13 ratings
Implement siamese network with triplet loss function
Create the training loop for siamese network
Showcase this hands-on experience in an interview
In this 2-hour long guided-project course, you will learn how to implement a Siamese Network, you will train the network with the Triplet loss function. You will create Anchor, Positive and Negative image dataset, which will be the inputs of triplet loss function, through which the network will learn feature embeddings. Siamese Network have plethora of applications such as face recognition, signature checking, person re-identification, etc. In this project, you will train a simple Siamese Network for person re-identification.
Prior programming experience in Python and basic PyTorch. Theoretical knowledge of Convolutional Neural Network, Optimization and Siamese Network.
siamese network
Deep Learning
Convolutional Neural Network
pytorch
Computer Vision
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Set up colab runtime
Configurations
Create APN Dataset
Load dataset into batches
Create Model
Create train and eval functions
Training Loop
Get Anchor Encodings
Inference
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by DL
Aug 30, 2022I cannot unenroll this project. Is this normal or will I be charged? I just wanted to explore this and one another guided project.
by MS
Oct 16, 2022Overall, it is good enough.
it will be better if there is accuracy calculation,
thank you!
by SB
Oct 29, 2022A very practical project discovering Siames Network.
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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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