Datasets:
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
Tags:
object-counting
dense-object-counting
agriculture
blueberries
point-annotations
computer-vision
License:
image imagewidth (px) 540 4.1k | image_id stringlengths 21 31 | width int64 540 4.1k | height int64 540 4.1k | count int64 3 1.93k | points listlengths 3 1.93k | box_examples_coordinates listlengths 3 10 |
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End of preview. Expand in Data Studio
DIOCblueberry
DIOCblueberry is a dense and indiscernible object counting dataset for in-field blueberry scenes. It is organized for Hugging Face as an imagefolder dataset with per-split metadata.jsonl files. Our benchmark is publicly available at https://huggingface.co/datasets/weihao-bo/DIOCblueberry.
Dataset Description
The dataset matches the paper description for DIOCblueberry:
- 6,265 high-resolution images
- 679,030 point annotations
- 108.38 annotated instances per image on average
- Dense, small, visually ambiguous blueberry targets under natural field conditions
- Point annotations plus exemplar box annotations in
box_examples_coordinates
The original archive also contained a DIOC16 directory. That directory was not used for this Hugging Face dataset because it contains 1,677 mixed-category images and does not match the paper's DIOCblueberry statistics.
Repository Layout
data/
train/
*.jpg
metadata.jsonl
val/
*.jpg
metadata.jsonl
test/
*.jpg
metadata.jsonl
annotation.json
train_val_test.json
images_class.txt
Each metadata.jsonl row contains:
file_name: image filename relative to its split folderimage_id: original image filenamewidth: image width from the annotation fileheight: image height from the annotation filecount: number of annotated blueberry points in the imagepoints: list of[x, y]point annotationsbox_examples_coordinates: exemplar box coordinates from the original annotation file
Splits
The canonical split uses the source file train_val_test_1253.json, renamed here to train_val_test.json because it covers all 6,265 images described in the paper.
| Split | Images |
|---|---|
| train | 3,759 |
| val | 1,253 |
| test | 1,253 |
| total | 6,265 |
Loading Example
from datasets import load_dataset
dataset = load_dataset("weihao-bo/DIOCblueberry", data_dir="data")
print(dataset)
print(dataset["train"][0].keys())
Notes
The annotations are intended for dense object counting. For density-map training, convert the points field into sparse impulse maps and apply the desired Gaussian kernel or adaptive kernel policy. The original annotation.json is kept at the repository root for compatibility with code that expects the paper-style source format.
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