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README.md

@@ -1,80 +1,165 @@
 ---
 dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': tench
-          '1': goldfish
-          '2': great white shark
-          '3': tiger shark
-          '4': hammerhead
-          '5': electric ray
-          '6': stingray
-          '7': cock
-          '8': hen
-          '9': ostrich
-          '10': brambling
-          '11': goldfinch
-          '12': house finch
-          '13': junco
-          '14': indigo bunting
-          '15': robin
-          '16': bulbul
-          '17': jay
-          '18': magpie
-          '19': chickadee
-          '20': water ouzel
-          '21': kite
-          '22': bald eagle
-          '23': vulture
-          '24': great grey owl
-          '25': European fire salamander
-          '26': common newt
-          '27': eft
-          '28': spotted salamander
-          '29': axolotl
-          '30': bullfrog
-          '31': tree frog
-          '32': tailed frog
-          '33': loggerhead
-          '34': leatherback turtle
-          '35': mud turtle
-          '36': terrapin
-          '37': box turtle
-          '38': banded gecko
-          '39': common iguana
-          '40': American chameleon
-          '41': whiptail
-          '42': agama
-          '43': frilled lizard
-          '44': alligator lizard
-          '45': Gila monster
-          '46': green lizard
-          '47': African chameleon
-          '48': Komodo dragon
-          '49': African crocodile
-  - name: wnid
-    dtype: string
-  - name: class_name
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 5311045365.0
-    num_examples: 45000
-  - name: validation
-    num_bytes: 610425193.0
-    num_examples: 5000
-  download_size: 6271612635
-  dataset_size: 5921470558.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
+  dataset_name: imagenet-50-subset
+  dataset_size: 50 classes, 50000 images
+task_categories:
+- image-classification
+language:
+- en
+size_categories:
+- 10K<n<100K
 ---
+
+# ImageNet-50 Subset
+
+This dataset contains the first 50 classes from ImageNet-1K with up to 1,000 images per class (where available).
+
+## Dataset Statistics
+
+- **Total Classes**: 50
+- **Total Images**: 50000
+- **Train/Val Split**: 90%/10%
+- **Max Images per Class**: 1000
+
+## Dataset Structure
+
+```
+imagenet-50-subset/
+├── train/
+│   ├── n01440764/  # tench
+│   │   ├── n01440764_1234.JPEG
+│   │   └── ...
+│   ├── n01443537/  # goldfish
+│   └── ...
+├── val/
+│   ├── n01440764/
+│   ├── n01443537/
+│   └── ...
+├── metadata.json
+├── wnid_to_class.txt
+└── README.md
+```
+
+## Classes Included
+
+| WordNet ID | Class Name | Train Images | Val Images | Total |
+|------------|------------|--------------|------------|-------|
+| n01440764 | tench | 900 | 100 | 1000 |
+| n01443537 | goldfish | 900 | 100 | 1000 |
+| n01484850 | great white shark | 900 | 100 | 1000 |
+| n01491361 | tiger shark | 900 | 100 | 1000 |
+| n01494475 | hammerhead | 900 | 100 | 1000 |
+| n01496331 | electric ray | 900 | 100 | 1000 |
+| n01498041 | stingray | 900 | 100 | 1000 |
+| n01514668 | cock | 900 | 100 | 1000 |
+| n01514859 | hen | 900 | 100 | 1000 |
+| n01518878 | ostrich | 900 | 100 | 1000 |
+| n01530575 | brambling | 900 | 100 | 1000 |
+| n01531178 | goldfinch | 900 | 100 | 1000 |
+| n01532829 | house finch | 900 | 100 | 1000 |
+| n01534433 | junco | 900 | 100 | 1000 |
+| n01537544 | indigo bunting | 900 | 100 | 1000 |
+| n01558993 | robin | 900 | 100 | 1000 |
+| n01560419 | bulbul | 900 | 100 | 1000 |
+| n01580077 | jay | 900 | 100 | 1000 |
+| n01582220 | magpie | 900 | 100 | 1000 |
+| n01592084 | chickadee | 900 | 100 | 1000 |
+| n01601694 | water ouzel | 900 | 100 | 1000 |
+| n01608432 | kite | 900 | 100 | 1000 |
+| n01614925 | bald eagle | 900 | 100 | 1000 |
+| n01616318 | vulture | 900 | 100 | 1000 |
+| n01622779 | great grey owl | 900 | 100 | 1000 |
+| n01629819 | European fire salamander | 900 | 100 | 1000 |
+| n01630670 | common newt | 900 | 100 | 1000 |
+| n01631663 | eft | 900 | 100 | 1000 |
+| n01632458 | spotted salamander | 900 | 100 | 1000 |
+| n01632777 | axolotl | 900 | 100 | 1000 |
+| n01641577 | bullfrog | 900 | 100 | 1000 |
+| n01644373 | tree frog | 900 | 100 | 1000 |
+| n01644900 | tailed frog | 900 | 100 | 1000 |
+| n01664065 | loggerhead | 900 | 100 | 1000 |
+| n01665541 | leatherback turtle | 900 | 100 | 1000 |
+| n01667114 | mud turtle | 900 | 100 | 1000 |
+| n01667778 | terrapin | 900 | 100 | 1000 |
+| n01669191 | box turtle | 900 | 100 | 1000 |
+| n01675722 | banded gecko | 900 | 100 | 1000 |
+| n01677366 | common iguana | 900 | 100 | 1000 |
+| n01682714 | American chameleon | 900 | 100 | 1000 |
+| n01685808 | whiptail | 900 | 100 | 1000 |
+| n01687978 | agama | 900 | 100 | 1000 |
+| n01688243 | frilled lizard | 900 | 100 | 1000 |
+| n01689811 | alligator lizard | 900 | 100 | 1000 |
+| n01692333 | Gila monster | 900 | 100 | 1000 |
+| n01693334 | green lizard | 900 | 100 | 1000 |
+| n01694178 | African chameleon | 900 | 100 | 1000 |
+| n01695060 | Komodo dragon | 900 | 100 | 1000 |
+| n01697457 | African crocodile | 900 | 100 | 1000 |
+
+
+## Usage with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("your-username/imagenet-50-subset")
+
+# Access train and validation splits
+train_dataset = dataset['train']
+val_dataset = dataset['validation']
+
+# Example: Load and display an image
+from PIL import Image
+import matplotlib.pyplot as plt
+
+sample = train_dataset[0]
+image = Image.open(sample['image'])
+label = sample['label']
+plt.imshow(image)
+plt.title(f"Class: {label}")
+plt.show()
+```
+
+## Usage with PyTorch
+
+```python
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader
+
+# Define 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 datasets
+train_dataset = datasets.ImageFolder('./imagenet-50-subset/train', transform=transform)
+val_dataset = datasets.ImageFolder('./imagenet-50-subset/val', transform=transform)
+
+# Create data loaders
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
+```
+
+## License
+
+This subset inherits the ImageNet license. Please ensure you have the right to use ImageNet data.
+The original ImageNet dataset is available at http://www.image-net.org/
+
+## 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}
+}
+```