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.
Full Pytorch Training Pipeline on Image Classification part1
Full Pytorch Training Pipeline on Image Classification part2
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
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 ...
==> 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
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%