I am developing a CNN model to recognize 24 hand-signs of American Sign Language. I have 2500 Images/hand-sign. The data split is:

Training: 1250 Images/hand-sign

Validation: 625 Images/hand-sign

Testing: 625 Images/hand-sign

How should I proceed with training the model?:

1. Should I develop a model starting from fewer hand-signs (like 5) and then increase them gradually?

2. Should I start models from scratch or use transfer learning (VGG16 or other)

Applying data augmentation, I did some tests with VGG16 and added a dense classifier at the end and received these accuracies:

Train: 0.6560544

Validation: 0.8376694

Test: 0.8810667

Model:

from tensorflow.keras.regularizers import l2

model = Sequential([

conv_base,

Flatten(),

Dropout(0.5),

Dense(512, activation='relu', kernel_regularizer=l2(0.01)),

Dropout(0.5),

Dense(NUM_CLASSES, activation='softmax')

])

EPOCHS = 100

STEPS_PER_EPOCH = 125

VALIDATION_STEPS = 75

TEST_STEPS = 75

Framework: Keras, Tensorflow

OPTIMIZER = adam

#artificialintelligence #deeplearning #cnn #handsignsrecognition #imageclassification #keras #tensorflow