vishaljha2121 / ImageClassifier-DogVsCat

The Architecture and parameter used in this network are capable of producing accuracy of 97.56% on Validation Data which is pretty good. It is possible to Achieve more accuracy on this dataset using deeper network and fine tuning of network parameters for training.

Dog v/s Cat classifier

WEB APPLICATION

REACT FRONTEND

Uploading file to the server API

fileUploadHandler = () => {    
    header.append("Access-Control-Allow-Origin", "*");
    header.append("Access-Control-Allow-Credentials", "true");
    var  fd = new  FormData();    
    fd.append("image", this.state.selectedFile, header);    
    axios.post(apiUrl, fd).then((res) => {    
	    this.setState({    
		    animal:  res.data["label"],
	    });    
	    return  res;    
    });    
};

To run the frontend,

cd server
npm start

FLASK RestFUL API

The model saves the uploaded photo locally to run prediction model on it.

def  upload():
    fetch file from json input
    file = flask.request.files["image"]
    if  file == "":
	    return  "Please enter an image"
    file_location = os.path.join(UPLOAD_FOLDER, file.filename)
    file.save(file_location)
    call prediction function
    result = predict_results(file_location)
    return result

CNN Deep learning model

Convolution Neural Network(CNN) Classifier for Classifying dog and cat images. The Total number of images available for training is 8,000 and final testing is done on seperate 8,000 images.

Dependencies

• Jupyter notebook
• Tensorflow 1.10
• Python 3.6
• Matplotlib
• Seaborn
• Scikit-Learn
• Pandas
• Numpy Dependencies can be installed by using server/requirement.txt file.

MODEL ARCHITECTURE

cnn = tf.keras.models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3,activation='relu', input_shape=[64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))
cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))

cnn.compile(optimizer='adam', loss  =  'binary_crossentropy', metrics  = ['accuracy'])

Conclusion

The Architecture and parameter used in this network are capable of producing accuracy of 97.56% on Validation Data which is pretty good. It is possible to Achieve more accuracy on this dataset using deeper network and fine tuning of network parameters for training. You can download this trained model from resource directory and Play with it.