Skip to content

Phase 1: pb-lite edge detection using k-means clustering responses from various filterbanks. Phase 2: Backbone network architectures for Image Classifiers from scratch. This was Homework 0 for CMSC733 at University of Maryland, College Park

Notifications You must be signed in to change notification settings

h-gokul/pbLite-DL

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

For Phase 1- PB Lite Edge Detection:

To find edges on image number 7,

python3 Wrapper.py --ImageName 7.jpg

For Phase 2- DeepLearning Architectures,

For the Simple First Neural network, run:

Training:

    python3 Train.py --BasePath ../CIFAR10/Train/ --CheckPointPath ../Checkpoints/BasicModel/ --NumEpochs 25 --DivTrain 1 --MiniBatchSize 64 --LoadCheckPoint 0 --LogsPath Logs/BasicModel/ --ModelName BasicModel 

Testing:

    python3 Test.py --ModelName BasicModel --ModelPath ../Checkpoints/BasicModel/24model.ckpt --BasePath ../CIFAR10/Test/ --LabelsPath ./TxtFiles/LabelsTest.txt 

For the Modified network, run:

Training:

python3 Train.py --BasePath ../CIFAR10/Train/ --CheckPointPath ../Checkpoints/BasicModel2/ --NumEpochs 50 --DivTrain 1 --MiniBatchSize 64 --LoadCheckPoint 0 --LogsPath Logs/BasicModel2/ --ModelName BasicModel2 

Testing:

python3 Test.py --ModelName BasicModel2 --ModelPath ../Checkpoints/BasicModel2/49model.ckpt --BasePath ../CIFAR10/Test/ --LabelsPath ./TxtFiles/LabelsTest.txt  

For Simple ResNet, run:

Training:

    python3 Train.py --BasePath ../CIFAR10/Train/ --CheckPointPath ../Checkpoints/ResNet2/ --NumEpochs 50 --DivTrain 1 --MiniBatchSize 64 --LoadCheckPoint 0 --LogsPath Logs/ResNet2/ --ModelName ResNet2 

Testing:

    python3 Test.py --ModelName ResNet2  --ModelPath ../Checkpoints/ResNet2/49model.ckpt --BasePath ../CIFAR10/Test/ --LabelsPath ./TxtFiles/LabelsTest.txt 

For ResNet pooling version, run:

Training:

   python3 Train.py --BasePath ../CIFAR10/Train/ --CheckPointPath ../Checkpoints/ResNet/ --NumEpochs 50 --DivTrain 1 --MiniBatchSize 64 --LoadCheckPoint 0 --LogsPath Logs/ResNet/ --ModelName ResNet 

Testing:

python3 Test.py --ModelName ResNet --ModelPath ../Checkpoints/ResNet/49model.ckpt --BasePath ../CIFAR10/Test/ --LabelsPath ./TxtFiles/LabelsTest.txt 

For DenseNet, run:

Training:

python3 Train.py --BasePath ../CIFAR10/Train/ --CheckPointPath ../Checkpoints/DenseNet/ --NumEpochs 50 --DivTrain 1 --MiniBatchSize 64 --LoadCheckPoint 0 --LogsPath Logs/DenseNet/ --ModelName DenseNet 

Testing:

python3 Test.py --ModelName DenseNet --ModelPath ../Checkpoints/DenseNet/49model.ckpt --BasePath ../CIFAR10/Test/ --LabelsPath ./TxtFiles/LabelsTest.txt  

For Resnext, run:

Training:

python3 Train.py --BasePath ../CIFAR10/Train/ --CheckPointPath ../Checkpoints/ResNext/ --NumEpochs 25 --DivTrain 1 --MiniBatchSize 64 --LoadCheckPoint 0 --LogsPath Logs/ResNext/ --ModelName ResNext 

Testing:

python3 Test.py --ModelName ResNext --ModelPath ../Checkpoints/ResNext/24model.ckpt --BasePath ../CIFAR10/Test/ --LabelsPath ./TxtFiles/LabelsTest.txt 

About

Phase 1: pb-lite edge detection using k-means clustering responses from various filterbanks. Phase 2: Backbone network architectures for Image Classifiers from scratch. This was Homework 0 for CMSC733 at University of Maryland, College Park

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published