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Algorithms and Deep Learning Models/Cricket Shots Deep Learning Model Expand file tree Collapse file tree 11 files changed +1245
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+ # <h1 align = " center " > Cricket Shots Deep Learning Model</h1 >
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+ ## Aim of the project:
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+ ### The project focuses on classification of different cricket shots using various Deep Learning Algorithms.
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+
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+ ## Deep Learning Algorithms used:
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+ 1 . ResNet
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+ 2 . DenseNet
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+ 3 . InceptionNet
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+ 4 . EfficientNet
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+
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+ ### Libraries and Frameworks used:
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+ 1 . Pandas
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+ 2 . Numpy
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+ 3 . Matplotlib
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+ 4 . Seaborn
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+ 5 . Tensorflow
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+ 6 . Keras
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+ 7 . sklearn
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+ 8 . glob
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+ 9 . OpenCV
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+
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+ ## Accuracy and training time comparison of all the Deep Learning Algorithms
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+ | | Accuracy |
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+ | --------------------| ---------------|
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+ | ResNet | 86% |
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+ | DenseNet | 92% |
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+ | InceptionNet | 96% |
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+ | EfficientNet | 95% |
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+
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+ # Representation of different cricket shots
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+ ![ EDA] ( https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/eda_cric.png )
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+
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+ # Bar plot of counts of each shot in the dataset
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+ ![ values] ( https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/bar.png )
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+
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+ # Pie chart for the distribution of shots in the dataset
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+ ![ ri] ( https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/pie%20chart.png )
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+
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+
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+ # Accuracy and plots of all models
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+
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+ ## InceptionNetV2
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+ ![ inv2] ( https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/inception.png )
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+
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+ ## DenseNet
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+ ![ densenet] ( https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/dense.png )
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+
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+ ## ResNet50
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+ ![ resnet] ( https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/resnet.png )
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+
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+ ## EfficientNet
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+ ![ effnet] ( https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/efficient.png )
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+
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+
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+ # Conclusion
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+ InceptionNet model performs better comparative to other models used on the above dataset.
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+ # Dataset
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+
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+ The dataset used in this project is take from the Kaggle website.
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+ <br >
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+ <b >Dataset Link:- https://www.kaggle.com/datasets/aneesh10/cricket-shot-dataset </b >
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+ <br >
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+
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+ <br >
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+ 1 . The directory drives consists of the cover drive, straight drive and off drive.
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+ <br >
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+ 2 . The directory legglance-flick contains the images for the leg glance and flick shot.
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+ <br >
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+ 3 . The directory pullshot has the images for pull shot.
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+ <br >
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+ 4 . The directory sweep has the image for sweep shot.
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+ <br >
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+
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+
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+
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