dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# 150 Pokemon > Pokedex resized
+https://universe.roboflow.com/robert-demo-qvail/pokedex
+
+Provided by [Lance Zhang](https://www.kaggle.com/lantian773030/pokemonclassification)
+License: Public Domain
+

README.md

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+---
+task_categories:
+- image-classification
+tags:
+- roboflow
+- roboflow2huggingface
+- Gaming
+---
+
+<div align="center">
+  <img width="640" alt="fcakyon/pokemon-classification" src="https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['test', 'train', 'valid', 'valid-mini']
+```
+
+
+### Number of Images
+
+```json
+{'train': 4869, 'test': 732, 'valid': 1390}
+```
+
+
+### How to Use
+
+- Install [datasets](https://pypi.org/project/datasets/):
+
+```bash
+pip install datasets
+```
+
+- Load the dataset:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("fcakyon/pokemon-classification", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14](https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ pokedex_dataset,
+    title = { Pokedex Dataset },
+    type = { Open Source Dataset },
+    author = { Lance Zhang },
+    howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
+    url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { dec },
+    note = { visited on 2023-01-14 },
+}
+```
+
+### License
+Public Domain
+
+### Dataset Summary
+This dataset was exported via roboflow.com on December 20, 2022 at 5:34 PM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 6991 images.
+Pokemon are annotated in folder format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 224x224 (Fit (black edges))
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Pokedex - v14 Pokedex resized
+==============================
+
+This dataset was exported via roboflow.com on December 20, 2022 at 5:34 PM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 6991 images.
+Pokemon are annotated in folder format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 224x224 (Fit (black edges))
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cc48f0b83b2f4d6084d945cfb99ab6d772818db20c89c382fa8d215ee7e33061
+size 6956373

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35f8aa7ead6fc3da22710ca6488d86ea80ed95c7b9774a0827af7015a9c37471
+size 45324257

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8735d2a60d326aadeb69edfb0303c907d0788cbf851576666d2c30b868a204fd
+size 675604

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d641ec0b6bedbe8a84bc9785a0a613f438fa12b9184c36f2c6b900973da03f11
+size 13196775

pokemon-classification.py

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+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_HOMEPAGE = "https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14"
+_LICENSE = "Public Domain"
+_CITATION = """\
+@misc{ pokedex_dataset,
+    title = { Pokedex Dataset },
+    type = { Open Source Dataset },
+    author = { Lance Zhang },
+    howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
+    url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { dec },
+    note = { visited on 2023-01-14 },
+}
+"""
+_CATEGORIES = ['test', 'train', 'valid', 'valid-mini']
+
+
+class POKEMONCLASSIFICATIONConfig(datasets.BuilderConfig):
+    """Builder Config for pokemon-classification"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for pokemon-classification.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(POKEMONCLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class POKEMONCLASSIFICATION(datasets.GeneratorBasedBuilder):
+    """pokemon-classification image classification dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        POKEMONCLASSIFICATIONConfig(
+            name="full",
+            description="Full version of pokemon-classification dataset.",
+            data_urls={
+    "train": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/test.zip",
+}
+,
+        ),
+        POKEMONCLASSIFICATIONConfig(
+            name="mini",
+            description="Mini version of pokemon-classification dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/data/valid-mini.zip",
+            },
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image_file_path": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "labels": datasets.features.ClassLabel(names=_CATEGORIES),
+                }
+            ),
+            supervised_keys=("image", "labels"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(self.config.data_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["train"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["validation"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["test"]]),
+                },
+            ),
+]
+
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
+                yield i, {
+                    "image_file_path": path,
+                    "image": path,
+                    "labels": os.path.basename(os.path.dirname(path)),
+                }

split_name_to_num_samples.json

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+{"train": 4869, "test": 732, "valid": 1390}