This project explores how compressed and degraded dermatology images (from the ISIC 2019 dataset) affect classification performance using pretrained vision models. It compares fine-tuning vs. linear probing across multiple JPEG quality levels.
- Evaluate model robustness to image compression (JPEG 90/50/20)
- Compare pretrained models: ViT, DINOv2, and SimCLR
- Benchmark fine-tuning vs. linear probing
- Analyze FLOPs, GPU memory, and classification accuracy
ViT: Vision Transformer from Hugging FaceDINOv2: Self-supervised ViT from MetaSimCLR: Contrastive ResNet50 trained with linear classifier
- Accuracy, F1 Score, AUC
- FLOPs (GFLOPs)
- GPU memory usage
- Training and evaluation time
CS231N/
├── configs/
│ └── example_config.yaml # Configs for job submissions
│
├── scripts/ # Lightweight utility or shell scripts
│ ├── download_unpack_isic2019.sh # Downloads and unpacks ISIC data
│ └── submit_from_config.sh # SLURM submission helper
│
├── jobs/ # SLURM-related job definitions
│ └── job_template.slurm
│
├── src/ # Source code, logically grouped
│ ├── __init__.py
│ ├── finetune/ # Fine-tuning workflows
│ │ └── baseline_finetuning.py
│ ├── evaluation/ # Evaluation + plotting
│ │ └── evaluate_isic_results.py
│ └── models/ # Model-related scripts
│ ├── model_comparison.py # Config file with constant strings
│ ├── model_comparison.py
│ └── model_comparison_2.py
│
├── results/ # Auto-generated results
│ ├── plots/ # Accuracy/f1/AUC plots
│ └── logs/ # Training logs or SLURM outputs
│
├── requirements.txt
├── .gitignore
├── .github
└── README.md
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Install requirements:
pip install -r requirements.txt
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Run training:
python train_models.py
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View results We use weights and biases for logging, so output plots can be seen there