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license: mit
tags:
- target-plp
- power-lines
- polyline
- yolino
- cross-domain
- zero-shot
---
# Target-PLP
**Target-PLP** is a cross-domain **test-only** dataset for **zero-shot** evaluation of aerial power-line detection models.
It contains 194 aerial frames annotated with **polyline** instance labels in YOLinO format. Models are trained on [TTPLA](https://huggingface.co/datasets/V8heart/yolino-ttpla-benchmark) and evaluated on Target-PLP **without fine-tuning**.
Compared to TTPLA, Target-PLP is **more complex and challenging**: denser wire layouts, more parallel instances per frame, greater scene variation, and a different imaging domain. Annotations are **ordered polylines** (vertex sequences along each wire), not axis-aligned boxes or masks.
## Statistics
| Split | Images | Resolution | Purpose |
|-------|--------|------------|---------|
| test | **194** | 512×512 | zero-shot evaluation only |
No train or validation split is provided.
## Preprocessing
1. **Source**: CVAT XML exports from aerial video frames
2. **Resize**: each frame is stretch-resized to 512×512 (LANCZOS); polyline vertices scaled with `sx = 512/W`, `sy = 512/H`
3. **No tiling / no dual-crop** — one full frame per image
4. Frames without valid polyline instances (fewer than 2 points) are skipped
## Directory layout
```
images/test/*.png
labels/test/*.npy
```
Image and label files share the same stem, e.g. `ANAR4318_frame_000000.png` ↔ `ANAR4318_frame_000000.npy`.
## Annotation format (polyline)
Each `labels/test/<stem>.npy` is a Python dict (**format version 3**):
```python
{
"polylines": [ # one polyline per power-line instance
[[x1, y1], [x2, y2], ...], # instance 1: ordered vertices (pixels)
[[x1, y1], [x2, y2], ...], # instance 2
...
],
"instance_ids": [1, 2, ...], # unique ID per wire instance
"source_file": "<stem>",
"image_size_wh": [512, 512],
"format_version": 3
}
```
- Coordinates are in **pixel space** after the 512×512 resize
- Each polyline is an ordered sequence of 2D points along one wire
- Multiple parallel wires are separate instances with distinct `instance_ids`
## Loading example
```python
import numpy as np
from PIL import Image
root = "./Target-PLP"
stem = "ANAR4318_frame_000000"
img = Image.open(f"{root}/images/test/{stem}.png") # 512×512 RGB
label = np.load(f"{root}/labels/test/{stem}.npy", allow_pickle=True).item()
polylines = label["polylines"] # list[list[[x, y]]]
instance_ids = label["instance_ids"] # list[int]
```
## Download
```bash
hf download V8heart/Target-PLP \
--repo-type dataset \
--local-dir ./Target-PLP
```
## Related
- TTPLA training benchmark: [V8heart/yolino-ttpla-benchmark](https://huggingface.co/datasets/V8heart/yolino-ttpla-benchmark)
- Code: [V8heart/CAPSTONE](https://github.com/V8heart/CAPSTONE)
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