adR6x/pest_detection_dataset

---
license: mit
task_categories:
- object-detection
tags:
- pest-detection
- coco
- synthetic
- surveillance
- bounding-box
- mouse-detection
- rat-detection
- cockroach-detection
- kitchen
- indoor
- video-frames
- synthetic-data
- places365
- food-safety
- pest-control
- computer-vision
- temporal
- detr
- yolo
size_categories:
- 10K<n<100K
---

# Pest Detection Dataset (Synthetic, COCO Format)

A synthetic dataset for pest detection in kitchen/indoor surveillance scenarios.
All videos and annotations are procedurally generated using a custom pipeline.

## Demo — Job `64d68645` (1 mouse · 1 rat · 1 cockroach)


*Bounding boxes: 🔴 mouse &nbsp; 🟢 rat &nbsp; 🔵 cockroach*

---

## Dataset Statistics

| Split | Jobs | Frames | Annotations | 🐭 Mouse | 🐀 Rat | 🪳 Cockroach | Empty Jobs |
|-----------|-------:|--------:|------------:|---------:|--------:|-------------:|------------:|
| train | 1,460 | 36,500 | 55,650 | 16,200 | 10,975 | 28,475 | 368 (25.2%) |
| val | 298 | 7,450 | 11,650 | 3,225 | 2,375 | 6,050 | 76 (25.5%) |
| test | 308 | 7,700 | 11,575 | 3,575 | 2,025 | 5,975 | 84 (27.3%) |
| **total** |**2,066**|**51,650**| **78,875**|**23,000**|**15,375**| **40,500**| **528 (25.6%)** |

---

## Pest Categories

| ID | Name | Description |
|----|------|-------------|
| 1 | mouse | House mouse |
| 2 | rat | Common rat |
| 3 | cockroach | Cockroach |

---

## Dataset Structure

```
├── annotations/
│ ├── train.json # COCO-format annotations for train split
│ ├── val.json # COCO-format annotations for val split
│ └── test.json # COCO-format annotations for test split
├── images/
│ ├── train/
│ │ └── {job_id}/ # One folder per video job (~25 frames each)
│ │ ├── frame_0001.png
│ │ ├── frame_0010.png
│ │ └── ...
│ ├── val/
│ │ └── {job_id}/
│ └── test/
│ └── {job_id}/
├── generated_state.json # Full metadata for all 2,066 generated jobs
└── demo.mp4 # Demo video with bounding box overlays
```

### Why job-ID subfolders?
Frames from the same synthetic video are grouped under one `{job_id}` folder.
This preserves temporal structure — consecutive frames in a folder belong to the same video sequence — enabling temporal models and frame-sequence dataloaders without needing extra metadata.

---

## Annotation Format (COCO)

Each annotation JSON follows the standard COCO object detection format:

```json
{
"images": [
{
"id": 1,
"file_name": "64d68645/frame_0001.png",
"width": 640,
"height": 480
}
],
"annotations": [
{
"id": 1,
"image_id": 1,
"category_id": 1,
"bbox": [x, y, width, height],
"area": 1234.5,
"iscrowd": 0
}
],
"categories": [
{"id": 1, "name": "mouse"},
{"id": 2, "name": "rat"},
{"id": 3, "name": "cockroach"}
]
}
```

Note: `file_name` includes the `{job_id}/` prefix — prepend `images/{split}/` to get the full path.

---

## Loading the Dataset

```python
from huggingface_hub import snapshot_download
import json, os
from PIL import Image

# Download the dataset
dataset_dir = snapshot_download("adR6x/pest_detection_dataset", repo_type="dataset")

# Load train annotations
with open(os.path.join(dataset_dir, "annotations", "train.json")) as f:
coco = json.load(f)

# Load an image
img_info = coco["images"][0]
img_path = os.path.join(dataset_dir, "images", "train", img_info["file_name"])
img = Image.open(img_path)
```

---

## generated_state.json

Each entry in `generated_state.json` describes one generated video job:

```json
{
"job_id": "64d68645",
"split": "train",
"mouse_count": 1,
"rat_count": 1,
"cockroach_count": 1,
"length_of_video_seconds": 24.0,
"fps": 10,
"frames_dir": "images/train/64d68645",
"labels_dir": "annotations/train.json",
...
}
```

Use this to filter jobs by pest composition, split, or other metadata.

---

## Data Sources

| Asset | Source | Details |
|-------|--------|---------|
| Kitchen backgrounds | [Places365](http://places2.csail.mit.edu/) + manually curated | 70 kitchen images selected from Places365 to cover diverse kitchen layouts, lighting conditions, and viewpoints |
| Pest models & animation | Custom synthetic pipeline | 3D pest models (mouse, rat, cockroach) procedurally animated and composited onto background images |
| Bounding box annotations | Auto-generated | Derived directly from the synthetic compositing process — no manual labelling required |

---

## Frame Sampling

Frames are sampled at every 10th frame from the original video (`--every_n 10` default), plus frame 1. Each job contributes ~25 frames at 640×480 resolution.