---
license: cc-by-4.0
task_categories:
  - object-detection
tags:
  - yolo
  - aerial
  - drone
  - master-reservoir
size_categories:
  - 10K<n<100K
---

# Master Reservoir

**A merged, standardized aerial object-detection dataset combining DetFly and AOD4 into a single YOLO-format collection with a unified class mapping.**

## Motivation

**Master Reservoir is a unified aerial object detection dataset created by merging multiple public datasets into a common YOLO annotation format.**

The objective is to provide a single, standardized dataset for training and evaluating drone detection models while reducing inconsistencies in annotation formats, class naming, and directory structure.
## Dataset Summary

| **Total images** | 40,291 |
| **Total label files** | 40,291 |
| **Total annotated instances** | 44,868 |
| **Number of classes** | 4 |
| **Format** | YOLO (`class x_center y_center width height`, normalized 0–1) |
| **Source datasets** | DetFly, AOD4 |
| **Archive size** | 28.0 GB |
| **Version** | v1 |

Every image has a matching label file (0 missing pairs). Of the 40,291 label files, **5,101 (~12.7%) are empty** — images with no annotated objects, i.e. background/negative samples.

## Dataset Structure

This is currently a **single unsplit collection** — there is no train/val/test division yet, all images live in one `images/` folder.

```
master_reservoir/
├── images/            (40,291 files)
│   ├── aod4_test_20190925_101846_1_1_000_jpg.rf.497f3eb572bec39e6856f75fc88b3d1b.jpg
│   ├── aod4_test_20190925_101846_1_1_004_jpg.rf.0c12b3a3cad8a8cac2e429c602904551.jpg
│   └── ...
├── labels/             (40,291 files)
│   ├── aod4_test_20190925_101846_1_1_000_jpg.rf.497f3eb572bec39e6856f75fc88b3d1b.txt
│   ├── aod4_test_20190925_101846_1_1_004_jpg.rf.0c12b3a3cad8a8cac2e429c602904551.txt
│   └── ...
├── metadata/
│   └── stats.json
└── version_logs/
    └── v1.txt.txt
```

## Class Distribution

| Class ID | Instances | Images containing class | % of instances |
|---|---|---|---|
| `class_0`(Drone) | 21,168 | 20,694 | 47.2% |
| `class_1`(Bird) | 7,900 | 3,213 | 17.6% |
| `class_2`(Helicopter) | 7,900 | 5,764 | 17.6% |
| `class_3`(Airplane) | 7,900 | 5,555 | 17.6% |


`class_0` accounts for nearly half of all annotated instances — worth keeping in mind for training (e.g. class-balanced sampling or loss weighting), since `class_1`–`class_3` are ~2.7x rarer per instance and even rarer per image for `class_1`.


## Merge & Standardization Pipeline

```mermaid
flowchart LR
    A[DetFly raw] --> C[Class-mapping unification]
    B[AOD4 raw] --> C
    C --> D[YOLO label conversion]
    D --> E[Image / label validation<br/>0 missing pairs]
    E --> F[(Master Reservoir) v1]
```

## Download

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="<your-username>/master-reservoir",
    repo_type="dataset",
    local_dir="./master-reservoir"
)
```

```bash
huggingface-cli download <your-username>/master-reservoir --repo-type dataset --local-dir ./master-reservoir
```

## Usage

The dataset is currently unsplit, so create your own train/val/test split before training. Example with a simple random split:

```python
import os, random, shutil

random.seed(42)
images = os.listdir("master-reservoir/images")
random.shuffle(images)

n = len(images)
splits = {
    "train": images[:int(0.8 * n)],
    "val":   images[int(0.8 * n):int(0.9 * n)],
    "test":  images[int(0.9 * n):],
}

for split, files in splits.items():
    os.makedirs(f"master-reservoir/{split}/images", exist_ok=True)
    os.makedirs(f"master-reservoir/{split}/labels", exist_ok=True)
    for f in files:
        stem = os.path.splitext(f)[0]
        shutil.copy(f"master-reservoir/images/{f}", f"master-reservoir/{split}/images/{f}")
        shutil.copy(f"master-reservoir/labels/{stem}.txt", f"master-reservoir/{split}/labels/{stem}.txt")
```

Then point Ultralytics YOLO at it:

```yaml
# data.yaml
path: ./master-reservoir
train: train/images
val: val/images
test: test/images
names:
  0: class_0
  1: class_1
  2: class_2
  3: class_3
```

```python
from ultralytics import YOLO

model = YOLO("yolo11n.pt")
model.train(data="data.yaml", epochs=100, imgsz=640)
```

## Data Quality Notes

- Image/label pairing validated — 0 missing images, 0 missing labels.
- 5,101 label files (~12.7%) are empty (no annotated instances) — these are background images, not errors.
- Class mapping was unified across DetFly and AOD4 prior to merge; the mapping table itself isn't included in the archive yet (see below).