Update README.md

README.md

@@ -42,17 +42,73 @@ PM25Vision (PM25V) is a large-scale dataset for estimating air quality (PM2.5) f
 | ViT-B/16        | 0.40 | 0.37 | 0.41      | 0.36   |
 | EfficientNet-B0 | 0.40 | 0.34 | 0.42      | 0.33   |
 
-## Dataset Structure
+## Usage
+
+### Quick Start
+
+```python
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+import torchvision.transforms as T
+from PIL import Image
+from io import BytesIO
+
+# ===== Load dataset =====
+ds = load_dataset("DeadCardassian/PM25Vision")
+
+transform = T.Compose([
+    T.Resize((224, 224)),
+    T.ToTensor(),
+])
+
+def collate_fn(batch):
+    imgs = [transform(Image.open(BytesIO(x["image"])).convert("RGB")) for x in batch]
+    labels = [x["pm25"] for x in batch]   # pm25 AQI value
+    return torch.stack(imgs), torch.tensor(labels, dtype=torch.float32)
+
+train_loader = DataLoader(ds["train"], batch_size=32, shuffle=True, collate_fn=collate_fn)
+
+# ===== Simple CNN =====
+class SimpleCNN(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
+            nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2),
+            nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.AdaptiveAvgPool2d(1),
+        )
+        self.fc = nn.Linear(64, 1)  # regression
+
+    def forward(self, x):
+        x = self.net(x)
+        x = x.view(x.size(0), -1)
+        return self.fc(x).squeeze(1)
+
+# ===== Training loop =====
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = SimpleCNN().to(device)
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+criterion = nn.MSELoss()
+
+for epoch in range(5):  # 5 epoch for demo
+    for imgs, labels in train_loader:
+        imgs, labels = imgs.to(device), labels.to(device)
+
+        optimizer.zero_grad()
+        outputs = model(imgs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+    print(f"Epoch {epoch+1}: train loss = {loss.item():.4f}")
+```
+
+
+### Label Fields
 
-The dataset is organized into two main splits: **train** and **test**, each containing:
-
-- **`images/`**: all image files used in the dataset.  
-- **`samples_by_bin/`**: a small set of 30 example images per AQI bin (for quick visual inspection).  
-- **`metadata.csv`**: a CSV file describing metadata (including pm2.5 labels) for each image.  
-
-### Metadata Fields
-
-Each row in `metadata.csv` contains:
 
 | Field          | Type    | Description                                                          |
 |----------------|---------|----------------------------------------------------------------------|
@@ -69,6 +125,8 @@ Each row in `metadata.csv` contains:
 | `quality`      | object  | ResNet18 classified label for image quality (e.g., `good` or `bad`). |
 | `pm25_bin`     | object  | Discrete AQI level label (e.g., `0–50`, `51–100`, etc.).             |
 
+**Only `image_id` and `pm25` will be used most of the time.**
+
 ### Splits
 
 - **Train**: 80% of samples, balanced across AQI bins.  
@@ -81,16 +139,19 @@ Each row in `metadata.csv` contains:
 - Rare extreme AQI classes remain underrepresented.
 
 ## Access
-- Arxiv: ...
+- Arxiv: [PM25Vision](https://arxiv.org/abs/2509.16519)
 - Online demo: [pm25vision.com](http://www.pm25vision.com)
+- Kaggle (Download the entire data folder in a zip file, suitable for expansion needs): [PM25Vision](https://www.kaggle.com/datasets/DeadCardassian/pm25vision)
 
 ## Citation
 ```bibtex
-@misc{pm25vision2025,
-  title        = {PM25Vision: Street-level imagery with PM2.5 annotations},
-  author       = {Han, Yang},
-  year         = {2025},
-  publisher    = {Hugging Face Datasets},
-  url          = {https://huggingface.co/datasets/DeadCardassian/PM25Vision}
+@misc{han2025pm25visionlargescalebenchmarkdataset,
+      title={PM25Vision: A Large-Scale Benchmark Dataset for Visual Estimation of Air Quality}, 
+      author={Yang Han},
+      year={2025},
+      eprint={2509.16519},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2509.16519}, 
 }
 ```