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dataset uploaded by roboflow2huggingface package

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README.dataset.txt ADDED
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+ # Defects > Set_4
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+ https://universe.roboflow.com/diplom-qz7q6/defects-2q87r
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+
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+ Provided by a Roboflow user
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+ License: CC BY 4.0
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+
README.md ADDED
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+ ---
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+ task_categories:
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+ - image-segmentation
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+ tags:
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+ - roboflow
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+ - roboflow2huggingface
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+
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+ ---
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+
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+ <div align="center">
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+ <img width="640" alt="keremberke/pcb-defect-segmentation" src="https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/thumbnail.jpg">
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+ </div>
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+
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+ ### Dataset Labels
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+
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+ ```
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+ ['dry_joint', 'incorrect_installation', 'pcb_damage', 'short_circuit']
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+ ```
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+
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+
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+ ### Number of Images
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+
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+ ```json
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+ {'valid': 25, 'train': 128, 'test': 36}
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+ ```
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+
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+
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+ ### How to Use
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+
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+ - Install [datasets](https://pypi.org/project/datasets/):
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+
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+ ```bash
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+ pip install datasets
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+ ```
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+
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+ - Load the dataset:
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("keremberke/pcb-defect-segmentation", name="full")
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+ example = ds['train'][0]
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+ ```
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+
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+ ### Roboflow Dataset Page
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+ [https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8](https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8?ref=roboflow2huggingface)
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+
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+ ### Citation
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+
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+ ```
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+ @misc{ defects-2q87r_dataset,
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+ title = { Defects Dataset },
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+ type = { Open Source Dataset },
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+ author = { Diplom },
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+ howpublished = { \\url{ https://universe.roboflow.com/diplom-qz7q6/defects-2q87r } },
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+ url = { https://universe.roboflow.com/diplom-qz7q6/defects-2q87r },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2023 },
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+ month = { jan },
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+ note = { visited on 2023-01-27 },
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+ }
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+ ```
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+
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+ ### License
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+ CC BY 4.0
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+
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+ ### Dataset Summary
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+ This dataset was exported via roboflow.com on January 27, 2023 at 1:45 PM GMT
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+
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+ Roboflow is an end-to-end computer vision platform that helps you
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+ * collaborate with your team on computer vision projects
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+ * collect & organize images
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+ * understand and search unstructured image data
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+ * annotate, and create datasets
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+ * export, train, and deploy computer vision models
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+ * use active learning to improve your dataset over time
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+
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+ For state of the art Computer Vision training notebooks you can use with this dataset,
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+ visit https://github.com/roboflow/notebooks
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+
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+ To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
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+
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+ The dataset includes 189 images.
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+ Defect are annotated in COCO format.
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+
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+ The following pre-processing was applied to each image:
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+
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+ No image augmentation techniques were applied.
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+
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+
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+
README.roboflow.txt ADDED
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+
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+ Defects - v8 Set_4
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+ ==============================
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+
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+ This dataset was exported via roboflow.com on January 27, 2023 at 1:45 PM GMT
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+
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+ Roboflow is an end-to-end computer vision platform that helps you
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+ * collaborate with your team on computer vision projects
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+ * collect & organize images
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+ * understand and search unstructured image data
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+ * annotate, and create datasets
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+ * export, train, and deploy computer vision models
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+ * use active learning to improve your dataset over time
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+
15
+ For state of the art Computer Vision training notebooks you can use with this dataset,
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+ visit https://github.com/roboflow/notebooks
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+
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+ To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
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+
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+ The dataset includes 189 images.
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+ Defect are annotated in COCO format.
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+
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+ The following pre-processing was applied to each image:
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+
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+ No image augmentation techniques were applied.
