Descriptor Convolutional Neural Network
src:
| config.zip .json file with all user configurations accessed by training.ipynb file
| inference.ipynb Script to produce some inferences
| training.ipynb Main script to build and training models
| learning_rate_range_test.ipynb Script to test Learning Rate Range
| lr_schedulers.zip Learning Rate Schedulears
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+---Datasets Folder to stores all datasets used in this project
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+---Models Folder to stores all models build in this project
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+---Outputs Folder to stores all outputs/results from models build in this project
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+---Pretrained Folder to stores checkpoints from models build in this project
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---utilities.zip Utility Library
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+---dataset Module to manipulate datasets
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+---descriptors Module to stores all function related with descriptors manipulation
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+---global_variables Module to stores all global variables used in this project
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+---local_feature_detection Module to stores the Local Feature Detection (LFD) Layer
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+---local_feature_reduction Module to stores the Local Descriptor Convolution (LDC) Layer
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+---logfile Module to stores all function related with logfile
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+---reducers Module to stores all function related with reducers manipulation
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+---residual_neural_network Module to stores ResNet model
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+---tensorflow_functions Module to stores all function related with TensorFlow
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+---timer Python Timer Class
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+---visualizations Module to stores all function related with visualizations and save figures
- Configure the parameters in config.json
- Train the model using python training.ipynb
- Evaluate the model on test dataset or custom dataset using: inference.ipynb
⬜ Implement Dictionary Learning in scikit-cuda
⬜ Rephrase use of use_desc_block parameter
⬜ Solve MNIST confusion matrix plot problem
⬜ PEP8 and PEP257 Conventions
⬜ Generate requirements.txt file
⬜ Plot ROC-AUC
⬜ Plot Filters
⬜ Plot Feature Maps
⬜ Add RMSE metric to custom objetcs to allow model retraining