dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# undefined > raw-images_ommittedSuitClasses
+https://public.roboflow.ai/object-detection/undefined
+
+Provided by undefined
+License: CC BY 4.0
+
+# Personal Protective Equipment Dataset and Model
+
+This dataset is a collection of images that contains annotations for the classes below:
+
+*  goggles
+*  helmet
+*  mask
+*  no-suit
+*  no_goggles
+*  no_helmet
+*  no_mask
+*  no_shoes
+*  shoes
+*  suit
+*  no_glove
+*  glove
+
+
+## Usage
+
+Most of these classes are underrepresented and would need to be [balanced](https://blog.roboflow.com/handling-unbalanced-classes/) for better detection. An improved model can be utilized for use cases that'll detect the classes above in order to minimize exposure to hazards that cause serious workplace injuries.

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+- roboflow2huggingface
+- Manufacturing
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/protective-equipment-detection" src="https://huggingface.co/datasets/keremberke/protective-equipment-detection/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['glove', 'goggles', 'helmet', 'mask', 'no_glove', 'no_goggles', 'no_helmet', 'no_mask', 'no_shoes', 'shoes']
+```
+
+
+### Number of Images
+
+```json
+{'valid': 3570, 'test': 1935, 'train': 6473}
+```
+
+
+### 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("keremberke/protective-equipment-detection", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/personal-protective-equipment/ppes-kaxsi/dataset/7](https://universe.roboflow.com/personal-protective-equipment/ppes-kaxsi/dataset/7?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ ppes-kaxsi_dataset,
+    title = { PPEs Dataset },
+    type = { Open Source Dataset },
+    author = { Personal Protective Equipment },
+    howpublished = { \\url{ https://universe.roboflow.com/personal-protective-equipment/ppes-kaxsi } },
+    url = { https://universe.roboflow.com/personal-protective-equipment/ppes-kaxsi },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { jul },
+    note = { visited on 2023-01-18 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.ai on July 7, 2022 at 3:49 PM GMT
+
+It includes 11978 images.
+Ppe-equipements are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+PPEs - v7 raw-images_ommittedSuitClasses
+==============================
+
+This dataset was exported via roboflow.ai on July 7, 2022 at 3:49 PM GMT
+
+It includes 11978 images.
+Ppe-equipements are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:16dfeef2e6e14bb188e40d0e856c12c48033e6a61b5b9230eaf53db97d3feb6e
+size 245068886

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e5e3796e56892acf35631767af6138f622eafba875a53996e5618783c575cf39
+size 903731317

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:58602e56adafaca03e12f457c7f82ce7f2036099a0fc65a0327e4af2ec254e84
+size 379962

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e86d9e44eb4d13ae81f7b93de75fb8955665f80136911d97262ea61ff3db694c
+size 1125517514

protective-equipment-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/personal-protective-equipment/ppes-kaxsi/dataset/7"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ ppes-kaxsi_dataset,
+    title = { PPEs Dataset },
+    type = { Open Source Dataset },
+    author = { Personal Protective Equipment },
+    howpublished = { \\url{ https://universe.roboflow.com/personal-protective-equipment/ppes-kaxsi } },
+    url = { https://universe.roboflow.com/personal-protective-equipment/ppes-kaxsi },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { jul },
+    note = { visited on 2023-01-18 },
+}
+"""
+_CATEGORIES = ['glove', 'goggles', 'helmet', 'mask', 'no_glove', 'no_goggles', 'no_helmet', 'no_mask', 'no_shoes', 'shoes']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class PROTECTIVEEQUIPMENTDETECTIONConfig(datasets.BuilderConfig):
+    """Builder Config for protective-equipment-detection"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for protective-equipment-detection.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PROTECTIVEEQUIPMENTDETECTIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class PROTECTIVEEQUIPMENTDETECTION(datasets.GeneratorBasedBuilder):
+    """protective-equipment-detection object detection dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        PROTECTIVEEQUIPMENTDETECTIONConfig(
+            name="full",
+            description="Full version of protective-equipment-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/protective-equipment-detection/resolve/main/data/train.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/protective-equipment-detection/resolve/main/data/valid.zip",
+                "test": "https://huggingface.co/datasets/keremberke/protective-equipment-detection/resolve/main/data/test.zip",
+            },
+        ),
+        PROTECTIVEEQUIPMENTDETECTIONConfig(
+            name="mini",
+            description="Mini version of protective-equipment-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/protective-equipment-detection/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/protective-equipment-detection/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/keremberke/protective-equipment-detection/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

split_name_to_num_samples.json

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+{"valid": 3570, "test": 1935, "train": 6473}