Abin0008/real-infrared-maritime-vessel-dataset

Real Infrared Maritime Vessel Dataset

Real infrared imagery of maritime vessels. The dataset is provided in three forms — full-frame detection images, per-object classification crops, and a hand-curated subset.

Classes (7): liner, bulk carrier, warship, sailboat, canoe, container ship, fishing boat.

Layout

real-infrared-maritime-vessel-dataset/
├── original/          Full-frame IR images + XML bounding-box labels (detection)
│   ├── images/{train,test}/*.jpg      (9,402 images)
│   ├── labels/train_xml/*.xml
│   └── labels.txt
├── cropped/           Per-object classification crops by class 0–6
│   ├── train/{0..6}/*.jpg             (25,045)
│   ├── val/{0..6}/*.jpg               (700)
│   ├── test/{0..6}/*.jpg              (700)
│   ├── labels.txt, {train,val,test}.csv, class-*.npy
└── cropped-curated/   Hand-picked subset of the crops
    ├── train/{0..6}/*.jpg             (1,750)
    ├── val/{0..6}/*.jpg               (175)
    └── test/{0..6}/*.jpg              (175)

Note: synthetic / enhanced variants (*_enhanced_*, prompt-conditioned, and upsampled splits) are excluded — this repo holds only real IR data.

Subsets

original/ — detection

Full-frame single-channel infrared images with Pascal-VOC-style XML annotations (bounding boxes). Use for detection or as the source for generating crops.

cropped/ — classification

Each labelled object in the original/ detection images is cropped out to build this classification set. The annotation bounding box is expanded to add surrounding context (expanded_bbox), that region is cut from the full-frame image, and the crop is filed under its class-id folder within a train/val/test split. Class ids here are assigned alphabetically by class name (see cropped/labels.txt), so they differ from the original/ label order. Class distribution is imbalanced.

class	id	train
bulk carrier	0	1,740
canoe	1	4,735
container ship	2	495
fishing boat	3	8,918
liner	4	1,233
sailboat	5	5,577
warship	6	2,347

Each split ships a matching {train,val,test}.csv describing every crop, with columns:

column	description
filename	crop image file name
instance_id	unique id of the object instance
label	class name
image_size	(W, H) of the source full-frame image
truncated	1 if the object is cut off at the image border, else 0
bbox	original annotation box [x_min, y_min, x_max, y_max] in the source image
expanded_bbox	the context-expanded box actually used for the crop
expanded_bbox_size	[w, h] of the expanded crop
label_id	numeric class id (alphabetical, matches the folder name)

cropped-curated/ — classification (high quality subset)

A manually curated, class-balanced-ish subset of the crops (1,750 / 175 / 175 train/val/test), organised by class id directly under each split. This is the subset used for the linear-evaluation and fine-tuning experiments in the parent project.

Class id → name

original/ label order: 0 liner · 1 bulk carrier · 2 warship · 3 sailboat · 4 canoe · 5 container ship · 6 fishing boat

cropped/ and cropped-curated/ label order (alphabetical): 0 bulk carrier · 1 canoe · 2 container ship · 3 fishing boat · 4 liner · 5 sailboat · 6 warship