https://github.com/yuhexiong/cat-and-dog-classification-cnn-resnet50-python

Cats and dogs images classifier using Python CNN ResNet50.
https://github.com/yuhexiong/cat-and-dog-classification-cnn-resnet50-python

cnn data-augmentation python resnet-50 tensorflow

Last synced: 2 months ago
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Cats and dogs images classifier using Python CNN ResNet50.

• Host: GitHub
• URL: https://github.com/yuhexiong/cat-and-dog-classification-cnn-resnet50-python
• Owner: yuhexiong
• Created: 2023-12-19T14:34:45.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-21T05:43:40.000Z (over 1 year ago)
• Last Synced: 2025-01-30T01:14:40.460Z (over 1 year ago)
• Topics: cnn, data-augmentation, python, resnet-50, tensorflow
• Language: Python
• Homepage:
• Size: 188 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README-CH.md

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README

          
# Cat and Dog CNN with ResNet50

### 資料集來源：[Kaggle - Cat and Dog](https://www.kaggle.com/datasets/tongpython/cat-and-dog)

**注意**：由於資料集過大，無法直接包含在此。請從提供的 Kaggle 連結自行下載。

使用 ResNet50 再自行疊加其他神經網路層，將貓咪與狗的圖片進行分類，最後倒出 2 個神經元，分別代表貓狗。  

## Overview

- Language: Python v3.10.12

- Package: Tensorflow

- Model: CNN(ResNet50)

## Model Architecture

模型使用 **Cross Entropy** 作為損失函數，採用 **Adam** 優化器，學習率設定為 **0.0001**，並應用 **數據擴增** 技術來減少過擬合，透過生成訓練圖像的變化來達成。  

```

              OPERATION        DATA DIMENSIONS   WEIGHTS(N)   WEIGHTS(%)

              Input   #####       3  224  224

         InputLayer     |      ----------------          0         0.0%

                      #####       3  224  224

      ResNet50 (Base)  \|/     ----------------    2359808         1.7%

               -      #####     512  224  224

       MaxPooling2D   Y max    ----------------          0         0.0%

                      #####     512  112  112

      Convolution2D    \|/     ----------------     147584         0.1%

               relu   #####     128  112  112

       MaxPooling2D   Y max    ----------------          0         0.0%

                      #####     128   56   56

           Flatten    |||||    ----------------          0         0.0%

                      #####         50176

              Dense   XXXXX    ----------------    1605696        74.3%

               relu   #####          32

           Dropout    |||||    ----------------          0         0.0%

                      #####          32

              Dense   XXXXX    ----------------         64         2.8%

               relu   #####           2

              Dense   XXXXX    ----------------         64         2.8%

            softmax   #####           2

```

## Conclusion

### Loss

![Loss](./image/loss.png)

### Accuracy

![Accuracy](./image/accuracy.png)

### Confusion Matrix - Accuracy Rate 97.53%

![image](./image/confusion_matrix.png)