Click here to Skip to main content
15,440,014 members
Please Sign up or sign in to vote.
5.00/5 (1 vote)
My project is hand sign recognition, I created the dataset and i wanted to train the model using keras and predict in real time .
I trained the model and i am getting better accuracy but all the predictions (completely ) are worng.
How do i get accurate predictions. Is there a defect in training or testing the model . How do i resolve this problem. Please help me.

What I have tried:

My cnn_model.py is as follows

import numpy as np
import keras
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras import regularizers,callbacks

# Initialing the CNN
model = Sequential()
    
model.add(Conv2D(16, kernel_size = [3,3], padding = 'same', activation = 'relu', input_shape = (224,224,3)))
model.add(Conv2D(32, kernel_size = [3,3], padding = 'same', activation = 'relu'))
model.add(MaxPool2D(pool_size = [3,3]))
    
model.add(Conv2D(32, kernel_size = [3,3], padding = 'same', activation = 'relu'))
model.add(Conv2D(64, kernel_size = [3,3], padding = 'same', activation = 'relu'))
model.add(MaxPool2D(pool_size = [3,3]))
    
model.add(Conv2D(128, kernel_size = [3,3], padding = 'same', activation = 'relu'))
model.add(Conv2D(256, kernel_size = [3,3], padding = 'same', activation = 'relu'))
model.add(MaxPool2D(pool_size = [3,3]))
     
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(512, activation = 'relu', kernel_regularizer = regularizers.l2(0.001)))
model.add(Dense(29, activation = 'softmax'))
    
model.compile(optimizer = 'adam', loss = keras.losses.categorical_crossentropy, metrics = ["accuracy"])
    
print("MODEL CREATED")
print(model.summary())

#Compiling The CNN
#Part 2 Fittting the CNN to the image
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory(
        'Dataset1/train',
        target_size=(224,224),
        batch_size=16,
        class_mode='categorical')

test_set = test_datagen.flow_from_directory(
        'Dataset1/test',
        target_size=(224 ,224),
        batch_size=16,
        class_mode='categorical')

earlystopping = callbacks.EarlyStopping(monitor="val_loss" , mode = "min" , patience=5 , restore_best_weights=True , verbose=1)

model.fit(
        training_set,
        steps_per_epoch=4793/16,
        epochs=30,
        validation_data = test_set,
        validation_steps = 1582/16,
        callbacks= [earlystopping]
      )

loss, accuracy = model.evaluate(test_set)
print('Final Accuracy of your model is :: %.2f%% '%(accuracy * 100))
print('Final Loss of your model is :: %.2f%% '%(loss * 100))
model.save("Trained_model.h5")
print("Saved model to disk")

"""
Final Accuracy of your model is :: 95.02%
Final Loss of your model is :: 30.57%
Saved model to disk
"""


In order to test the model i made randam images for each sign and tested the model against it.It predicted completely wrong.

The code is as follows
import string
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
import os
from PIL import ImageOps,Image

# image folder
folder_path = 'asl_alphabet_test'
# path to model
model_path = 'Trained_model.h5'
with open("Teachable_machine/keras/labels.txt", 'r') as f:
    labels =  [line.strip() for line in f.readlines()]

# load the trained model
model = load_model(model_path)
#model.evaluate()
# load all images into a list
a = list(string.ascii_uppercase)
a.extend(['Del','Nothing','Space'])
#print(a)
for i in a:   
    img = image.load_img("asl_alphabet_test/"+i+".jpg")
    img=image.img_to_array(img)
    img = np.expand_dims(img,axis=0)
    img = img/255
    result = model.predict(img)
    #print(result)
    top_k = result[0].argsort()[-len(result[0]):][::-1]
    a = []
    for j in top_k:
            sign = labels[j]
            score = result[0][j]
            a.append((sign,score))
    a = sorted(a, key = lambda x: x[1],reverse=True)
    print(i,a[0][0] , a[0][1]*100) 

"""
output
A Predicted label :  J
B Predicted label :  L
C Predicted label :  C
D Predicted label :  D
E Predicted label :  F
F Predicted label :  L
G Predicted label :  H
H Predicted label :  I
I Predicted label :  Nothing
J Predicted label :  K
K Predicted label :  L
L Predicted label :  M
M Predicted label :  O
N Predicted label :  O
O Predicted label :  D
P Predicted label :  R
Q Predicted label :  S
R Predicted label :  L
S Predicted label :  Q
T Predicted label :  T
U Predicted label :  Z
V Predicted label :  Y
W Predicted label :  Z
X Predicted label :  Del
Y Predicted label :  Space
Z Predicted label :  Nothing
Del Predicted label :  E
Nothing Predicted label :  P
Space Predicted label :  V
"""


You can access the model and test images in the following link
https://www.dropbox.com/sh/ok1v4hbr8ohcfi4/AABfCDKe1Ki6fj-K0nofl2J6a?dl=0<pre>
Posted

This content, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)



CodeProject, 20 Bay Street, 11th Floor Toronto, Ontario, Canada M5J 2N8 +1 (416) 849-8900