dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# valorant > 2022-11-05 3:58pm
+https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp
+
+Provided by a Roboflow user
+License: CC BY 4.0
+

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+---
+
+### Roboflow Dataset Page
+https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp
+
+### Citation
+```
+@misc{ valorant-9ufcp_dataset,
+    title = { valorant Dataset },
+    type = { Open Source Dataset },
+    author = { Daniels Magonis },
+    howpublished = { \\url{ https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp } },
+    url = { https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { nov },
+    note = { visited on 2022-12-28 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.com on December 22, 2022 at 5:10 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 9898 images.
+Planted are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Resize to 416x416 (Stretch)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+valorant - v3 2022-11-05 3:58pm
+==============================
+
+This dataset was exported via roboflow.com on December 22, 2022 at 5:10 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 9898 images.
+Planted are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Resize to 416x416 (Stretch)
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6fc726d0af1c72d2df277c9f292fb93c7b209249ed92616c63ac73d168faab29
+size 24920040

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:881acb63be708d494a995c047e382cab5b68887dece1148db351a3c57e1599e3
+size 175503866

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1641f78a0ee402bdadc513b62bf9ad1b66489dbf64b08fcf190a025254ca6691
+size 50166533

valorant-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ valorant-9ufcp_dataset,
+    title = { valorant Dataset },
+    type = { Open Source Dataset },
+    author = { Daniels Magonis },
+    howpublished = { \\url{ https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp } },
+    url = { https://universe.roboflow.com/daniels-magonis-0pjzx/valorant-9ufcp },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { nov },
+    note = { visited on 2022-12-28 },
+}
+"""
+_URLS = {
+    "train": "https://huggingface.co/datasets/keremberke/valorant-object-detection/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/valorant-object-detection/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/valorant-object-detection/resolve/main/data/test.zip",
+}
+
+_CATEGORIES = ['dropped spike', 'teammate', 'enemy', 'planted spike']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class VALORANTOBJECTDETECTION(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