On this project, I create a custom dataset of 5 male models and conduct a full Pytorch training pipeline. I use a pretrained model and transfer learning, as well as do hyper-parameter search to help increase the accuracy.
✍️ Analysis and Evaluation:
Full Pytorch Training Pipeline on Image Classification part1
Full Pytorch Training Pipeline on Image Classification part2
📺 My implementations are based off Aladdin Persson and Python Engineer.
My repository contains:
- A training script (using pretrained vgg16 and transfer learning)
- A script for Hyper-parameter Search
- A script for loading the model for either resumed training or inference
- A trained model 😬
- Some helper functions
- A dataset
📰 Dataset Structure
train/val
|___chau_minh_chi
|___chau_minh_chi_01.jpg
|___chau_minh_chi_02.jpg
...
|___keita_machida
|___keita_machida_01.jpg
|___keita_machida_02.jpg
...
Small batch visualization
Large batch visualization
Visualize the number of classes
😑 Hyper-parameter Search
💥 Training Epochs
==> Saving new best
Epoch 1/25
Step 34/34, train Loss = 1.84, train Acc = 0.29
Step 20/20, val loss = 1.58, val acc = 0.25
Time spent for this epoch -----> 0m 32s
==> Saving new best
Epoch 2/25
Step 34/34, train Loss = 0.91, train Acc = 0.63
Step 20/20, val loss = 1.53, val acc = 0.43
Time spent for this epoch -----> 0m 13s
==> Validation accuracy did not improve.
Epoch 3/25
Step 34/34, train Loss = 0.61, train Acc = 0.82
Step 20/20, val loss = 1.67, val acc = 0.27
Time spent for this epoch -----> 0m 8s
😌 Visualize Loss and Accuracy
😅 Accuracy of Each Class
Test Acc
Got 13/30 correct samples over 43.33%
Accuracy of timmy_xu: 33.33%
Accuracy of corbyn_besson: 62.50%
Accuracy of keita_machida: 16.67%
Accuracy of wang_kai: 30.00%
Accuracy of chau_minh_chi: 100.00%
😞 Classification Report and Confusion Matrix Heatmap
😍 Predict a single image
😐 Prediction In Batch
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