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README.dataset.txt

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-# wlots > 2022-05-06 10:52pm
-https://universe.roboflow.com/asd-culfr/wlots
-
-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/asd-culfr/wlots
-
-### Citation
-```
-@misc{ wlots_dataset,
-    title = { wlots Dataset },
-    type = { Open Source Dataset },
-    author = { asd },
-    howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
-    url = { https://universe.roboflow.com/asd-culfr/wlots },
-    journal = { Roboflow Universe },
-    publisher = { Roboflow },
-    year = { 2022 },
-    month = { may },
-    note = { visited on 2022-12-29 },
-}
-```
-
-### License
-CC BY 4.0
-
-### Dataset Summary
-This dataset was exported via roboflow.com on December 28, 2022 at 8:08 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 4454 images.
-Ct-cthead-t-thead 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 416x416 (Fill (with center crop))
-
-The following augmentation was applied to create 3 versions of each source image:
-* Random brigthness adjustment of between -15 and +15 percent
-
-
-

README.roboflow.txt

@@ -1,25 +0,0 @@
-
-wlots - v1 2022-05-06 10:52pm
-==============================
-
-This dataset was exported via roboflow.com on December 28, 2022 at 8:08 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 4454 images.
-Ct-cthead-t-thead 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 416x416 (Fill (with center crop))
-
-The following augmentation was applied to create 3 versions of each source image:
-* Random brigthness adjustment of between -15 and +15 percent
-
-

csgo-object-detection.py

@@ -1,121 +0,0 @@
-import collections
-import json
-import os
-
-import datasets
-
-
-_HOMEPAGE = "https://universe.roboflow.com/asd-culfr/wlots"
-_LICENSE = "CC BY 4.0"
-_CITATION = """\
-@misc{ wlots_dataset,
-    title = { wlots Dataset },
-    type = { Open Source Dataset },
-    author = { asd },
-    howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
-    url = { https://universe.roboflow.com/asd-culfr/wlots },
-    journal = { Roboflow Universe },
-    publisher = { Roboflow },
-    year = { 2022 },
-    month = { may },
-    note = { visited on 2022-12-29 },
-}
-"""
-_URLS = {
-    "train": "https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/data/train.zip",
-    "validation": "https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/data/valid.zip",
-    "test": "https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/data/test.zip",
-}
-
-_CATEGORIES = ['thead', 't', 'cthead', 'ct']
-_ANNOTATION_FILENAME = "_annotations.coco.json"
-
-
-class CSGOOBJECTDETECTION(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 → default/csgo-object-detection-test.parquet

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+size 4951505

data/train.zip → default/csgo-object-detection-train.parquet

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data/valid.zip → default/csgo-object-detection-validation.parquet

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