Update README.md

README.md

@@ -30,7 +30,7 @@ configs:
 Marc Chapus, Carlos Crispim-Junior, Véronique Eglin, Atilla Baskurt
 LIRIS, UMR 5205 — INSA Lyon, Université Lumière Lyon 2, CNRS, Université Claude Bernard Lyon 1
 
-**BMVC 2026**
+**BMVC 2026** · [Project page](https://countvideobench.github.io)
 
 ## Dataset summary
 
@@ -42,7 +42,7 @@ CountVideoBench contains **2,600 synthetic videos** rendered with Blender, each
 - **Static objects, moving camera**: objects are static and the camera rotates in place. The white background carries a regular line pattern, and object surfaces a low-amplitude procedural noise, so that densely packed objects stay visually distinct.
 - **Exact ground truth**: counts come from the generation process, with no human annotation.
 
-The benchmark is far from saturated: humans reach 90.8% exact-match accuracy, while no model evaluated in the paper exceeds 27.6%.
+The benchmark is far from saturated: no model evaluated in the paper exceeds 27.6% exact-match accuracy. On a 104-video subset, human participants reach 90.8%, against 26.9% for the best models on the same videos.
 
 ## Example
 
@@ -85,17 +85,35 @@ Videos: MP4, 1920 × 1080, 30 fps, 5 seconds (150 frames). The full clips are pr
 
 ## Usage
 
+Load the dataset in Python with the [`datasets`](https://huggingface.co/docs/datasets) library, or download the video files with the Hugging Face command-line tool [`hf`](https://huggingface.co/docs/huggingface_hub/guides/cli). The dataset is public: no account is needed.
+
 ```python
-from datasets import load_dataset
+# pip install datasets
+from datasets import load_dataset, Video
 
+# Downloads the full dataset (139 GB) on first call
 ds = load_dataset("countvideobench/CountVideoBench", "single_class", split="test")
-print(ds[0]["count"], ds[0]["question"])
+
+# Keep file paths instead of decoded videos, to sample frames your own way
+ds = ds.cast_column("video", Video(decode=False))
+
+sample = ds[0]
+print(sample["question"], sample["count"], sample["video"]["path"])
 ```
 
+Without the `cast_column` line, accessing a row decodes its video, which requires [`torchcodec`](https://github.com/meta-pytorch/torchcodec) and FFmpeg.
+
 To download the raw files instead:
 
 ```bash
+# pip install -U huggingface_hub   (provides the hf command)
+
+# All 2,600 videos
 hf download countvideobench/CountVideoBench --repo-type dataset --local-dir CountVideoBench
+
+# Or one count only, e.g. the 100 videos with 20 objects
+hf download countvideobench/CountVideoBench --repo-type dataset --local-dir CountVideoBench \
+  --include "single_class/metadata.csv" "single_class/20_entities/*"
 ```
 
 ## Intended uses