Update parquet files

README.dataset.txt

@@ -1,39 +0,0 @@
-# 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

@@ -1,58 +0,0 @@
----
-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

@@ -1,24 +0,0 @@
-
-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

@@ -1,121 +0,0 @@
-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 → default/blood-cell-object-detection-test.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d2239ef3b1d9574edd0dc7b4dd2b12bcab2cb6c7fad69d750b90de74095af6e1
-size 471118
+oid sha256:e89d58a4bb3e814a909921cd4a3881f333b2fb796b7d9a72e31da48a77d3398e
+size 478483

data/train.zip → default/blood-cell-object-detection-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:42081183d4a75b2f6b43ab7b572650dd924afb96c21d7f1468ad29feca50e59e
-size 3361545
+oid sha256:b320adb5aa7a621f6e6e9d16bea46ecc67f9ff8e0aea1a43c87017de203825ec
+size 3375570

data/valid.zip → default/blood-cell-object-detection-validation.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ba16c63bfa5bc08eebd2ef0f8eea7d8b5acdbbddc55ad3d07631c958238359e1
-size 959009
+oid sha256:953c5a317d20e807c7a23e3bee79fbdba8ceef13f0877dd17b3cf72438a22e78
+size 964911