Create README.md

README.md

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+---
+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).