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@@ -101,3 +101,40 @@ print("Predictions:")
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  for i, prob in enumerate(verdict.predictions):
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  print(f" Label {i}: {prob * 100:.2f}%")
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  ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  for i, prob in enumerate(verdict.predictions):
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  print(f" Label {i}: {prob * 100:.2f}%")
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  ```
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+
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+ ## Labels
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+
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+ The model can detect the following labels:
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+
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+ - **AI_GEN**: Is the video AI-generated or not?
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+ - **ANIME_1D**: Is the video in 2D anime style?
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+ - **ANIME_2D**: Is the video in 3D anime style?
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+ - **VIDEO_GAME**: Does the video look like a video game?
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+ - **KLING**: Is the video generated by Kling?
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+ - **HIGGSFIELD**: Is the video generated by Higgsfield?
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+ - **WAN**: Is the video generated by Wan?
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+ - **MIDJOURNEY**: Is the video generated using images from Midjourney?
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+ - **HAILUO**: Is the video generated by Hailuo?
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+ - **RAY**: Is the video generated by Ray?
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+ - **VEO**: Is the video generated by Veo?
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+ - **RUNWAY**: Is the video generated by Runway?
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+ - **SORA**: Is the video generated by Sora?
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+ - **CHATGPT**: Is the video generated using images from ChatGPT?
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+ - **PIKA**: Is the video generated by Pika?
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+ - **HUNYUAN**: Is the video generated by Hunyuan?
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+ - **VIDU**: Is the video generated by Vidu?
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+
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+ > **Note**: The **AI_GEN** label is the most accurate as it has the most training data. Other labels have limited training data and may be less accurate.
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+
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+ ## Accuracy
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+
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+ The PR curve of the model is shown below:
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+
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+ <p align="center">
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+ <img src="https://github.com/LaunchPlatform/cakelens-v5/raw/master/assets/pr-curve.png?raw=true" alt="PR Curve" />
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+ </p>
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+
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+ At threshold 0.5, the model has an precision of 0.77 and a recall of 0.74.
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+ The dataset size is 5,093 videos for training and 498 videos for validation.
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+ Please note that the model is not perfect and may make mistakes.
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+