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README.md

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+---
+license: apache-2.0
+tags:
+  - image-classification
+  - x-ray
+  - customs
+  - smuggling-detection
+  - sonar
+language:
+  - ar
+  - en
+---
+
+# 🔱 SONAR AI - Customs X-Ray Inspection Models
+
+## Dr. Abbas Fadel Jassim | 2026
+
+> AI-powered customs inspection system for X-ray cargo scanning
+
+---
+
+## 📊 Models Performance Summary
+
+| # | Model | Task | Classes | F1 Score | Architecture |
+|---|-------|------|---------|----------|-------------|
+| 1 | **Classification** | تصنيف البضاعة | 43 | **94.2%** | Swin-V2 Tiny |
+| 2 | **Concealment** | كشف الإخفاء | 2 | **98.4%** | Swin-V2 Tiny |
+| 3 | **Risk Assessment** | تقييم الخطورة | 5 | **97.3%** | DaViT Tiny |
+
+---
+
+## 🔍 Model 1: Classification (43 cargo categories)
+
+**Task:** Classify X-ray images into 43 different cargo types
+
+| Model | Params | Accuracy | F1 Score |
+|-------|--------|----------|----------|
+| 🏆 **Swin-V2 Tiny** | 27.6M | 94.2% | **94.1%** |
+| 🥈 **DaViT Tiny** | 27.6M | 94.2% | **94.1%** |
+
+### Per-Class Performance (Swin-V2):
+
+| Category | Precision | Recall | F1 | Samples |
+|----------|-----------|--------|-----|---------|
+| appliances | 98.2% | 98.2% | **98.2%** | 57 |
+| auto_parts | 92.6% | 94.3% | **93.5%** | 53 |
+| bags | 100% | 100% | **100%** | 12 |
+| banana | 100% | 100% | **100%** | 40 |
+| batteries | 100% | 88.9% | **94.1%** | 9 |
+| beverages | 71.4% | 100% | **83.3%** | 10 |
+| cables | 100% | 94.4% | **97.1%** | 18 |
+| canned_food | 92.0% | 79.3% | **85.2%** | 29 |
+| ceramic | 97.5% | 97.5% | **97.5%** | 40 |
+| chemicals | 100% | 94.9% | **97.4%** | 39 |
+| cleaning | 91.7% | 91.7% | **91.7%** | 24 |
+| clothes | 91.8% | 96.3% | **94.0%** | 81 |
+| cooking_oil | 76.9% | 100% | **87.0%** | 10 |
+| cosmetics | 100% | 94.1% | **97.0%** | 17 |
+| electronics | 92.7% | 87.9% | **90.3%** | 58 |
+| fruits | 85.7% | 100% | **92.3%** | 6 |
+| furniture | 97.1% | 96.6% | **96.8%** | 174 |
+| glass | 95.2% | 100% | **97.6%** | 20 |
+| kitchenware | 100% | 100% | **100%** | 2 |
+| lubricants | 92.3% | 100% | **96.0%** | 12 |
+| machinery | 87.5% | 95.5% | **91.3%** | 66 |
+| meat | 92.3% | 92.3% | **92.3%** | 13 |
+| medical | 100% | 100% | **100%** | 3 |
+| milk | 98.4% | 98.4% | **98.4%** | 62 |
+| motorcycle | 100% | 66.7% | **80.0%** | 6 |
+| nuts | 92.9% | 100% | **96.3%** | 26 |
+
+**Training:** 30 epochs, AdamW (lr=1e-4), CosineAnnealing, Albumentations augmentation
+
+---
+
+## 🔍 Model 2: Concealment Detection (match vs no_match)
+
+**Task:** Detect hidden/smuggled items in X-ray containers
+
+| Model | Params | Accuracy | F1 Score |
+|-------|--------|----------|----------|
+| 🏆 **Swin-V2 Tiny** | 27.6M | 98.4% | **98.4%** |
+| 🥈 **DaViT Tiny** | 27.6M | 98.4% | **98.4%** |
+| 🥉 **EVA-02 Tiny** | 5.5M | 97.8% | **97.8%** |
+| 4️⃣ **MaxViT Tiny** | 30.4M | 96.7% | **96.8%** |
+
+### Classes:
+- **match:** Container contents match declaration (6,348 images)
+- **no_match:** Concealed/smuggled items detected (702 images)
+
+---
+
+## ⚠️ Model 3: Risk Assessment (5 levels)
+
+**Task:** Assess risk level of container cargo
+
+| Model | Params | Accuracy | F1 Score |
+|-------|--------|----------|----------|
+| 🏆 **DaViT Tiny** | 27.6M | 97.2% | **97.3%** |
+| 🥈 **Swin-V2 Tiny** | 27.6M | 97.2% | **97.2%** |
+
+### Risk Levels:
+
+| Level | Name (AR) | Name (EN) | Samples | F1 Score |
+|-------|-----------|-----------|---------|----------|
+| 0 | آمن | Safe | 6,355 | **99.1%** |
+| 1-2 | منخفض | Low | 5 | ⚠️ Limited |
+| 3 | متوسط | Medium | 387 | **79.5%** |
+| 4 | عالي | High | 297 | **84.5%** |
+| 5 | حرج | Critical | 6 | ⚠️ Limited |
+
+**Note:** Low and Critical levels have very few training samples.
+
+---
+
+## 📁 Files
+```
+SONAR-AI-Models/
+├── concealment/
+│   ├── best_swinv2.pth      (110 MB, 98.4% F1)
+│   ├── best_davit.pth       (110 MB, 98.4% F1)
+│   ├── best_eva02.pth       (22 MB, 97.8% F1)
+│   └── best_maxvit.pth      (122 MB, 96.8% F1)
+├── classification/
+│   ├── best_swinv2_43cls.pth (110 MB, 94.1% F1)
+│   └── best_davit_43cls.pth  (110 MB, 94.1% F1)
+├── risk/
+│   ├── best_swinv2_risk.pth  (110 MB, 97.2% F1)
+│   └── best_davit_risk.pth   (110 MB, 97.3% F1)
+└── results/
+    ├── classification_cm.png
+    ├── classification_training.png
+    ├── risk_cm.png
+    └── risk_training.png
+```
+
+## 🛠️ Usage
+```python
+import timm, torch
+from torchvision import transforms
+from PIL import Image
+
+# Load model
+model = timm.create_model('swinv2_tiny_window8_256', pretrained=False, num_classes=43)
+ckpt = torch.load('classification/best_swinv2_43cls.pth', map_location='cpu')
+model.load_state_dict(ckpt['model_state_dict'])
+model.eval()
+
+# Inference
+tf = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225])
+])
+img = tf(Image.open('xray.jpg').convert('RGB')).unsqueeze(0)
+with torch.no_grad():
+    pred = model(img).argmax(1).item()
+```
+
+## 📜 License
+Apache 2.0
+
+## 👨‍💻 Author
+**Dr. Abbas Fadel Jassim** - 2026