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data/train-00000-of-00001-1359597a978bc4fa.parquet → Maysee--tiny-imagenet/parquet-train.parquet

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data/valid-00000-of-00001-70d52db3c749a935.parquet → Maysee--tiny-imagenet/parquet-valid.parquet

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README.md

@@ -1,96 +0,0 @@
----
-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()
-```

dataset_infos.json

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