- Simple reconstructive AutoEncoder
- Reconstructive AutoEncoder for denoising demo
- Reconstructive AutoEncoder for Anomaly Detection score
- train.py
- finetune.py
- test.py
- stored_models
- 2 trained models with loss plots
- [21-02-2022] model trained on kaggle leather dataset
- test.py - CHECK IF TESTED??
- UNTESTED
Description:
- class6.py - Modified ResNet-50 model from 100 classes to 6 classes, corresponding to the 6 Kaggle leather dataset catagories
- class6a.py - Above code split and modified for direct testing/classification + Training evaluation
- class6b.py - Custom model created & tested with stored pre-processed dataset + test prediction?
Description:
- class_4.py
- class_5.py
- class_10.py
- custom1.py
- custom2.py
- inception.py
- resnet50.py
- vgg16.py
Description: Various ViT code examples tested up to classification (at least)
- ViT_0 - UNTESTED
- ViT_1
- Classifier tested in Locally & in Colab
- ViT_1a.py - Original Vision Transformer example from Colab (link in code description)
- ViT_1b.py - Removed visualisations and other superfluous code
- ilsvrc2012... - ImageNet Labels for class prediction
- ViT_2 - Classifier tested in Colab
- ViT_3
- Tested in Colab - Not with custom test image (only Cifar-10)
- ViT_4
- Good test metrics at the end (Activation Map / Confusion Matrix)
- Testing incomplete (Colab / Custom Image)
To update GIT:
git push https://github.com/ANTZ314/d_learning.git --force