WosberbonDesu/Fruit-Detection

:star: Real Time Fruit Detectipn

Fruit-Detection

⭐ Real Time Fruit Detection

real time fruit identifier

1. Tomato
2. Banana
3. Blueberry
4. Strawberry
5. Corn
6. Crimson-Golden Apple
7. Lemon and Lime
8. Avocado
9. Cherry
10. Raspberry

It is written in the explanations about the codes

dataset i use; https://www.kaggle.com/moltean/fruits

you can increase the layers to make the model more accurate

model.add(Dense(units = 256,activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(units = 256,activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(units = 256,activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(units = 256,activation = 'relu'))
model.add(Dropout(0.2))

and epoch size like;

hist = model.fit_generator(dataGen.flow(x_train,y_train,batch_size=batch_size),
                                        validation_data = (x_validation,
                                                           y_validation),
                                        epochs = 60, <------------------
                                        steps_per_epoch = x_train.shape[0]//batch_size,
                                        shuffle = 1)

And zoom_range the higher you raise, the more successful you will predict

dataGen = ImageDataGenerator(width_shift_range = 0.1,
                             height_shift_range = 0.1,
                             zoom_range = 0.6, <-------
                             rotation_range = 10)

If you increase the data set to a higher amount I used 600 photos

When splitting the data set, you can replace the training data with

x_train, x_test, y_train, y_test = train_test_split(images,classNo,test_size = 0.2, <------ %80 percent for training 
                                                    random_state = 42)