Update README.md

README.md

@@ -2,25 +2,17 @@
 license: apache-2.0
 pipeline_tag: image-classification
 tags:
-  - tensorflow
-  - keras
-  - safetensors
-  - tflite
-  - tensorflow.js
-  - image-classification
   - efficientnetv2
-  - architecture
-  - building
   - fgic
   - transfer-learning
   - gem-pooling
   - focal-loss
-  - discriminative-learning-rate
   - swa
   - grad-cam
   - calibration
-  - roc-auc
   - temperature-scaling
+  - computer-vision
+  - fgvc
 library_name: keras
 language: en
 datasets:
@@ -285,39 +277,15 @@ See the dataset curation page for [World Architectural Buildings Dataset for Mul
 
 ## Files
 
-| File | Description |
-|------|-------------|
-| `fine_tuning_swa.keras` | Best model — SWA averaged weights (226.75 MB) |
-| `fine_tuning_swa.weights.h5` | Best model weights — clean, no ProtectAI flag (158.31 MB) |
-| `fine_tuning_swa.safetensors` | Best model weights — HF standard, cross-framework (157.10 MB) |
-| `build_model.py` | Architecture reconstruction + CLI inference (20.7 KB) |
-| `head_training.keras` | Phase 1 checkpoint |
-| `fine_tuning.keras` | Phase 2 checkpoint |
-| `fine_tuning_ema.keras` | Phase 2 EMA checkpoint |
-| `saved_model/` | TensorFlow SavedModel format (183.29 MB, for TF Serving) |
-| `tflite/model.tflite` | TensorFlow Lite model (88.36 MB, mobile/embedded) |
-| `tflite/label.txt` | Class label names for TF-Lite |
-| `tfjs_model/` | TensorFlow.js model (89.54 MB, 23 shards + model.json) |
-| `config.json` | Model configuration and evaluation metrics |
-| `label_mapping.json` | Class name ↔ ID mapping with training config and architecture info |
-| `preprocessor_config.json` | Input preprocessing specification (320×320) |
-| `confusion_pairs.json` | Auto-detected confusion pairs from confusion matrix (threshold >5%) |
-| `class_confidence_stats.json` | Per-class mean/std/p5/p95 confidence distribution |
-| `model_benchmark.json` | Model parameters, sizes, speed, Top-K, AUC, ECE, TTA metrics |
-| `calibration_data.json` | ECE, bin accuracies/confidences, per-class AUC, Temperature Scaling (T_opt) for calibrated inference |
-| `temperature_config.json` | Temperature Scaling parameters (T, ECE before/after, method) — Guo et al., ICML 2017 |
-| `results/training_curves.png` | Training/validation accuracy and loss curves |
-| `results/confusion_matrix.png` | Confusion matrix on test set |
-| `results/per_class_accuracy.png` | Per-class accuracy bar chart |
-| `results/reliability_diagram.png` | ECE calibration reliability diagram (15 bins) |
-| `results/roc_curves.png` | Per-class ROC curves (One-vs-Rest) |
-| `results/tsne_embedding.png` | t-SNE 2D scatter plot from GeM Pooling features |
-| `results/gradcam_heatmaps.png` | Grad-CAM heatmap visualization per class |
-| `results/confidence_per_class.png` | Per-class confidence distribution bar chart |
-| `results/misclassification_examples.png` | Misclassified test samples |
-| `results/augmentation_examples.png` | Example images after augmentation (training set) |
-| `results/inference_keras.png` | Keras inference grid — 1 sample per class |
-| `results/inference_tflite.png` | TF-Lite inference grid — 1 sample per class |
+| Category | Files |
+|----------|-------|
+| **Model (best)** | `fine_tuning_swa.keras` (227 MB) · `.weights.h5` (158 MB) · `.safetensors` (157 MB) |
+| **Code** | `build_model.py` (21 KB) — architecture + CLI inference |
+| **Config** | `config.json` · `label_mapping.json` · `preprocessor_config.json` |
+| **Evaluation** | `calibration_data.json` · `model_benchmark.json` · `confusion_pairs.json` · `class_confidence_stats.json` · `temperature_config.json` |
+| **Deployment** | `saved_model/` (183 MB) · `tflite/` (88 MB) · `tfjs_model/` (90 MB, 23 shards) |
+| **Results** | `results/` — 12 PNG (training curves, confusion matrix, ROC, t-SNE, Grad-CAM, etc.) |
+| **Archive** | `models_keras/` — 3 checkpoints (head_training, fine_tuning, fine_tuning_ema) |
 
