Day #126 - Deep Learning Class Notes

Lesson 8: Deep Learning Part 2 2018 - Single object detection

Advice

• If you have not come across doesn't mean its hard
• Type it out yourself all code everytime
•  Don't wait to be perfect before you start communicating

Neural Network Architecture

• Dataset
• Newtork Architecture (Number of convolution layers, pooling, dropouts, activation functions)
• Loss Function

Flow of Architecture for Single Object
1. I/p Image
2. ConvNet
3. Output Tensor vector

Flow of Architecture for Multiple Object
1. I/p Image
2. ConvNet
3. Output Tensor vector
4. 16 set of outputs

Notes

• Bounding Boxes
• Take Labelled data and generate classes
• Labeling is expensive 
• Pascal VOC Dataset
• Bounding Box with coordinates, category, image_id

Steps (Pytorch coding)

• Build Classifier
• Finding biggest object in each image and classify
• Go through each bounding box in image
• Get Largest One
• Using Restnet to Classify
• Model with 4 activations, mean square loss functions
• Multiple label classification
• Add Rotations, Flips, Constrast Changing

Architecture

• Flatten
• RELU
• Dropout
• Linear
• Batch Normalization
• Dropout
• Linear
• Loss functions

SSD

• Single Shot Detection
• Conv2D
• Number of anchor boxes

Analysis

• Transfer learning is done on top of it 
• Identify the highlighted sections

My Thoughts

• Perform Segmentation
• Pick the objects
• Train and Classify them

To Learn Items

• Python Debugger pdb.set_trace()
• Detail Specific Code Walkthrough
• lambda functions in Python

Adam

• Momentum on gradient
• Past Squared gradient

Happy Learning!!!