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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
-
-```
-['Golbat', 'Machoke', 'Omastar', 'Diglett', 'Lapras', 'Kabuto', 'Persian', 'Weepinbell', 'Golem', 'Dodrio', 'Raichu', 'Zapdos', 'Raticate', 'Magnemite', 'Ivysaur', 'Growlithe', 'Tangela', 'Drowzee', 'Rapidash', 'Venonat', 'Pidgeot', 'Nidorino', 'Porygon', 'Lickitung', 'Rattata', 'Machop', 'Charmeleon', 'Slowbro', 'Parasect', 'Eevee', 'Starmie', 'Staryu', 'Psyduck', 'Dragonair', 'Magikarp', 'Vileplume', 'Marowak', 'Pidgeotto', 'Shellder', 'Mewtwo', 'Farfetchd', 'Kingler', 'Seel', 'Kakuna', 'Doduo', 'Electabuzz', 'Charmander', 'Rhyhorn', 'Tauros', 'Dugtrio', 'Poliwrath', 'Gengar', 'Exeggutor', 'Dewgong', 'Jigglypuff', 'Geodude', 'Kadabra', 'Nidorina', 'Sandshrew', 'Grimer', 'MrMime', 'Pidgey', 'Koffing', 'Ekans', 'Alolan Sandslash', 'Venusaur', 'Snorlax', 'Paras', 'Jynx', 'Chansey', 'Hitmonchan', 'Gastly', 'Kangaskhan', 'Oddish', 'Wigglytuff', 'Graveler', 'Arcanine', 'Clefairy', 'Articuno', 'Poliwag', 'Abra', 'Squirtle', 'Voltorb', 'Ponyta', 'Moltres', 'Nidoqueen', 'Magmar', 'Onix', 'Vulpix', 'Butterfree', 'Krabby', 'Arbok', 'Clefable', 'Goldeen', 'Magneton', 'Dratini', 'Caterpie', 'Jolteon', 'Nidoking', 'Alakazam', 'Dragonite', 'Fearow', 'Slowpoke', 'Weezing', 'Beedrill', 'Weedle', 'Cloyster', 'Vaporeon', 'Gyarados', 'Golduck', 'Machamp', 'Hitmonlee', 'Primeape', 'Cubone', 'Sandslash', 'Scyther', 'Haunter', 'Metapod', 'Tentacruel', 'Aerodactyl', 'Kabutops', 'Ninetales', 'Zubat', 'Rhydon', 'Mew', 'Pinsir', 'Ditto', 'Victreebel', 'Omanyte', 'Horsea', 'Pikachu', 'Blastoise', 'Venomoth', 'Charizard', 'Seadra', 'Muk', 'Spearow', 'Bulbasaur', 'Bellsprout', 'Electrode', 'Gloom', 'Poliwhirl', 'Flareon', 'Seaking', 'Hypno', 'Wartortle', 'Mankey', 'Tentacool', 'Exeggcute', 'Meowth']
-```
-
-
-### 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

@@ -1,24 +0,0 @@
-
-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 → full/pokemon-classification-test.parquet

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data/train.zip → full/pokemon-classification-train.parquet

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data/valid.zip → full/pokemon-classification-validation.parquet

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thumbnail.jpg → mini/pokemon-classification-test.parquet

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data/valid-mini.zip → mini/pokemon-classification-train.parquet

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mini/pokemon-classification-validation.parquet

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pokemon-classification.py

@@ -1,114 +0,0 @@
-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 = ['Golbat', 'Machoke', 'Omastar', 'Diglett', 'Lapras', 'Kabuto', 'Persian', 'Weepinbell', 'Golem', 'Dodrio', 'Raichu', 'Zapdos', 'Raticate', 'Magnemite', 'Ivysaur', 'Growlithe', 'Tangela', 'Drowzee', 'Rapidash', 'Venonat', 'Pidgeot', 'Nidorino', 'Porygon', 'Lickitung', 'Rattata', 'Machop', 'Charmeleon', 'Slowbro', 'Parasect', 'Eevee', 'Starmie', 'Staryu', 'Psyduck', 'Dragonair', 'Magikarp', 'Vileplume', 'Marowak', 'Pidgeotto', 'Shellder', 'Mewtwo', 'Farfetchd', 'Kingler', 'Seel', 'Kakuna', 'Doduo', 'Electabuzz', 'Charmander', 'Rhyhorn', 'Tauros', 'Dugtrio', 'Poliwrath', 'Gengar', 'Exeggutor', 'Dewgong', 'Jigglypuff', 'Geodude', 'Kadabra', 'Nidorina', 'Sandshrew', 'Grimer', 'MrMime', 'Pidgey', 'Koffing', 'Ekans', 'Alolan Sandslash', 'Venusaur', 'Snorlax', 'Paras', 'Jynx', 'Chansey', 'Hitmonchan', 'Gastly', 'Kangaskhan', 'Oddish', 'Wigglytuff', 'Graveler', 'Arcanine', 'Clefairy', 'Articuno', 'Poliwag', 'Abra', 'Squirtle', 'Voltorb', 'Ponyta', 'Moltres', 'Nidoqueen', 'Magmar', 'Onix', 'Vulpix', 'Butterfree', 'Krabby', 'Arbok', 'Clefable', 'Goldeen', 'Magneton', 'Dratini', 'Caterpie', 'Jolteon', 'Nidoking', 'Alakazam', 'Dragonite', 'Fearow', 'Slowpoke', 'Weezing', 'Beedrill', 'Weedle', 'Cloyster', 'Vaporeon', 'Gyarados', 'Golduck', 'Machamp', 'Hitmonlee', 'Primeape', 'Cubone', 'Sandslash', 'Scyther', 'Haunter', 'Metapod', 'Tentacruel', 'Aerodactyl', 'Kabutops', 'Ninetales', 'Zubat', 'Rhydon', 'Mew', 'Pinsir', 'Ditto', 'Victreebel', 'Omanyte', 'Horsea', 'Pikachu', 'Blastoise', 'Venomoth', 'Charizard', 'Seadra', 'Muk', 'Spearow', 'Bulbasaur', 'Bellsprout', 'Electrode', 'Gloom', 'Poliwhirl', 'Flareon', 'Seaking', 'Hypno', 'Wartortle', 'Mankey', 'Tentacool', 'Exeggcute', 'Meowth']
-
-
-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

@@ -1 +0,0 @@
-{"train": 4869, "test": 732, "valid": 1390}