dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# Blood Cell Detection > 2022-10-27 4:01pm
+https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu
+
+Provided by a Roboflow user
+License: Public Domain
+
+# Overview
+ 
+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). 
+
+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).
+
+Here's a class count from Roboflow's Dataset Health Check:
+
+![BCCD health](https://i.imgur.com/BVopW9p.png)
+
+And here's an example image:
+
+![Blood Cell Example](https://i.imgur.com/QwyX2aD.png)
+
+`Fork` this dataset (upper right hand corner) to receive the raw images, or (to save space) grab the 500x500 export.
+
+# Use Cases
+
+This is a small scale object detection dataset, commonly used to assess model performance. It's a first example of medical imaging capabilities.
+
+# Using this Dataset
+
+We're releasing the data as public domain. Feel free to use it for any purpose.
+
+It's not required to provide attribution, but it'd be nice! :)
+
+# About Roboflow
+
+[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. 
+
+Developers reduce 50% of their boilerplate code when using Roboflow's workflow, automate annotation quality assurance, save training time, and increase model reproducibility.
+
+#### [![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png =350x)](https://roboflow.ai)

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+---
+
+### Roboflow Dataset Page
+https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3
+
+### Dataset Labels
+
+```
+['platelets', 'rbc', 'wbc']
+```
+
+### Citation
+
+```
+@misc{ blood-cell-detection-1ekwu_dataset,
+    title = { Blood Cell Detection Dataset },
+    type = { Open Source Dataset },
+    author = { Team Roboflow },
+    howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } },
+    url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { nov },
+    note = { visited on 2022-12-31 },
+}
+```
+
+### License
+Public Domain
+
+### Dataset Summary
+This dataset was exported via roboflow.com on November 4, 2022 at 7:46 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 364 images.
+Cells 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 (Stretch)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Blood Cell Detection - v3 2022-10-27 4:01pm
+==============================
+
+This dataset was exported via roboflow.com on November 4, 2022 at 7:46 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 364 images.
+Cells 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 (Stretch)
+
+No image augmentation techniques were applied.
+
+

blood-cell-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3"
+_LICENSE = "Public Domain"
+_CITATION = """\
+@misc{ blood-cell-detection-1ekwu_dataset,
+    title = { Blood Cell Detection Dataset },
+    type = { Open Source Dataset },
+    author = { Team Roboflow },
+    howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } },
+    url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { nov },
+    note = { visited on 2022-12-31 },
+}
+"""
+_URLS = {
+    "train": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/test.zip",
+}
+
+_CATEGORIES = ['platelets', 'rbc', 'wbc']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class BLOODCELLOBJECTDETECTION(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

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2239ef3b1d9574edd0dc7b4dd2b12bcab2cb6c7fad69d750b90de74095af6e1
+size 471118

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42081183d4a75b2f6b43ab7b572650dd924afb96c21d7f1468ad29feca50e59e
+size 3361545

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ba16c63bfa5bc08eebd2ef0f8eea7d8b5acdbbddc55ad3d07631c958238359e1
+size 959009