Update README.md

README.md

@@ -122,7 +122,7 @@ task_categories:
 
 <img src="https://data.matsworld.io/ucbresearch/deepaction.gif" style="width: 100%">
 
-The DeepAction dataset contains over 3,000 videos generated by seven text-to-video AI models, as well as real matched videos. These videos show people performing ordinary actions such as walking, running, and cooking. The AI models used to generate these videos include, in alphabetic order, AnimateDiff, CogVideoX5B, Lumiere, Pexels, RunwayML, StableDiffusion, Veo (pre-release version), and VideoPoet. Refer to our <a href=''>our pre-print</a> for details.
+The DeepAction dataset contains over 3,000 videos generated by six text-to-video AI models, as well as real matched videos. These videos show people performing ordinary actions such as walking, running, and cooking. The AI models used to generate these videos include, in alphabetic order, AnimateDiff, CogVideoX5B, Pexels, RunwayML, StableDiffusion, Veo (pre-release version), and VideoPoet. Refer to our <a href=''>our pre-print</a> for details.
 
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@@ -139,7 +139,7 @@ dataset = load_dataset("faridlab/deepaction_v1", trust_remote_code=True)
 
 ## Data
 
-The data is structured into seven folders corresponding to text-to-video AI models, each with 100 subfolders corresponding to human action classes. All videos in a given subfolder were generated using the same prompt (see the list of prompts <a href=''>here</a>).
+The data is structured into seven folders, with six folders corresponding to text-to-video AI models and one folder for real videos. Each of these folders has 100 subfolders corresponding to human action classes. All videos in a given subfolder were generated using the same prompt (see the list of prompts <a href=''>here</a>).
 
 Included below are example videos generated using the prompt "a person taking a selfie". Note that, since each text-to-video AI model generates videos with different ratios and resolutions, these videos were normalized 512x512.