# Flower-Recognition-using-transfer-learning

**Repository Path**: cheng_xiaofeng_1996/Flower-Recognition-using-transfer-learning

## Basic Information

- **Project Name**: Flower-Recognition-using-transfer-learning
- **Description**: Transfer Learning using vgg16 and resNet50
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-27
- **Last Updated**: 2020-12-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Flower-Recognition-using-transfer-learning
## Transfer Learning using vgg16 and resNet50

## Contents 
### 1. Datasets:
[click here](https://www.kaggle.com/alxmamaev/flowers-recognition) to download the dataset. After downloading extract the zip file.
The dataset contain five listdir namely.['sunflower','tulip','daisy','rose','dandelion'] with total of 4241 images
![alt text](https://github.com/Devkul95/Flower-Recognition-using-transfer-learning/blob/master/Readme%20Images/flower.JPG)

### 2. Importing Various Modules

### 3. Preparing the Data

### 4. Vgg16 Model:
Model.summary()
![alt text](https://github.com/Devkul95/Flower-Recognition-using-transfer-learning/blob/master/Readme%20Images/model.JPG)

### 5. Evaluating the Model Performance
### 6. Visualizing Predictions on the Validation Set