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license: mit |
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metrics: |
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- accuracy |
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pipeline_tag: object-detection |
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tags: |
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- game |
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- tekken |
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- tekken 8 |
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- throw |
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- detection |
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This model is designed for detecting throw capture moments in Tekken 8 gameplay. |
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It is based on the VGG16 architecture, modified by removing the top layer to serve as a feature extractor. |
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The model was trained using Keras on a dataset comprising video compilations from Tekken 8 fights, resulting in a total of 701,990 images at a resolution of 640x360. Approximately 5,000 of these images feature throw captures. Training involved augmentation techniques such as slight color shifting and the addition of mild color or black-and-white noise to enhance model robustness. |
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The model underwent 65 training cycles, each consisting of 13 epochs. |
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In each cycle, a batch of 250 randomly selected images from the dataset was used, with at least 40 images depicting throw captures. The batch size was set to 20. |
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The custom top layer added for this task includes a Flatten layer followed by a Dense layer with 128 units and 'relu' activation, a Dropout layer with a rate of 0.4, and a final Dense layer with 1 unit and 'sigmoid' activation to predict throw captures. |
