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README.md

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+---
+license: mit
+tags:
+- image-classification
+- pytorch
+- resnet18
+- satellite-imagery
+- remote-sensing
+- eurosat
+datasets:
+- timm/eurosat-rgb
+metrics:
+- accuracy
+pipeline_tag: image-classification
+---
+
+# 🛰️ EuroSAT Satellite Image Classifier
+
+## Model Description
+Fine-tuned **ResNet18** on the **EuroSAT** dataset for satellite image land use classification.
+The model classifies Sentinel-2 satellite imagery (10m resolution) into 10 land use/cover categories.
+
+## Performance
+| Metric | Score |
+|--------|-------|
+| **Test Accuracy** | **98.21%** |
+| Architecture | ResNet18 (ImageNet pretrained) |
+| Training Epochs | 20 |
+| Dataset Size | 27,000 images |
+
+## Classes
+| Class | Description |
+|-------|-------------|
+| 🌾 AnnualCrop | Annual cropland |
+| 🌲 Forest | Dense forest areas |
+| 🌿 HerbaceousVegetation | Natural vegetation |
+| 🛣️ Highway | Road infrastructure |
+| 🏭 Industrial | Industrial zones |
+| 🐄 Pasture | Grazing land |
+| 🌳 PermanentCrop | Orchards, vineyards |
+| 🏘️ Residential | Urban residential areas |
+| 🏞️ River | Rivers and waterways |
+| 🌊 SeaLake | Seas and lakes |
+
+## Usage
+```python
+import torch
+from torchvision import models, transforms
+from PIL import Image
+import torch.nn as nn
+
+checkpoint = torch.load('eurosat_resnet18_finetuned.pth', map_location='cpu')
+model = models.resnet18(weights=None)
+model.fc = nn.Sequential(
+    nn.Dropout(0.4), nn.Linear(512, 256), nn.BatchNorm1d(256),
+    nn.ReLU(), nn.Dropout(0.2), nn.Linear(256, 10)
+)
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+img = Image.open('satellite_image.jpg').convert('RGB')
+with torch.no_grad():
+    pred = model(transform(img).unsqueeze(0))
+    class_id = pred.argmax(1).item()
+    print(f"Predicted: {checkpoint['class_names'][class_id]}")
+```
+
+## Dataset
+[EuroSAT RGB](https://huggingface.co/datasets/timm/eurosat-rgb) - 27,000 labeled Sentinel-2 satellite images.
+
+## Training Details
+- **Base model**: ResNet18 (ImageNet pretrained)
+- **Fine-tuned layers**: layer3 + layer4 + fc head
+- **Optimizer**: AdamW (lr=1e-3, weight_decay=0.01)
+- **Scheduler**: OneCycleLR (max_lr=3e-3)
+- **Augmentation**: RandomFlip, Rotation, ColorJitter, Affine
+- **Label smoothing**: 0.1