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+
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+
data/test.zip ADDED
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+ size 1719625
data/train.zip ADDED
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+ size 6411968
data/valid-mini.zip ADDED
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pcb-defect-segmentation.py ADDED
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+ import collections
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+ import json
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+ import os
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+
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+ import datasets
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+
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+
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+ _HOMEPAGE = "https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8"
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+ _LICENSE = "CC BY 4.0"
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+ _CITATION = """\
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+ @misc{ defects-2q87r_dataset,
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+ title = { Defects Dataset },
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+ type = { Open Source Dataset },
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+ author = { Diplom },
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+ howpublished = { \\url{ https://universe.roboflow.com/diplom-qz7q6/defects-2q87r } },
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+ url = { https://universe.roboflow.com/diplom-qz7q6/defects-2q87r },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2023 },
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+ month = { jan },
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+ note = { visited on 2023-01-27 },
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+ }
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+ """
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+ _CATEGORIES = ['dry_joint', 'incorrect_installation', 'pcb_damage', 'short_circuit']
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+ _ANNOTATION_FILENAME = "_annotations.coco.json"
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+
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+
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+ class PCBDEFECTSEGMENTATIONConfig(datasets.BuilderConfig):
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+ """Builder Config for pcb-defect-segmentation"""
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+
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+ def __init__(self, data_urls, **kwargs):
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+ """
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+ BuilderConfig for pcb-defect-segmentation.
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+
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+ Args:
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+ data_urls: `dict`, name to url to download the zip file from.
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(PCBDEFECTSEGMENTATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
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+ self.data_urls = data_urls
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+
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+
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+ class PCBDEFECTSEGMENTATION(datasets.GeneratorBasedBuilder):
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+ """pcb-defect-segmentation instance segmentation dataset"""
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+
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+ VERSION = datasets.Version("1.0.0")
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+ BUILDER_CONFIGS = [
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+ PCBDEFECTSEGMENTATIONConfig(
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+ name="full",
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+ description="Full version of pcb-defect-segmentation dataset.",
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+ data_urls={
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+ "train": "https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/data/train.zip",
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+ "validation": "https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/data/valid.zip",
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+ "test": "https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/data/test.zip",
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+ },
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+ ),
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+ PCBDEFECTSEGMENTATIONConfig(
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+ name="mini",
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+ description="Mini version of pcb-defect-segmentation dataset.",
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+ data_urls={
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+ "train": "https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/data/valid-mini.zip",
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+ "validation": "https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/data/valid-mini.zip",
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+ "test": "https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/data/valid-mini.zip",
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+ },
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+ )
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+ ]
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+
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+ def _info(self):
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+ features = datasets.Features(
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+ {
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+ "image_id": datasets.Value("int64"),
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+ "image": datasets.Image(),
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+ "width": datasets.Value("int32"),
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+ "height": datasets.Value("int32"),
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+ "objects": datasets.Sequence(
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+ {
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+ "id": datasets.Value("int64"),
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+ "area": datasets.Value("int64"),
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+ "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
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+ "segmentation": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
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+ "category": datasets.ClassLabel(names=_CATEGORIES),
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+ }
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+ ),
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ features=features,
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+ homepage=_HOMEPAGE,
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+ citation=_CITATION,
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+ license=_LICENSE,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ data_files = dl_manager.download_and_extract(self.config.data_urls)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "folder_dir": data_files["train"],
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ gen_kwargs={
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+ "folder_dir": data_files["validation"],
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={
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+ "folder_dir": data_files["test"],
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, folder_dir):
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+ def process_annot(annot, category_id_to_category):
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+ return {
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+ "id": annot["id"],
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+ "area": annot["area"],
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+ "bbox": annot["bbox"],
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+ "segmentation": annot["segmentation"],
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+ "category": category_id_to_category[annot["category_id"]],
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+ }
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+
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+ image_id_to_image = {}
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+ idx = 0
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+
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+ annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
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+ with open(annotation_filepath, "r") as f:
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+ annotations = json.load(f)
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+ category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
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+ image_id_to_annotations = collections.defaultdict(list)
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+ for annot in annotations["annotations"]:
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+ image_id_to_annotations[annot["image_id"]].append(annot)
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+ filename_to_image = {image["file_name"]: image for image in annotations["images"]}
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+
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+ for filename in os.listdir(folder_dir):
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+ filepath = os.path.join(folder_dir, filename)
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+ if filename in filename_to_image:
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+ image = filename_to_image[filename]
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+ objects = [
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+ process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
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+ ]
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+ with open(filepath, "rb") as f:
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+ image_bytes = f.read()
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+ yield idx, {
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+ "image_id": image["id"],
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+ "image": {"path": filepath, "bytes": image_bytes},
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+ "width": image["width"],
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+ "height": image["height"],
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+ "objects": objects,
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+ }
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+ idx += 1
split_name_to_num_samples.json ADDED
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+ {"valid": 25, "train": 128, "test": 36}
thumbnail.jpg ADDED

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