license: cc-by-4.0
task_categories:
- object-detection
tags:
- finedet
FineOpenimages — Open Images V7 boxed subset in the unified detection format
Source: official Open Images bbox CSVs + CVDF-hosted image tars (open-images-dataset S3 bucket).
Converted by the finedet project into a unified, AutoTrain-compatible layout:
image / width / height / objects{bbox, category} with COCO-format
[x, y, w, h] boxes in absolute pixels. Boxes are clipped to the image and
empty boxes dropped; category ids are densified per the category tables
below.
Box format
objects.bbox follows the COCO convention: [x, y, w, h] in absolute pixels,
origin at the image's top-left corner.
License
Annotations: CC BY 4.0 (Google LLC). Images: listed as CC BY 2.0 individually; Google does not warrant the license status of each image. https://storage.googleapis.com/openimages/web/factsfigures_v7.html
Example images
Boxes are colored by category: near-transparent fill, opaque outline.
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Conversion notes
Only images with box annotations are included. Normalized XMin/XMax/YMin/YMax converted to absolute COCO xywh using the decoded image size. IsGroupOf boxes are kept. Attribute flags (occluded/truncated/depiction/inside) are not carried over.
Splits
- test: 112194 images
- train: 1743042 images
- validation: 37306 images
Categories
This dataset has 601 categories. The full category table has moved to categories.csv (columns: id, original_id, name).
Training with transformers
The boxes are already in the absolute-pixel COCO [x, y, w, h] format that
AutoImageProcessor expects, so fine-tuning a detector needs no bbox
conversion:
import torch
from datasets import load_dataset
from transformers import (AutoImageProcessor, AutoModelForObjectDetection,
Trainer, TrainingArguments)
ds = load_dataset("finedet/openimages")
obj_feat = ds["train"].features["objects"]
if hasattr(obj_feat, "feature"):
obj_feat = obj_feat.feature
cat_feat = obj_feat["category"]
names = (cat_feat.feature if hasattr(cat_feat, "feature") else cat_feat).names
checkpoint = "facebook/detr-resnet-50"
processor = AutoImageProcessor.from_pretrained(checkpoint)
model = AutoModelForObjectDetection.from_pretrained(
checkpoint,
id2label=dict(enumerate(names)),
label2id={n: i for i, n in enumerate(names)},
ignore_mismatched_sizes=True,
)
def transform(batch):
images = [img.convert("RGB") for img in batch["image"]]
annotations = [
{"image_id": i,
"annotations": [
{"bbox": box, "category_id": cat, "area": box[2] * box[3], "iscrowd": 0}
for box, cat in zip(objs["bbox"], objs["category"])
]}
for i, objs in enumerate(batch["objects"])
]
return processor(images=images, annotations=annotations, return_tensors="pt")
def collate(batch):
return {"pixel_values": torch.stack([x["pixel_values"] for x in batch]),
"labels": [x["labels"] for x in batch]}
trainer = Trainer(
model=model,
args=TrainingArguments(output_dir="out", per_device_train_batch_size=4,
num_train_epochs=10, learning_rate=1e-5,
remove_unused_columns=False),
train_dataset=ds["train"].with_transform(transform),
data_collator=collate,
)
trainer.train()