dataset uploaded by roboflow2huggingface package

README.md

@@ -3,10 +3,13 @@ task_categories:
 - object-detection
 tags:
 - roboflow
+- roboflow2huggingface
+- Biology
 ---
 
-### Roboflow Dataset Page
-[https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3](https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface)
+<div align="center">
+  <img width="640" alt="keremberke/blood-cell-object-detection" src="https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/thumbnail.jpg">
+</div>
 
 ### Dataset Labels
 
@@ -14,6 +17,34 @@ tags:
 ['platelets', 'rbc', 'wbc']
 ```
 
+
+### Number of Images
+
+```json
+{'train': 255, 'test': 36, 'valid': 73}
+```
+
+
+### 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/blood-cell-object-detection", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface](https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface?ref=roboflow2huggingface)
+
 ### Citation
 
 ```
@@ -27,7 +58,7 @@ tags:
     publisher = { Roboflow },
     year = { 2022 },
     month = { nov },
-    note = { visited on 2022-12-31 },
+    note = { visited on 2023-01-18 },
 }
 ```
 

blood-cell-object-detection.py

@@ -5,7 +5,7 @@ import os
 import datasets
 
 
-_HOMEPAGE = "https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3"
+_HOMEPAGE = "https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface"
 _LICENSE = "Public Domain"
 _CITATION = """\
 @misc{ blood-cell-detection-1ekwu_dataset,
@@ -18,21 +18,52 @@ _CITATION = """\
     publisher = { Roboflow },
     year = { 2022 },
     month = { nov },
-    note = { visited on 2022-12-31 },
+    note = { visited on 2023-01-18 },
 }
 """
-_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 BLOODCELLOBJECTDETECTIONConfig(datasets.BuilderConfig):
+    """Builder Config for blood-cell-object-detection"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for blood-cell-object-detection.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(BLOODCELLOBJECTDETECTIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
 class BLOODCELLOBJECTDETECTION(datasets.GeneratorBasedBuilder):
+    """blood-cell-object-detection object detection dataset"""
+
     VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        BLOODCELLOBJECTDETECTIONConfig(
+            name="full",
+            description="Full version of blood-cell-object-detection dataset.",
+            data_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",
+            },
+        ),
+        BLOODCELLOBJECTDETECTIONConfig(
+            name="mini",
+            description="Mini version of blood-cell-object-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/data/valid-mini.zip",
+            },
+        )
+    ]
 
     def _info(self):
         features = datasets.Features(
@@ -59,7 +90,7 @@ class BLOODCELLOBJECTDETECTION(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        data_files = dl_manager.download_and_extract(_URLS)
+        data_files = dl_manager.download_and_extract(self.config.data_urls)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -92,7 +123,7 @@ class BLOODCELLOBJECTDETECTION(datasets.GeneratorBasedBuilder):
 
         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)
@@ -100,12 +131,12 @@ class BLOODCELLOBJECTDETECTION(datasets.GeneratorBasedBuilder):
         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"]}
+        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 image_id_to_image:
-                image = image_id_to_image[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"]]
                 ]

data/test.zip

@@ -1,3 +1,3 @@
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-oid sha256:d2239ef3b1d9574edd0dc7b4dd2b12bcab2cb6c7fad69d750b90de74095af6e1
+oid sha256:be611adbf8186ade4d6a0f2867073b98ea23b619bd198066096231a37893b2e4
 size 471118

data/train.zip

@@ -1,3 +1,3 @@
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-oid sha256:42081183d4a75b2f6b43ab7b572650dd924afb96c21d7f1468ad29feca50e59e
+oid sha256:35017e0fb12a34dcb1931dfe7cf6f63cc7ea15169e27586bcbe82f8f8135b1b4
 size 3361545

data/valid-mini.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:059f87bf017891129db2d4a368de6f9d4759112e356be7d5902b641811aa8ff0
+size 41369

data/valid.zip

@@ -1,3 +1,3 @@
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-oid sha256:ba16c63bfa5bc08eebd2ef0f8eea7d8b5acdbbddc55ad3d07631c958238359e1
+oid sha256:e801c70b89af6ad9439d48f972fb6b755ec4fa5c12278753c342dbc22905cfeb
 size 959009

split_name_to_num_samples.json

@@ -0,0 +1 @@
+{"train": 255, "test": 36, "valid": 73}