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README.md

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+# ImageNet 2012 Dataset Backup
+
+Complete backup of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 dataset.
+
+## Download & Extract
+
+```bash
+# huggingface_hub 설치 (필요시)
+pip install huggingface_hub
+
+# 다운로드
+huggingface-cli download leekwoon/imagenet_dataset_backup --repo-type dataset --local-dir ./imagenet_data
+
+# 무결성 확인 (선택사항)
+cd imagenet_data
+md5sum -c checksums.md5
+
+# 파일 합치기 및 압축 해제
+cat data.tar.gz.part_* | tar -xzvf -
+```
+
+## Dataset Information
+
+### Overview
+ImageNet is a large-scale hierarchical image database that has been instrumental in advancing deep learning and computer vision research. The ILSVRC 2012 subset is the most commonly used benchmark for image classification tasks.
+
+### Statistics
+- **Training Images**: 1,281,167
+- **Validation Images**: 50,000
+- **Test Images**: 100,000
+- **Number of Classes**: 1,000
+- **Image Format**: JPEG
+- **Average Resolution**: ~469x387 pixels
+
+## Directory Structure
+
+```
+imagenet2012/
+├── train/                  # Training images
+│   ├── n01440764/         # Class folder (tench)
+│   ├── n01443537/         # Class folder (goldfish)
+│   ├── n01484850/         # Class folder (great white shark)
+│   └── ...                # 997 more class folders
+├── val/                    # Validation images
+│   ├── n01440764/
+│   ├── n01443537/
+│   ├── n01484850/
+│   └── ...
+└── test/                   # Test images (if available)
+    └── ...
+```
+
+## Class Information
+
+The dataset contains 1,000 object classes from the WordNet hierarchy, including:
+- Animals (mammals, birds, fish, reptiles, etc.)
+- Plants (trees, flowers, fruits, vegetables)
+- Objects (vehicles, furniture, tools, instruments)
+- Scenes and structures
+
+Each class is identified by a WordNet ID (synset), such as:
+- n01440764: tench (a type of fish)
+- n02119789: kit fox
+- n07734744: mushroom
+- n04515003: upright piano
+
+## Usage
+
+### Loading with PyTorch
+```python
+from torchvision import datasets, transforms
+
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                       std=[0.229, 0.224, 0.225]),
+])
+
+# Load training data
+train_dataset = datasets.ImageFolder('imagenet2012/train', transform=transform)
+
+# Load validation data
+val_dataset = datasets.ImageFolder('imagenet2012/val', transform=transform)
+```
+
+### Loading with TensorFlow
+```python
+import tensorflow as tf
+
+def preprocess_image(image):
+    image = tf.image.resize(image, [224, 224])
+    image = tf.keras.applications.imagenet_utils.preprocess_input(image)
+    return image
+
+# Load dataset
+train_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    'imagenet2012/train',
+    image_size=(224, 224),
+    batch_size=32
+)
+```
+
+## Important Notes
+
+- This dataset is for research purposes only
+- The original ImageNet dataset requires accepting the terms of use
+- Some images may be missing due to broken URLs in the original dataset
+- Class labels follow the ILSVRC 2012 convention
+
+## Citation
+
+If you use this dataset, please cite the original ImageNet paper:
+
+```bibtex
+@article{deng2009imagenet,
+  title={ImageNet: A large-scale hierarchical image database},
+  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
+  journal={2009 IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={248--255},
+  year={2009},
+  organization={IEEE}
+}
+```
+
+## License
+
+The annotations in this dataset are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). The images have various licenses that should be checked on the original ImageNet website.