Duplicate from zh-plus/tiny-imagenet

Co-authored-by: Hao Zheng <[email protected]>

.gitattributes

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README.md

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+---
+annotations_creators:
+- crowdsourced
+extra_gated_prompt: "By clicking on \u201CAccess repository\u201D below, you also\
+  \ agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\"\
+  ) has requested permission to use the ImageNet database (the \"Database\") at Princeton\
+  \ University and Stanford University. In exchange for such permission, Researcher\
+  \ hereby agrees to the following terms and conditions:\n1. Researcher shall use\
+  \ the Database only for non-commercial research and educational purposes.\n2. Princeton\
+  \ University, Stanford University and Hugging Face make no representations or warranties\
+  \ regarding the Database, including but not limited to warranties of non-infringement\
+  \ or fitness for a particular purpose.\n3. Researcher accepts full responsibility\
+  \ for his or her use of the Database and shall defend and indemnify the ImageNet\
+  \ team, Princeton University, Stanford University and Hugging Face, including their\
+  \ employees, Trustees, officers and agents, against any and all claims arising from\
+  \ Researcher's use of the Database, including but not limited to Researcher's use\
+  \ of any copies of copyrighted images that he or she may create from the Database.\n\
+  4. Researcher may provide research associates and colleagues with access to the\
+  \ Database provided that they first agree to be bound by these terms and conditions.\n\
+  5. Princeton University, Stanford University and Hugging Face reserve the right\
+  \ to terminate Researcher's access to the Database at any time.\n6. If Researcher\
+  \ is employed by a for-profit, commercial entity, Researcher's employer shall also\
+  \ be bound by these terms and conditions, and Researcher hereby represents that\
+  \ he or she is fully authorized to enter into this agreement on behalf of such employer.\n\
+  7. The law of the State of New Jersey shall apply to all disputes under this agreement."
+language:
+- en
+language_creators:
+- crowdsourced
+license: []
+multilinguality:
+- monolingual
+paperswithcode_id: imagenet
+pretty_name: Tiny-ImageNet
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|imagenet-1k
+task_categories:
+- image-classification
+task_ids:
+- multi-class-image-classification
+---
+
+# Dataset Card for tiny-imagenet
+
+## Dataset Description
+
+- **Homepage:** https://www.kaggle.com/c/tiny-imagenet
+- **Repository:** [Needs More Information]
+- **Paper:** http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf
+- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-tiny-imagenet-1
+
+### Dataset Summary
+
+Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.
+
+### Languages
+
+The class labels in the dataset are in English.
+
+## Dataset Structure
+
+### Data Instances
+
+```json
+{
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190,
+  'label': 15
+}
+```
+
+### Data Fields
+
+- image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
+- label: an int classification label. -1 for test set as the labels are missing. Check `classes.py` for the map of numbers & labels.
+
+### Data Splits
+
+|              | Train  | Valid |
+| ------------ | ------ | ----- |
+| # of samples | 100000 | 10000 |
+
+## Usage
+
+### Example
+
+#### Load Dataset
+```python
+def example_usage():
+    tiny_imagenet = load_dataset('Maysee/tiny-imagenet', split='train')
+    print(tiny_imagenet[0])
+
+if __name__ == '__main__':
+    example_usage()
+```

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dataset_infos.json

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