Update README.md

README.md

@@ -69,10 +69,57 @@ Annotations were manually created using [COCO Annotator](https://github.com/jsbr
 
 ## Usage Example
 
+### (Recommended) Full Download — COCO Format Ready
+
+```bash
+# Clone the dataset repo (includes CSV + annotations.zip + dataset.py)
+git lfs install
+git clone https://huggingface.co/datasets/etri/ForestPersons
+cd ForestPersons
+
+# Download and extract all images (already included in the repo)
+# Structure: images/{folder}/{image}.jpg
+
+# Unzip COCO-style annotations
+unzip annotations.zip -d annotations/
+
+# Resulting directory:
+# ├── images/
+# ├── annotations/
+# │   ├── train.json
+# │   ├── val.json
+# │   └── test.json
+
+```
+
+### Visualize One Sample
 ```python
-from datasets import load_dataset
+import requests
+from PIL import Image
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from io import BytesIO
+
+# Hugging Face image base path
+BASE_URL = "https://huggingface.co/datasets/etri/ForestPersons/resolve/main/images/"
+
+# Load sample
+sample = dataset[0]
+image_url = BASE_URL + sample["file_name"]
+
+# Load image
+image = Image.open(BytesIO(requests.get(image_url).content))
+
+# Draw image + bounding box
+fig, ax = plt.subplots()
+ax.imshow(image)
+x, y, w, h = sample["bbox_x"], sample["bbox_y"], sample["bbox_w"], sample["bbox_h"]
+rect = patches.Rectangle((x, y), w, h, linewidth=2, edgecolor='red', facecolor='none')
+ax.add_patch(rect)
+plt.title(f"{sample['file_name']} | {sample['category_name']}")
+plt.axis("off")
+plt.show()
 
-dataset = load_dataset("etri/ForestPersons")
 ```
 
 ## License