Course UE D-Deep Learning at IMT Atlantique
Made by:
- David HURTADO @dr-hurtado
- Zijie NING @mm0806son
Thesis Reproduction and application: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Main Activities:
- Read the paper to understand the mathematical principles and network structure.
- Read the code and understand the main functions.
- Development of new applications based on experimental conditions.
Our own applications:
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Processing of single images
- Body joint confidence map
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Part Affinity Fields (PAFs)
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Generated body skeleton
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Processing video
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Use Google Colab to call a local webcam and process the images
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Layer visualization to see what happened in the network
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For example, after the preprocessing network (here model0, first 10 layers of vgg19)
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3rd stage of PAFs branch (model3_2, first 20 output)
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3rd stage of confidence map branch (model3_2)
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Use Google Colab to call a local webcam and process the video (TODO)
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Control of a robot simulator using
pybullet(TODO)
2021/10/01 & 08
Introduction to PyTorch
- Linear regression
- Logistique regression
2021/10/15
Multi-Layer Perception with MNIST dataset
- Build a 2-layer Neuro network
- Evolution of the loss function for both training and validation sets
2021/10/15
Convolutional Neural Networks (CNN) on MNIST
- Create data loaders
- Build and train a CNN network
2021/10/29 Graded session
Fashion-MNIST dataset
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Data loader
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MLP
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CNN
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Transfer learning with vgg-16
Accuracy: 93.71% (1st in the class ✌️)
Pretrained VGG16 with lr = 0.01, epoch = 7, Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
2021/11/12 Graded session
Recurrent Neural Networks (RNN) on the the Lorenz-63 system (chaos system)