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Object Detection
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Browse files- README.dataset.txt +0 -39
- README.md +0 -58
- README.roboflow.txt +0 -24
- blood-cell-object-detection.py +0 -121
- data/test.zip → default/blood-cell-object-detection-test.parquet +2 -2
- data/train.zip → default/blood-cell-object-detection-train.parquet +2 -2
- data/valid.zip → default/blood-cell-object-detection-validation.parquet +2 -2
README.dataset.txt
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# Blood Cell Detection > 2022-10-27 4:01pm
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https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu
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Provided by a Roboflow user
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License: Public Domain
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# Overview
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This is a dataset of blood cells photos, originally open sourced by [cosmicad](https://github.com/cosmicad/dataset) and [akshaylambda](https://github.com/akshaylamba/all_CELL_data).
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There are 364 images across three classes: `WBC` (white blood cells), `RBC` (red blood cells), and `Platelets`. There are 4888 labels across 3 classes (and 0 null examples).
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Here's a class count from Roboflow's Dataset Health Check:
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![BCCD health](https://i.imgur.com/BVopW9p.png)
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And here's an example image:
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![Blood Cell Example](https://i.imgur.com/QwyX2aD.png)
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`Fork` this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 500x500 export.
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# Use Cases
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This is a small scale object detection dataset, commonly used to assess model performance. It's a first example of medical imaging capabilities.
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# Using this Dataset
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We're releasing the data as public domain. Feel free to use it for any purpose.
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It's not required to provide attribution, but it'd be nice! :)
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# About Roboflow
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[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.
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Developers reduce 50% of their boilerplate code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.
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#### [![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png =350x)](https://roboflow.ai)
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README.md
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---
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task_categories:
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- object-detection
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tags:
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- roboflow
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---
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### Roboflow Dataset Page
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https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3
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### Dataset Labels
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```
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['platelets', 'rbc', 'wbc']
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```
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### Citation
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```
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@misc{ blood-cell-detection-1ekwu_dataset,
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title = { Blood Cell Detection Dataset },
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type = { Open Source Dataset },
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author = { Team Roboflow },
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howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } },
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url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2022 },
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month = { nov },
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note = { visited on 2022-12-31 },
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}
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```
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### License
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Public Domain
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### Dataset Summary
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This dataset was exported via roboflow.com on November 4, 2022 at 7:46 PM GMT
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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 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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It includes 364 images.
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Cells are annotated in COCO format.
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The following pre-processing was applied to each image:
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* Auto-orientation of pixel data (with EXIF-orientation stripping)
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* Resize to 416x416 (Stretch)
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No image augmentation techniques were applied.
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README.roboflow.txt
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Blood Cell Detection - v3 2022-10-27 4:01pm
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==============================
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This dataset was exported via roboflow.com on November 4, 2022 at 7:46 PM GMT
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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 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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It includes 364 images.
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Cells are annotated in COCO format.
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The following pre-processing was applied to each image:
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* Auto-orientation of pixel data (with EXIF-orientation stripping)
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* Resize to 416x416 (Stretch)
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No image augmentation techniques were applied.
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blood-cell-object-detection.py
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import collections
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import json
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import os
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import datasets
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_HOMEPAGE = "https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3"
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_LICENSE = "Public Domain"
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_CITATION = """\
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@misc{ blood-cell-detection-1ekwu_dataset,
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title = { Blood Cell Detection Dataset },
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type = { Open Source Dataset },
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author = { Team Roboflow },
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howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } },
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url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2022 },
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month = { nov },
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note = { visited on 2022-12-31 },
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}
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"""
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_URLS = {
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"train": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/train.zip",
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"validation": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/valid.zip",
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"test": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/test.zip",
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}
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_CATEGORIES = ['platelets', 'rbc', 'wbc']
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_ANNOTATION_FILENAME = "_annotations.coco.json"
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class BLOODCELLOBJECTDETECTION(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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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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"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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def _split_generators(self, dl_manager):
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data_files = dl_manager.download_and_extract(_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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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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"category": category_id_to_category[annot["category_id"]],
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}
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image_id_to_image = {}
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idx = 0
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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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image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
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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 image_id_to_image:
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image = image_id_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
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data/test.zip → default/blood-cell-object-detection-test.parquet
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oid sha256:e89d58a4bb3e814a909921cd4a3881f333b2fb796b7d9a72e31da48a77d3398e
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size 478483
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data/train.zip → default/blood-cell-object-detection-train.parquet
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oid sha256:b320adb5aa7a621f6e6e9d16bea46ecc67f9ff8e0aea1a43c87017de203825ec
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size 3375570
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data/valid.zip → default/blood-cell-object-detection-validation.parquet
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oid sha256:953c5a317d20e807c7a23e3bee79fbdba8ceef13f0877dd17b3cf72438a22e78
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size 964911
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