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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.

## Deep Learning Algorithms used:
1. ResNet
2. DenseNet
3. InceptionNet
4. EfficientNet

### Libraries and Frameworks used:
1. Pandas
2. Numpy
3. Matplotlib
4. Seaborn
5. Tensorflow
6. Keras
7. sklearn
8. glob
9. OpenCV

## Accuracy and training time comparison of all the Deep Learning Algorithms
| | Accuracy |
|--------------------|---------------|
| ResNet | 86% |
| DenseNet | 92% |
| InceptionNet | 96% |
| EfficientNet | 95% |

# Representation of different cricket shots
![EDA](https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/eda_cric.png)

# 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)

# 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)


# Accuracy and plots of all models

## InceptionNetV2
![inv2](https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/inception.png)

## DenseNet
![densenet](https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/dense.png)

## ResNet50
![resnet](https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/resnet.png)

## EfficientNet
![effnet](https://github.com/the-silent-geek/DL-Simplified/blob/2e8e0b207bd08e758fca8e93d5433c73f277ef1e/Cricket%20Shots%20Detection/images/efficient.png)


# Conclusion
InceptionNet model performs better comparative to other models used on the above dataset.
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# Dataset

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>
<br>

<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.
<br>



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pandas
numpy
matplotlib
glob
tensorflow
opencv
scikit-learn
seaborn
glob
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