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