dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# NFL-competition > 2022-07-08 12:06am
+https://universe.roboflow.com/home-mxzv1/nfl-competition
+
+Provided by a Roboflow user
+License: Public Domain
+
+The NFL Competition dataset contains nearly 10,000 images of football players during game play. This dataset can be used to detect the number of players and their locations on the field in order to alert coaches of possible NFL formation penalties or to analyze formations post-game. It can also be used as a starting point for automated player safety checks, ensuring each player is wearing a helmet.
+
+For inspiration on how computer vision applications in the world of sports, check out this post from the Roboflow blog:
+
+* https://blog.roboflow.com/ai-football-coach-playbook
+* https://blog.roboflow.com/computer-vision-twilio-notifications/ 
+* https://blog.roboflow.com/polygons-object-detection/ 

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+---
+
+### Roboflow Dataset Page
+https://universe.roboflow.com/home-mxzv1/nfl-competition/dataset/1
+
+### Dataset Labels
+
+```
+['helmet', 'helmet-blurred', 'helmet-difficult', 'helmet-partial', 'helmet-sideline']
+```
+
+### Citation
+
+```
+@misc{ nfl-competition_dataset,
+    title = { NFL-competition Dataset },
+    type = { Open Source Dataset },
+    author = { home },
+    howpublished = { \\url{ https://universe.roboflow.com/home-mxzv1/nfl-competition } },
+    url = { https://universe.roboflow.com/home-mxzv1/nfl-competition },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { sep },
+    note = { visited on 2022-12-30 },
+}
+```
+
+### License
+Public Domain
+
+### Dataset Summary
+This dataset was exported via roboflow.com on December 29, 2022 at 8:12 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 9947 images.
+Helmets 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 1280x720 (Stretch)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+NFL-competition - v1 2022-07-08 12:06am
+==============================
+
+This dataset was exported via roboflow.com on December 29, 2022 at 8:12 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 9947 images.
+Helmets 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 1280x720 (Stretch)
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e7dce8a1c5ee951ab36a7980925a22a88485ea0c496f40af861183c6246724f6
+size 121495574

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7a4fda18ebed706b9e82e4ff593f23e5d265de516ee9869d93769c2151ff204
+size 859629963

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ff4e9f5fedfb23ee1c8b0057b8ca7b87adf7a9d03749882e592d28754c6e3cf
+size 244027422

nfl-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/home-mxzv1/nfl-competition/dataset/1"
+_LICENSE = "Public Domain"
+_CITATION = """\
+@misc{ nfl-competition_dataset,
+    title = { NFL-competition Dataset },
+    type = { Open Source Dataset },
+    author = { home },
+    howpublished = { \\url{ https://universe.roboflow.com/home-mxzv1/nfl-competition } },
+    url = { https://universe.roboflow.com/home-mxzv1/nfl-competition },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { sep },
+    note = { visited on 2022-12-30 },
+}
+"""
+_URLS = {
+    "train": "https://huggingface.co/datasets/keremberke/nfl-object-detection/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/nfl-object-detection/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/nfl-object-detection/resolve/main/data/test.zip",
+}
+
+_CATEGORIES = ['helmet', 'helmet-blurred', 'helmet-difficult', 'helmet-partial', 'helmet-sideline']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class NFLOBJECTDETECTION(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