Duplicate from timm/mini-imagenet

Co-authored-by: Ross Wightman <rwightman@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': n01532829
+          '1': n01558993
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+  splits:
+  - name: train
+    num_bytes: 6284840508
+    num_examples: 50000
+  - name: validation
+    num_bytes: 1286953696
+    num_examples: 10000
+  - name: test
+    num_bytes: 670707560
+    num_examples: 5000
+  download_size: 7433461683
+  dataset_size: 8242501764
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
+license: other
+license_name: imagenet
+license_link: https://www.image-net.org/download.php
+task_categories:
+- image-classification
+pretty_name: Mini-ImageNet
+size_categories:
+- 10K<n<100K
+---
+
+## Dataset Description
+
+A mini version of ImageNet-1k with 100 of 1000 classes present.
+
+Unlike some 'mini' variants this one includes the original images at their original sizes. Many such subsets downsample to 84x84 or other smaller resolutions.
+
+### Data Splits
+
+#### Train
+* 50000 samples from ImageNet-1k train split
+
+#### Validation
+* 10000 samples from ImageNet-1k train split
+
+#### Test
+* 5000 samples from ImageNet-1k validation split (all 50 samples per class)
+
+### Usage
+
+This dataset is good for testing hparams and models in `timm`
+
+#### Train
+
+`python train.py --dataset hfds/timm/mini-imagenet --model resnet50 --amp --num-classes 100`
+
+### Citation Information
+
+For the specific instance of this mini variant I am not sure what the origin is. It is different from commonly referenced [Vinyales et al.,2016](https://arxiv.org/abs/1606.04080) as it doesn't match the classes / splits.
+
+Train & validation splits match train & test of https://www.kaggle.com/datasets/ctrnngtrung/miniimagenet ... it is not clear where that originated though.
+
+Original ImageNet citation:
+```bibtex
+@article{imagenet15russakovsky,
+    Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
+    Title = { {ImageNet Large Scale Visual Recognition Challenge} },
+    Year = {2015},
+    journal   = {International Journal of Computer Vision (IJCV)},
+    doi = {10.1007/s11263-015-0816-y},
+    volume={115},
+    number={3},
+    pages={211-252}
+}
+```

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