      - split: test
-        path: data/test.jsonl
-license: cc-by-4.0
-task_categories:
-  - video-classification
-  - visual-question-answering
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test.jsonl
 tags:
-  - video
-  - counting
-  - repetition-counting
-  - exercise
-  - benchmark
-pretty_name: PushUpBench
-size_categories:
-  - n<1K
+- video
+- counting
+- repetition-counting
+- exercise
+- benchmark
 ---
 
 # PushUpBench: Video Repetition Counting Benchmark
 
-PushUpBench is a benchmark for evaluating vision-language models on their ability to count exercise repetitions in videos.
+[**Project Page**](https://pushupbench.com) | [**Paper**](https://huggingface.co/papers/2604.23407) | [**GitHub**](https://github.com/EvolvingLMMs-Lab/lmms-eval)
+
+PushUpBench is a benchmark for evaluating vision-language models (VLMs) on their ability to count exercise repetitions in videos. It was introduced in the paper ["PushupBench: Your VLM is not good at counting pushups"](https://huggingface.co/papers/2604.23407). The dataset consists of 446 long-form clips (averaging 36.7s) designed to test temporal reasoning and repetition counting beyond simple pattern recognition.
 
 ## Dataset Description
 
-- **Total samples**: 227
+- **Total samples**: 446 clips (227 in the test split)
 - **Video format**: MP4
 - **Task**: Count the number of repetitions of a specified exercise in a video
 
@@ -58,6 +60,8 @@ Each sample contains:
 
 ## Usage with lmms-eval
 
+PushUpBench is incorporated in the [`lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval) toolkit.
+
 ```bash
 # Set the video directory
 export PUSHUPBENCH_VIDEO_DIR=/path/to/videos
@@ -72,6 +76,6 @@ python -m lmms_eval \
 
 ## Metrics
 
-- **Exact Match**: Prediction matches any value in the ground truth count list
-- **MAE**: Mean Absolute Error between prediction and primary ground truth
-- **OBO**: Off-By-One accuracy (prediction within 1 of any ground truth)
+- **Exact Match**: Prediction matches any value in the ground truth count list.
+- **MAE**: Mean Absolute Error between prediction and primary ground truth.
+- **OBO**: Off-By-One accuracy (prediction within 1 of any ground truth).