efficientnet-b4-flowers102

🌸 EfficientNet-B4 Flower Classifier

A state-of-the-art image classification model for identifying 102 flower species from the Oxford Flowers-102 dataset.

Model Details

Model Description

This model is built on the EfficientNet-B4 backbone with a custom classifier head, trained using a novel 6-Phase Progressive Training strategy. The training progressively increases image resolution (280px → 400px) and augmentation difficulty (None → MixUp → CutMix → Hybrid).

• Developed by: fth2745
• Model type: Image Classification (CNN)
• License: MIT
• Finetuned from: EfficientNet-B4 (ImageNet pretrained)

Performance

Training Details

Training Data

Oxford Flowers-102 dataset with offline data augmentation (tier-based augmentation for class balancing).

Training Procedure

6-Phase Progressive Training

Preprocessing

• Resize → RandomCrop → HorizontalFlip → Rotation (±20°) → Affine → ColorJitter → Normalize (ImageNet)

Training Hyperparameters

• Optimizer: AdamW
• Learning Rate: 1e-3
• Weight Decay: 1e-4
• Scheduler: CosineAnnealingWarmRestarts (T_0=5, T_mult=2)
• Loss: CrossEntropyLoss (label_smoothing=0.1)
• Batch Size: 8
• Training Regime: fp16 mixed precision (AMP)

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🎯 6-Phase Progressive Training

Phase 1 ──→ Phase 2 ──→ Phase 3 ──→ Phase 4 ──→ Phase 5 ──→ Phase 6
 280px      320px       320px       380px       380px       400px
  None      MixUp       MixUp      CutMix      CutMix      Hybrid
           α=0.2       α=0.4       α=0.2       α=0.5      MixUp+Cut

Phase Details

💡 Why Progressive Training? Starting with low resolution helps the model learn general shapes first. Gradual augmentation increase builds robustness incrementally.

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🖼️ Preprocessing Pipeline (All Phases)

⚠️ Note: These preprocessing steps are applied in ALL PHASES. Only img_size changes per phase.

Complete Training Flow

┌─────────────────────────────────────────────────────────────┐
│              📷 RAW IMAGE INPUT                             │
└─────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────┐
│     🔄 STEP 1: IMAGE-LEVEL PREPROCESSING (Per image)        │
├─────────────────────────────────────────────────────────────┤
│  1️⃣ Resize         │ (img_size + 32) × (img_size + 32)     │
│  2️⃣ RandomCrop     │ img_size × img_size                   │
│  3️⃣ HorizontalFlip │ p=0.5                                 │
│  4️⃣ RandomRotation │ ±20°                                  │
│  5️⃣ RandomAffine   │ scale=(0.8, 1.2)                      │
│  6️⃣ ColorJitter    │ brightness, contrast, saturation=0.2  │
│  7️⃣ ToTensor       │ [0-255] → [0.0-1.0]                   │
│  8️⃣ Normalize      │ ImageNet mean/std                     │
└─────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────┐
│        🎲 STEP 2: BATCH-LEVEL AUGMENTATION (Phase-specific) │
├─────────────────────────────────────────────────────────────┤
│  Phase 1: None (Preprocessing only)                         │
│  Phase 2-3: MixUp (λ×ImageA + (1-λ)×ImageB)                 │
│  Phase 4-5: CutMix (Patch swap between images)              │
│  Phase 6: Hybrid (MixUp + CutMix combined)                  │
└─────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────┐
│              🎯 READY FOR MODEL TRAINING                    │
└─────────────────────────────────────────────────────────────┘

Phase-Specific Image Sizes

Preprocessing Details (All Phases)

Test/Validation Preprocessing

💡 Key Insight: Preprocessing (8 steps) is applied per image in every phase. MixUp/CutMix is applied AFTER preprocessing as batch-level augmentation.

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🔀 Batch-Level Augmentation Techniques (Phase-Specific)

MixUp

Image A (Rose) + Image B (Sunflower) 
    ↓
λ = Beta(α, α)  →  New Image = λ×A + (1-λ)×B
    ↓
Blended Image (70% Rose + 30% Sunflower features)

Benefits: ✅ Smoother decision boundaries ✅ Reduces overconfidence ✅ Better generalization

CutMix

Image A (Rose) + Random BBox from Image B (Sunflower)
    ↓
Paste B's region onto A
    ↓
Composite Image (Rose background + Sunflower patch)

Benefits: ✅ Object completion ability ✅ Occlusion robustness ✅ Localization skills

Hybrid (Grand Finale)

1. Apply MixUp (blend two images)
2. Apply CutMix (cut on blended image)
3. Result: Maximum augmentation challenge

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🛡️ Smart Training Features

Two-Layer Early Stopping

Smart Dropout Mechanism

Bounds: min=0.10, max=0.50

Model Architecture

EfficientNet-B4 (pretrained)
    └── Custom Classifier Head
        ├── BatchNorm1d (1792)
        ├── Dropout
        ├── Linear (1792 → 512)
        ├── GELU
        ├── BatchNorm1d (512)
        ├── Dropout
        └── Linear (512 → 102)

Total Parameters: ~19M (all trainable)

Supported Flower Classes

102 flower species including: Rose, Sunflower, Tulip, Orchid, Lily, Daisy, Hibiscus, Lotus, Magnolia, and 93 more.

Limitations

• Trained only on Oxford Flowers-102 dataset
• Best performance at 400×400 resolution
• May not generalize well to flowers outside the 102 trained classes

Citation

@misc{efficientnet-b4-flowers102,
  title={EfficientNet-B4 Flower Classifier with 6-Phase Progressive Training},
  author={fth2745},
  year={2024},
  url={https://huggingface.co/fth2745/efficientnet-b4-flowers102}
}