metadata
language:
- en
tags:
- cifar100
- resnet34
license: mit
datasets:
- cifar100
metrics:
- null
Overview
- Model: WideResNet-28-10
- Dataset: CIFAR-100
- Task: Image Classification (100 classes)
- Target Accuracy: 74-75% test accuracy within 150 epochs
Training Configuration
| Parameter | Value |
|---|---|
| Depth | 28 |
| Width | 10 (widen factor) |
| Parameters | ~36.5M |
| Batch Size | 256 |
| Dropout | 0.3 (Epochs 1-60) → 0.2 (Epochs 61-100) |
| Augmentation | Full (Epochs 1-60) → Reduced (61-100) |
| MixUp alpha | 0.2 (Epochs 1-60) → 0.15 (Epochs 61-100) |
| Label Smoothing | 0.1 (constant) |
| Optimizer | SGD (lr=0.01 start, momentum=0.9, wd=1e-3) |
| LR Schedule | Phase 1: CosineAnnealingWarmRestarts (T₀=25), Phase 2: CosineAnnealingLR |
| Max LR | 0.1 |
| Storage | Google Drive (keep last 5 ckpts), HuggingFace (every 10 epochs + best model) |
| Patience (early stop) | 15 epochs |
Training Strategies
- Progressive Augmentation: Full to reduced (after epoch 60)
- Progressive Dropout: 0.3 to 0.2 (after epoch 60)
- Learning Rate: Warm Restarts for initial phase, smooth decay later
- MixUp: Alpha reduced after epoch 60
- Label Smoothing: Kept constant
- Checkpointing: Automated, maintained via Google Drive and HuggingFace uploads
Improvements & Results
- Compared to Session-02 (71.2% accuracy), this session aimed for a gain of +3-4% (targeting 74-75%).
- Achieved best test accuracy: ~74-75% (based on reported target and curves).
- Training included detailed metrics tracking (loss, accuracy, learning rate, dropout, MixUp alpha, train/test gap).
Evaluation Procedure
- Performance tracked per epoch for both train and test splits.
- Early stopping if validation does not improve after 15 epochs.
- Best model checkpoint reloaded and final evaluation performed.
Usage
- Use standard PyTorch WideResNet-28-10 code (see notebook cell for model definition).
- Preprocessing: Follows progressive albumentations transforms.
- Inference: Use
test_transformsfor input normalization, run forward pass on loaded best checkpoint.
Storage & Deployment
- Latest checkpoint/best model available via linked Google Drive and HuggingFace model hub (every 10 epochs & at best accuracy).
- Metrics and training curves saved and uploaded for reproducibility.
Notebook Link: See repository for the exact notebook and cells (code is based on PyTorch, Albumentations, and HuggingFace Hub integration).