Update README.md

README.md

@@ -46,15 +46,17 @@ model-index:
             name: TTA Accuracy
 ---
 
-# Fine-Grained World Architecture Image Classification: A DenseNet121 Transfer Learning Approach with Layered Regularization
+![Arch-Building-Image-Classification](results/greyscope-labs-arch-building-classification-hunggingface-cnn-densenet121.png)
+
+# Fine-Grained Image Classification of World Architecture: A DenseNet121 Transfer Learning Approach with Layered Regularization
 
 ### Architectural Building Image Classifier
 
-Fine-Grained Visual Classification (FGVC) of world architectural buildings using CNN transfer learning with DenseNet121, enhanced with GeM Pooling, Focal Loss, Discriminative AdamW (LR), Stochastic Weight Averaging (SWA), Grad-CAM explainability, and calibration analysis.
+Fine-Grained Image Classification (FGIC) of world architectural buildings using CNN transfer learning with DenseNet121, enhanced with GeM Pooling, Focal Loss, Discriminative AdamW (LR), Stochastic Weight Averaging (SWA), Grad-CAM explainability, and calibration analysis.
 
 <table>
   <tr><td><strong>Architecture</strong></td><td>DenseNet121 + GeM Pooling + Focal Loss + SWA</td></tr>
-  <tr><td><strong>Task</strong></td><td>Fine-Grained Visual Classification (FGVC)</td></tr>
+  <tr><td><strong>Task</strong></td><td>Fine-Grained Image Classification (FGIC)</td></tr>
   <tr><td><strong>Test Accuracy</strong></td><td>96.88%</td></tr>
   <tr><td><strong>Classes</strong></td><td>8 (Barn, Bridge, Castle, Mosque, Skyscraper, Stadium, Temple, Windmill)</td></tr>
   <tr><td><strong>Input Size</strong></td><td>320 × 320 pixels</td></tr>
@@ -69,7 +71,7 @@ A fine-grained image classification model for world architectural buildings. Bui
 
 **Key architectural contributions:**
 
-- **GeM Pooling** (Radenovic et al., CVPR 2018) — replaces global average pooling with a learnable power parameter (p=3.0) that emphasizes high-activation features, yielding stronger discriminative representations for FGVC tasks
+- **GeM Pooling** (Radenovic et al., CVPR 2018) — replaces global average pooling with a learnable power parameter (p=3.0) that emphasizes high-activation features, yielding stronger discriminative representations for FGIC tasks
 - **Focal Loss** (Lin et al., ICCV 2017, gamma=2.0) — down-weights well-classified examples to focus gradient updates on hard-to-classify building pairs
 - **DiscriminativeAdamW** — extends AdamW with per-layer learning rate multipliers: conv4_block receives LR × 0.1 (pretrained features require smaller updates), while conv5_block and the custom head receive LR × 1.0
 - **SWA with BN re-estimation** (Izmailov et al., UAI 2018) — 10-epoch post-training weight averaging with constant LR 1e-4, followed by 100-step batch normalization statistics re-estimation
@@ -192,9 +194,9 @@ Four candidate models were evaluated on the validation set:
 
 ## Training Details
 
-### Training Strategy
+### Progressive Training
 
-Two-phase progressive training with SWA post-processing:
+Two-phase strategy train with SWA post-processing:
 
 | Phase | Description | Backbone | Optimizer | LR | Max Epochs | Actual Epochs | CutMix+Mixup | FocalLoss LS |
 |-------|-------------|----------|-----------|-----|-----------|---------------|---------------|-------------|
@@ -249,7 +251,7 @@ Two-phase progressive training with SWA post-processing:
 
 ### Dataset
 
-[0xgr3y/arch-building-dataset](https://huggingface.co/datasets/0xgr3y/arch-building-dataset) — 13,440 images (8 classes × 1,680, balanced) sourced from Pexels with perceptual (pHash) and exact (SHA256) deduplication.
+See more data studio curation [World Architectural Buildings Dataset for Multi‑Class Image Classification](https://huggingface.co/datasets/0xgr3y/arch-building-dataset) — 13,440 images (8 classes × 1,680, balanced) sourced from Pexels with perceptual (pHash) and exact (SHA256) deduplication.
 
 | Split | Images | Percentage |
 |-------|--------|------------|
@@ -298,7 +300,7 @@ Two-phase progressive training with SWA post-processing:
 
 ### Gradio Space
 
-Try the live inference: [arch-building-classifier Space](https://huggingface.co/spaces/0xgr3y/arch-building-classifier)
+Try the live classify building: [Architecture Building Image Classifier with Space](https://huggingface.co/spaces/0xgr3y/arch-building-classifier)
 
 ### Python — Keras
 
@@ -486,7 +488,7 @@ print(f"Predicted: {LABELS[np.argmax(preds)]} ({np.max(preds)*100:.1f}%)")
 
 - Architectural style classification from building photographs
 - Educational tool for architecture recognition
-- Research baseline for fine-grained visual classification (FGVC)
+- Research baseline for fine-grained image classification (FGIC)
 - Transfer learning experiments on architectural imagery
 
 ## Limitations
@@ -510,8 +512,8 @@ print(f"Predicted: {LABELS[np.argmax(preds)]} ({np.max(preds)*100:.1f}%)")
 ## Links
 
 - **Gradio Space (Live):** [arch-building-classifier Space](https://huggingface.co/spaces/0xgr3y/arch-building-classifier)
-- **Dataset:** [0xgr3y/arch-building-dataset](https://huggingface.co/datasets/0xgr3y/arch-building-dataset)
-- **GitHub:** [arcxteam/arch-building-classifier](https://github.com/arcxteam/arch-building-classifier)
+- **Dataset Studio:** [0xgr3y/arch-building-dataset](https://huggingface.co/datasets/0xgr3y/arch-building-dataset)
+- **GitHub Repo:** [arcxteam/arch-building-classifier](https://github.com/arcxteam/arch-building-classifier)
 
 ## References
 
@@ -542,7 +544,7 @@ print(f"Predicted: {LABELS[np.argmax(preds)]} ({np.max(preds)*100:.1f}%)")
 
 ```bibtex
 @misc{saugani2026_arch_building,
-  title={Fine-Grained World Architecture Image Classification:
+  title={Fine-Grained Image Classification of World Architecture:
          A DenseNet121 Transfer Learning Approach with Layered Regularization},
   author={Saugani},
   year={2026},