README.md

---
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)