 ## Usage
 
@@ -497,7 +465,7 @@ The `.keras` files in this repository are flagged **"Unsafe"** by [Protect AI Gu
 - **What the scanner checks:** String-matching of `class_name` fields in the Keras v3 config against a whitelist of built-in Keras layers.
 - **Why flagged:** The model contains a custom layer (`GeMPooling`) — a non-standard class name triggers the flag.
 - **What it does NOT check:** The scanner does not analyze the Python code of the custom class, does not look for `eval()`/`exec()`/`os.system()`, and does not detect actual malware.
-- **Other scanners:** VirusTotal ✅, JFrog ✅, HF Picklescan ✅ — all clean. Only Protect AI flags this file.
+- **Other scanners:** VirusTotal, JFrog, HF Picklescan — all clean. Only Protect AI flags this file.
 
 **The custom classes are safe and open source:**
 - `GeMPooling` — Generalized Mean Pooling (Radenovic et al., CVPR 2018). Pure tensor ops: `tf.pow`, `tf.reduce_mean`, `tf.maximum`.
@@ -508,16 +476,16 @@ Full source code for all custom classes is available in [`build_model.py`](https
 
 ## Multi-Format Deployment Guide
 
-This model is provided in multiple formats to suit different deployment scenarios. Formats marked ✅ are **not flagged** by Protect AI (no custom class serialization).
+With model is provided in multiple formats to suit different deployment scenarios. Formats marked ✓ are **not flagged** by Protect AI (no custom class serialization).
 
 | Format | File | Size | Protect AI | Inference Speed | Best For |
 |--------|------|------|------------|-----------------|----------|
-| **TF-Lite** ✅ | `tflite/model.tflite` | ~88 MB | ✅ Safe | **170.0 ms** (fastest) | Mobile, edge, embedded, HF Space |
-| **SavedModel** ✅ | `saved_model/` | ~183 MB | ✅ Safe | — | TensorFlow Serving, cloud backend |
-| **TFJS** ✅ | `tfjs_model/` | ~90 MB | ✅ Safe | — | Browser, Node.js (no backend) |
-| **Weights H5** ✅ | `fine_tuning_swa.weights.h5` | ~158 MB | ✅ Safe | — | Programmatic load via `build_model.py` |
-| **safetensors** ✅ | `fine_tuning_swa.safetensors` | ~157 MB | ✅ Safe | — | HF standard, cross-framework |
-| **Build Script** ✅ | `build_model.py` | ~21 KB | ✅ Safe | — | Architecture reconstruction + `load_weights()` |
+| **TF-Lite** ✓ | `tflite/model.tflite` | ~88 MB | ✓ Safe | **170.0 ms** (fastest) | Mobile, edge, embedded, HF Space |
+| **SavedModel** ✓ | `saved_model/` | ~183 MB | ✓ Safe | — | TensorFlow Serving, cloud backend |
+| **TFJS** ✓ | `tfjs_model/` | ~90 MB | ✓ Safe | — | Browser, Node.js (no backend) |
+| **Weights H5** ✓ | `fine_tuning_swa.weights.h5` | ~158 MB | ✓ Safe | — | Programmatic load via `build_model.py` |
+| **safetensors** ✓ | `fine_tuning_swa.safetensors` | ~157 MB | ✓ Safe | — | HF standard, cross-framework |
+| **Build Script** ✓ | `build_model.py` | ~21 KB | ✓ Safe | — | Architecture reconstruction + `load_weights()` |
 | **Keras** ⚠️ | `fine_tuning_swa.keras` | ~227 MB | ⚠️ Flagged | 358.0 ms | Developer reference, fine-tuning |
 
 ### Load Examples