Delete Micro-OD.py

Micro-OD.py

@@ -1,120 +0,0 @@
-"""Micro-OD: A few-shot microscopy object detection benchmark."""
-
-import json
-import os
-
-import datasets
-
-_DESCRIPTION = """\
-Micro-OD is a few-shot microscopy object detection benchmark spanning four
-biological imaging domains: BBBC (malaria parasite detection), BCCD (blood
-cell counting), LIVECell (RatC6 live-cell imaging), and NIH-3T3 (mouse
-fibroblast imaging).
-
-Two splits are provided:
-  - example: 10 images per sub-dataset (40 total) — the few-shot support set.
-  - test:    53 images per sub-dataset (212 total) — the evaluation query set.
-"""
-
-# Sorted alphabetically; used as ClassLabel names.
-ALL_CLASSES = [
-    "Gametocyte Cells",
-    "Platelets",
-    "Polygonal Cells",
-    "Red Blood Cells",
-    "Ring Cells",
-    "Round Cells",
-    "Schizont Cells",
-    "Spindle Cells",
-    "Trophozoite Cells",
-    "White Blood Cells",
-]
-
-SUBDATASETS = ["BBBC", "BCCD", "LIVECell", "NIH-3T3"]
-
-
-class MicroOD(datasets.GeneratorBasedBuilder):
-    """Micro-OD dataset loader."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "image_id": datasets.Value("string"),
-                    "subdataset": datasets.Value("string"),
-                    "objects": datasets.Sequence(
-                        feature={
-                            # COCO format: [x_min, y_min, width, height]
-                            "bbox": datasets.Sequence(
-                                datasets.Value("float32"), length=4
-                            ),
-                            "label": datasets.ClassLabel(names=ALL_CLASSES),
-                            "category": datasets.Value("string"),
-                        }
-                    ),
-                }
-            ),
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = os.path.dirname(os.path.abspath(__file__))
-        return [
-            datasets.SplitGenerator(
-                name=datasets.splits.NamedSplit("example"),
-                gen_kwargs={"split_path": os.path.join(data_dir, "example")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"split_path": os.path.join(data_dir, "test")},
-            ),
-        ]
-
-    def _generate_examples(self, split_path):
-        idx = 0
-        for subdataset in SUBDATASETS:
-            annotation_path = os.path.join(
-                split_path, subdataset, "annotation.jsonl"
-            )
-            image_base = os.path.join(split_path, subdataset)
-
-            with open(annotation_path, encoding="utf-8") as f:
-                for line in f:
-                    line = line.strip()
-                    if not line:
-                        continue
-
-                    entry = json.loads(line)
-                    image_path = os.path.join(image_base, entry["image_path"])
-
-                    bboxes, labels, categories = [], [], []
-                    for category, boxes in entry["bbox"].items():
-                        for box in boxes:
-                            x_min, y_min = box[0]
-                            x_max, y_max = box[1]
-                            # Convert [[x_min,y_min],[x_max,y_max]] → COCO [x,y,w,h]
-                            bboxes.append(
-                                [
-                                    float(x_min),
-                                    float(y_min),
-                                    float(x_max - x_min),
-                                    float(y_max - y_min),
-                                ]
-                            )
-                            labels.append(ALL_CLASSES.index(category))
-                            categories.append(category)
-
-                    yield idx, {
-                        "image": image_path,
-                        "image_id": f"{subdataset}/{entry['image_path']}",
-                        "subdataset": subdataset,
-                        "objects": {
-                            "bbox": bboxes,
-                            "label": labels,
-                            "category": categories,
-                        },
-                    }
-                    idx += 1