Datasets:

mapo80
/

stamps

license: cc-by-4.0
task_categories:
  - object-detection
tags:
  - stamp-detection
  - document-analysis
  - object-detection
  - yolo
  - computer-vision
pretty_name: Clean Core Stamp Detection Dataset
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: string
  splits:
    - name: train
      num_examples: 6283
    - name: validation
      num_examples: 785
    - name: test
      num_examples: 786

Clean Core Stamp Detection Dataset

A cleaned document-stamp detection dataset built from curated raw sources only. This version replaces the previous mapo80/stamps release and removes noisy or off-target sources such as postage stamps, shape/identity datasets, corrupted multi-class remaps, and duplicated negatives.

Overview

Parameter	Value
Task	Object detection
Classes	1 (`stamp`, class id 0)
Total images	7854
Positive images	6608
Negative images	1246
Total bounding boxes	12659
Annotation format	YOLO txt (`class x_center y_center width height`)

Splits

Split	Total Images	Positive	Negative	Bounding Boxes
Train	6283	5287	996	10046
Val	785	660	125	1298
Test	786	661	125	1315

What Changed

Kept only in-scope document stamp sources
Filtered stamp_detection_stampa to the raw stamp class only
Removed postage-stamp, shape, identity, and corrupted mixed-class sources
Rebuilt negatives from RVL-CDIP and FUNSD
Dropped severe negative outliers (blank pages, almost-black pages, heavily degraded scans)
Rebuilt train/val/test from scratch from data/raw

Included Sources

Positive sources

stamp_detection_jsam: 4385 images
yolo_stamp_classify: 1397 images
stamp_detectation_marcos: 365 images
stamp_detection_stampa (filtered): 461 images

Negative sources

neg_rvl_cdip: 1048 images
neg_funsd: 198 images

Excluded Source Families

detect_postage_stamp
stamp_class
stamp_shape
stamp_individual
stamp_recognition
stamp_detection_shujing
stamp_warisara
staver_yolo
neg_tobacco3482

Directory Structure

The repository stores the dataset as zip archives:

train.zip
val.zip
test.zip
dataset.yaml

Each zip contains:

images/<split>/
labels/<split>/

`dataset.yaml`

path: .
train: images/train
val: images/val
test: images/test

nc: 1
names:
  - stamp

Notes

Empty label files are valid negatives
This benchmark is intentionally narrower and cleaner than the previous release
It is optimized for document-level stamp detection, not postage stamps or stamp classification