dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# undefined > 2022-02-26 3:03pm-1920
+https://public.roboflow.ai/object-detection/undefined
+
+Provided by undefined
+License: CC BY 4.0
+
+## Background Information
+This dataset was curated and annotated by [Find This Base](https://findthisbase.com/). A custom dataset composed of 16 classes from the popular mobile game, Clash of Clans.
+* Classes: Canon, WizzTower, Xbow, AD, Mortar, Inferno, Scattershot, AirSweeper, BombTower, ClanCastle, Eagle, KingPad, QueenPad, RcPad, TH13 and WardenPad.
+
+![Find This Base](https://i.imgur.com/ztaqoaj.png)
+
+[How to Use Find This Base](https://findthisbase.com/howto)
+![How to Use Find This Base](https://i.imgur.com/ibfFpyQ.gif)
+
+The original custom dataset *(v1)* is composed of 125 annotated images.
+
+The dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/).
+
+
+## Getting Started
+You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
+
+## Dataset Versions
+### Version 1 (v1) - 125 images
+* Preprocessing - Auto-Orient and Resize: Fit (black edges) to 640x640
+* Augmentations - No augmentations applied
+* Training Metrics - Trained from Scratch (no checkpoint used) on Roboflow
+	* mAP = 83.1%, precision = 43.0%, recall = 99.1%
+
+### Version 4 (v4) - 301 images
+* Preprocessing - Auto-Orient and Resize: Fit (black edges) to 640x640
+* Augmentations - Mosaic
+* Generated Images - Outputs per training example: 3
+* Training Metrics - Trained from Scratch (no checkpoint used) on Roboflow
+	* mAP = %, precision = %, recall = %
+
+Find This Base: [Official Website](https://findthisbase.com/) | [How to Use Find This Base](https://findthisbase.com/howto) | [Discord](https://discord.gg/8EV8eRY) | [Patreon](https://www.patreon.com/FindThisBase)

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+---
+
+### Roboflow Dataset Page
+https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y/dataset/5
+
+### Dataset Labels
+
+```
+['ad', 'airsweeper', 'bombtower', 'canon', 'clancastle', 'eagle', 'inferno', 'kingpad', 'mortar', 'queenpad', 'rcpad', 'scattershot', 'th13', 'wardenpad', 'wizztower', 'xbow']
+```
+
+### Citation
+
+```
+@misc{ clash-of-clans-vop4y_dataset,
+    title = { Clash of Clans Dataset },
+    type = { Open Source Dataset },
+    author = { Find This Base },
+    howpublished = { \\url{ https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y } },
+    url = { https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { feb },
+    note = { visited on 2022-12-30 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.ai on March 30, 2022 at 4:31 PM GMT
+
+It includes 125 images.
+CoC are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 1920x1920 (Fit (black edges))
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Clash of Clans - v5 2022-02-26 3:03pm-1920
+==============================
+
+This dataset was exported via roboflow.ai on March 30, 2022 at 4:31 PM GMT
+
+It includes 125 images.
+CoC are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 1920x1920 (Fit (black edges))
+
+No image augmentation techniques were applied.
+
+

clash-of-clans-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y/dataset/5"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ clash-of-clans-vop4y_dataset,
+    title = { Clash of Clans Dataset },
+    type = { Open Source Dataset },
+    author = { Find This Base },
+    howpublished = { \\url{ https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y } },
+    url = { https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { feb },
+    note = { visited on 2022-12-30 },
+}
+"""
+_URLS = {
+    "train": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/data/test.zip",
+}
+
+_CATEGORIES = ['ad', 'airsweeper', 'bombtower', 'canon', 'clancastle', 'eagle', 'inferno', 'kingpad', 'mortar', 'queenpad', 'rcpad', 'scattershot', 'th13', 'wardenpad', 'wizztower', 'xbow']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class CLASHOFCLANSOBJECTDETECTION(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "image": datasets.Image(),
+                "width": datasets.Value("int32"),
+                "height": datasets.Value("int32"),
+                "objects": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int64"),
+                        "area": datasets.Value("int64"),
+                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                        "category": datasets.ClassLabel(names=_CATEGORIES),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "folder_dir": data_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "folder_dir": data_files["validation"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "folder_dir": data_files["test"],
+                },
+            ),
+]
+
+    def _generate_examples(self, folder_dir):
+        def process_annot(annot, category_id_to_category):
+            return {
+                "id": annot["id"],
+                "area": annot["area"],
+                "bbox": annot["bbox"],
+                "category": category_id_to_category[annot["category_id"]],
+            }
+
+        image_id_to_image = {}
+        idx = 0
+        
+        annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
+        with open(annotation_filepath, "r") as f:
+            annotations = json.load(f)
+        category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
+        image_id_to_annotations = collections.defaultdict(list)
+        for annot in annotations["annotations"]:
+            image_id_to_annotations[annot["image_id"]].append(annot)
+        image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
+
+        for filename in os.listdir(folder_dir):
+            filepath = os.path.join(folder_dir, filename)
+            if filename in image_id_to_image:
+                image = image_id_to_image[filename]
+                objects = [
+                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
+                ]
+                with open(filepath, "rb") as f:
+                    image_bytes = f.read()
+                yield idx, {
+                    "image_id": image["id"],
+                    "image": {"path": filepath, "bytes": image_bytes},
+                    "width": image["width"],
+                    "height": image["height"],
+                    "objects": objects,
+                }
+                idx += 1

data/test.zip

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+oid sha256:105f20bbd6730d7b2371964054b61275095280d693bdb58206fef9b7ce5f7647
+size 4506881

data/train.zip

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+oid sha256:164a24610d74d111fdd5fc9fc0fef343af60d40ad37b6e73abdf2a374bae17fd
+size 33770372

data/valid.zip

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+oid sha256:37b23798949abcbc631a481526d3d607b85f09e60ac36085c9ad53ed9cf1d1ba
+size 9828443