MCF-Endface

End-face images of 4-core multi-core fibers (MCF) captured on a fusion splicer, annotated with the position and radius of each core and the position of the orientation marker. The dataset is intended for training and evaluating models that locate cores and markers, which is the input needed to rotationally align two MCFs before splicing.

At a glance

Images	216 (108 X/Y pairs)
Image size	806 × 802 px, cropped and centred on the fiber cladding
Cores	864 (4 per image)
Markers	216 (1 per image)
Fiber types	Normal: 209 · Trenched: 7
Capture period	2026-03-05 → 2026-10-16 (27 capture days)
Split	train only

Repository layout

annotations.json        # all annotations, COCO-style (single file)
images/
├── metadata.jsonl      # same annotations, one line per image (used by load_dataset)
├── 000001.jpg
├── 000002.jpg
└── ...
MCFDataset.py           # small loader for annotations.json + images (no `datasets` needed)

annotations.json and images/metadata.jsonl contain the same annotations in two layouts.

Annotation fields

Each image has one record:

Field	Type	Description
id	int	Unique image id (images are numbered in capture order)
file_name	string	Image file in images/, e.g. 000001.jpg
width, height	int	Image size in pixels (806 × 802)
date_captured	string	Capture time, ISO 8601
side	string	X or Y, the two fiber end-faces imaged for one splice
pair_image_id	int	id of the other side of the same splice
type	string	Fiber type: Normal or Trenched
cores	list	4 cores, each {"position_x", "position_y", "radius"} in pixels
marker	object	Orientation marker {"position_x", "position_y"} in pixels

Coordinates: the origin is the top-left corner of the image, x increases to the right and y increases downwards. The fiber centre is at about (403, 401).

Example record:

{
  "id": 1, "file_name": "000001.jpg", "width": 806, "height": 802,
  "date_captured": "2026-03-05T18:17:22.145948", "side": "X", "pair_image_id": 2, "type": "Normal",
  "cores": [
    {"position_x": 205, "position_y": 395, "radius": 26},
    {"position_x": 405, "position_y": 199, "radius": 25},
    {"position_x": 596, "position_y": 397, "radius": 23},
    {"position_x": 401, "position_y": 592, "radius": 23}
  ],
  "marker": {"position_x": 668, "position_y": 256}
}

Usage

With 🤗 datasets

from datasets import load_dataset

ds = load_dataset("OCEAN-Lab/MCF-Endface", split="train")
sample = ds[0]
sample["image"]    # PIL image
sample["cores"]    # [{"position_x", "position_y", "radius"}, ...]
sample["marker"]   # {"position_x", "position_y"}

With the raw files and MCFDataset

from huggingface_hub import snapshot_download

root = snapshot_download("OCEAN-Lab/MCF-Endface", repo_type="dataset",
                         allow_patterns=["annotations.json", "images/*.jpg", "MCFDataset.py"])

import sys; sys.path.insert(0, root)
from MCFDataset import MCFDataset

ds = MCFDataset(root)
item = ds[0]
item["image"]                 # BGR numpy array (cv2.imread, IMREAD_UNCHANGED)
item["cores"], item["marker"] # same lists/dicts as annotations.json
x_item, y_item = ds.get_pair(0)   # both sides of one splice
ds.get_by_id(5)               # look up by image id

MCFDataset needs opencv-python. It also needs huggingface_hub if you create it without a path, in which case it downloads the dataset itself.

With plain Python

import json
from pathlib import Path
from PIL import Image

data = json.load(open(Path(root) / "annotations.json"))
for record in data["images"]:
    image = Image.open(Path(root) / "images" / record["file_name"])
    cores, marker = record["cores"], record["marker"]

How the data was collected

The images were captured on a fusion splicer as part of an automated MCF splicing setup. The images were taken from a Fujikura FSM 100P+ PM splicer. Each raw end-face image was cropped around the fiber cladding and resized to 806 × 802. Cores and markers were marked by an operator in a custom Python GUI before being saved.

License

CC BY-SA 4.0