dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# Construction Vehicle Detection > 2023-06-08 10:52pm
+https://universe.roboflow.com/capstone-lkzgq/construction-vehicle-detection-pxc7c
+
+Provided by a Roboflow user
+License: CC BY 4.0
+

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+- roboflow2huggingface
+
+---
+
+<div align="center">
+  <img width="640" alt="neogpx/constructionxc7c" src="https://huggingface.co/datasets/neogpx/constructionxc7c/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['bulldozer', 'dump truck', 'excavator', 'grader', 'loader', 'mixer truck', 'mobile crane', 'roller']
+```
+
+
+### Number of Images
+
+```json
+{'valid': 1524, 'test': 757, 'train': 16002}
+```
+
+
+### 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("neogpx/constructionxc7c", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/capstone-lkzgq/construction-vehicle-detection-pxc7c/dataset/2
+](https://universe.roboflow.com/capstone-lkzgq/construction-vehicle-detection-pxc7c/dataset/2
+?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{
+                            construction-vehicle-detection-pxc7c_dataset,
+                            title = { Construction Vehicle Detection Dataset },
+                            type = { Open Source Dataset },
+                            author = { Capstone },
+                            howpublished = { \\url{ https://universe.roboflow.com/capstone-lkzgq/construction-vehicle-detection-pxc7c } },
+                            url = { https://universe.roboflow.com/capstone-lkzgq/construction-vehicle-detection-pxc7c },
+                            journal = { Roboflow Universe },
+                            publisher = { Roboflow },
+                            year = { 2023 },
+                            month = { aug },
+                            note = { visited on 2025-02-11 },
+                            }
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.com on July 17, 2023 at 7:32 AM 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 and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 18283 images.
+Construction-utility-vechicles 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 640x640 (Stretch)
+
+The following augmentation was applied to create 3 versions of each source image:
+* Randomly crop between 0 and 20 percent of the image
+* Random rotation of between -20 and +20 degrees
+* Salt and pepper noise was applied to 5 percent of pixels
+
+
+

README.roboflow.txt

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+
+Construction Vehicle Detection - v2 2023-06-08 10:52pm
+==============================
+
+This dataset was exported via roboflow.com on July 17, 2023 at 7:32 AM 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 and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 18283 images.
+Construction-utility-vechicles 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 640x640 (Stretch)
+
+The following augmentation was applied to create 3 versions of each source image:
+* Randomly crop between 0 and 20 percent of the image
+* Random rotation of between -20 and +20 degrees
+* Salt and pepper noise was applied to 5 percent of pixels
+
+

constructionxc7c.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/capstone-lkzgq/construction-vehicle-detection-pxc7c/dataset/2
+"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{
+                            construction-vehicle-detection-pxc7c_dataset,
+                            title = { Construction Vehicle Detection Dataset },
+                            type = { Open Source Dataset },
+                            author = { Capstone },
+                            howpublished = { \\url{ https://universe.roboflow.com/capstone-lkzgq/construction-vehicle-detection-pxc7c } },
+                            url = { https://universe.roboflow.com/capstone-lkzgq/construction-vehicle-detection-pxc7c },
+                            journal = { Roboflow Universe },
+                            publisher = { Roboflow },
+                            year = { 2023 },
+                            month = { aug },
+                            note = { visited on 2025-02-11 },
+                            }
+"""
+_CATEGORIES = ['bulldozer', 'dump truck', 'excavator', 'grader', 'loader', 'mixer truck', 'mobile crane', 'roller']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class CONSTRUCTIONXC7CConfig(datasets.BuilderConfig):
+    """Builder Config for constructionxc7c"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for constructionxc7c.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CONSTRUCTIONXC7CConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class CONSTRUCTIONXC7C(datasets.GeneratorBasedBuilder):
+    """constructionxc7c object detection dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        CONSTRUCTIONXC7CConfig(
+            name="full",
+            description="Full version of constructionxc7c dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/neogpx/constructionxc7c/resolve/main/data/train.zip",
+                "validation": "https://huggingface.co/datasets/neogpx/constructionxc7c/resolve/main/data/valid.zip",
+                "test": "https://huggingface.co/datasets/neogpx/constructionxc7c/resolve/main/data/test.zip",
+            },
+        ),
+        CONSTRUCTIONXC7CConfig(
+            name="mini",
+            description="Mini version of constructionxc7c dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/neogpx/constructionxc7c/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/neogpx/constructionxc7c/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/neogpx/constructionxc7c/resolve/main/data/valid-mini.zip",
+            },
+        )
+    ]
+
+    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(self.config.data_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)
+        filename_to_image = {image["file_name"]: image for image in annotations["images"]}
+
+        for filename in os.listdir(folder_dir):
+            filepath = os.path.join(folder_dir, filename)
+            if filename in filename_to_image:
+                image = filename_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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+version https://git-lfs.github.com/spec/v1
+oid sha256:b02bb832dd77f0fd0b0f44bce359ad61555492931015f236da52f01cbc08d161
+size 43626301

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1bbfcc5c0e0cf4c38d1b8732b37d847607f2894e8ffb0683511b59d85dd9672f
+size 1581527981

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f1e2c76e426d69a6b3067df526d638939464c94b50d93f1f13c78e7218ff513c
+size 186038

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb36786c6412a69755f18f0254ae04599867a9632a0ba465339411fc5cb63baa
+size 88041968

split_name_to_num_samples.json

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+{"valid": 1524, "test": 757, "train": 16002}