---
task_categories:
  - image-classification
  - object-detection
tags:
  - infrared
  - maritime vessel
pretty_name: Real Infrared Maritime Vessel Dataset
size_categories:
  - 10K<n<100K
---

# 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`