README.md

Flowers102-Resnet50

FellowshipAi project

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The Notebook has everything from downloading dataset to fine tuning the model and inference.

Preprocessing

• download.py
• data_processing.py

Dataset

• Flowers102

Augmentation

• RandomResizedCrop
• RandomHorizontalFlip
• RandomVerticalFlip
• randomRotation
• CenterCrop

Trnasformations

• resize = 224
• ToTensor
• Normalize
  • mean = [0.485, 0.456, 0.406]
  • std = [0.229, 0.224, 0.225]

Model

• model.py

Architecture

• Resnet50 - backbone
• MlpHead - head
  • 3 layers
    • 2048 -> 512 -> 102 hidden units
    • ReLU activation
    • Dropout 0.5
    • BatchNorm

Train

• CrossEntropyLoss
• Adam optimizer
  • 0.01 learning rate
• 30 epochs

Fine-Tuning

• CrossEntropyLoss
• Radam optimizer
  • 3e-4 learning rate
• Cyclical Learning Rates
  • min_lr = 5e-8
  • max_lr = 3e-3
  • mode = 'triangular'
• 120 epochs

Results

1. EDA

Classest Distribution

• Class imbalance is present in the dataset

Sample Images

Training History

Highest Accuracy

• Test-Set

Experiment	Accuracy	Loss
Resnet50 - 30 Epochs	85.0838%	0.549521
Resnet50 - 120 Epochs	89.2956%	0.386665

• Validation-Set

Experiment	Accuracy	Loss
Resnet50 - 30 Epochs	87.2511%	0.451927
Resnet50 - 120 Epochs	91.0849%	0.312610

• Train-Set

Experiment	Accuracy	Loss
Resnet50 - 30 Epochs	88.8839%	0.389938
Resnet50 - 120 Epochs	96.7953%	0.105484

Graphs

LOSS

• 30 epochs
• 120 epochs (fine tuning)

ACCURACY

• 30 epochs
• 120 epochs (fine tuning)

GradCam

Train Sample

• grad camp for 1 image & top 4 classes

• CAM for all classes

Val Sample

• idx 75
• grad camp for 1 image & top 4 classes

• CAM for all classes

Test Sample

• idx 75
• grad camp for 1 image & top 4 classes

• CAM for all classes

Workflow