The intention here is to create a generic repositories which contain various practical applications in the field of deep learning.
PROJECT 1. IMAGE CLASSIFICATION (MNIST-10) USING CNNs - APPLICATION
PROJECT 2. IMAGE CLASSIFICATION (CIFAR-10) USING CNNs - APPLICATION
PROJECT 3. CLASSIFY GERMAN TRAFFIC SIGNS (LENET) USING CNNs - APPLICATION
PROJECT 4. CLASSIFY FASHION IMAGES(FASHION-MNIST) USING CNNs - APPLICATION
PROJECT 5. CLASSIFY CUSTOM IMAGE CLASSIFICATION -APPLICATION
The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. The digits have been size-normalized and centered in a fixed-size image. It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9. The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively.
https://en.wikipedia.org/wiki/MNIST_database
DATASET : http://yann.lecun.com/exdb/mnist/
(A) Model Summary, Confusion Matrix and Prediction vs Actual reporting
CIFAR-10 is a dataset that consists of several images divided into the following 10 classes: 0: Airplanes � 1: Cars � 2: Birds � 3: Cats � 4: Deer � 5: Dogs � 6: Frogs � 7: Horses � 8: Ships � 9: Trucks
The dataset stands for the Canadian Institute For Advanced Research (CIFAR) CIFAR-10 is widely used for machine learning and computer vision applications. The dataset consists of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images.
DATASET: https://www.kaggle.com/c/cifar-10/ or https://www.cs.toronto.edu/~kriz/cifar.html
(A) Model Summary, confusion Metrix and Prediction vs Actual reporting
• The dataset contains 43 different classes of images. Classes are as listed below: • ( 0, b'Speed limit (20km/h)') ( 1, b'Speed limit (30km/h)') • ( 2, b'Speed limit (50km/h)') ( 3, b'Speed limit (60km/h)') • ( 4, b'Speed limit (70km/h)') ( 5, b'Speed limit (80km/h)') • ( 6, b'End of speed limit (80km/h)') ( 7, b'Speed limit (100km/h)') • ( 8, b'Speed limit (120km/h)') ( 9, b'No passing') • (10, b'No passing for vehicles over 3.5 metric tons') • (11, b'Right-of-way at the next intersection') (12, b'Priority road') • (13, b'Yield') (14, b'Stop') (15, b'No vehicles') • (16, b'Vehicles over 3.5 metric tons prohibited') (17, b'No entry') • (18, b'General caution') (19, b'Dangerous curve to the left') • (20, b'Dangerous curve to the right') (21, b'Double curve') • (22, b'Bumpy road') (23, b'Slippery road') • (24, b'Road narrows on the right') (25, b'Road work') • (26, b'Traffic signals') (27, b'Pedestrians') (28, b'Children crossing') • (29, b'Bicycles crossing') (30, b'Beware of ice/snow') • (31, b'Wild animals crossing') • (32, b'End of all speed and passing limits') (33, b'Turn right ahead') • (34, b'Turn left ahead') (35, b'Ahead only') (36, b'Go straight or right') • (37, b'Go straight or left') (38, b'Keep right') (39, b'Keep left') • (40, b'Roundabout mandatory') (41, b'End of no passing') • (42, b'End of no passing by vehicles over 3.5 metric tons') The network used is called Le-Net that was presented by Yann LeCun
http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
(A) Model Summary, confusion Metrix and Prediction vs Actual reporting
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
Each training and test example is assigned to one of the following labels:
0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot
DATASET: https://github.com/zalandoresearch/fashion-mnist
DATASET was converted to CSV with this script: https://pjreddie.com/projects/mnist-in-csv/
The intention here is to create a generic custom image classifier which can be used with any real world custom image classification.
