# Lora Fewshot Aerial Eval

> Evaluates parameter-efficient fine-tuning (LoRA) for cross-domain few-shot object detection on aerial imagery. It probes the model's ability to generalize to new domains with limited labeled data while mitigating overfitting. Use when the user wants to benchmark on DOTA, DIOR, or asks about evaluating this task. Reports mAP@0.5.

- Skill: `qhjqhj00/lora-fewshot-aerial-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/lora-fewshot-aerial-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/lora-fewshot-aerial-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/lora-fewshot-aerial-eval

---


# lora-fewshot-aerial-eval

> Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot Object Detection in Aerial Images — Talaoubrid et al. (2025) (arXiv:2504.06330, 2025)

## What this evaluates

Evaluates parameter-efficient fine-tuning (LoRA) for cross-domain few-shot object detection on aerial imagery. It probes the model's ability to generalize to new domains with limited labeled data while mitigating overfitting.

## Datasets

- **DOTA** — total ?; splits: train (-1); HF `HichTala/dota`
- **DIOR** — total ?; splits: train (-1); HF `HichTala/dior`

## Metrics

- `mAP@0.5` **(primary)** — range: percent
  - Mean Average Precision computed at an Intersection over Union (IoU) threshold of 0.5, following the standard COCO evaluation protocol.

## Input / output format

**Input**: RGB aerial images with bounding box annotations converted to COCO format.

**Output**: Predicted bounding boxes and class labels per image, formatted as COCO detection results for pycocotools.

## Scoring recipe

```python
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
coco_gt = COCO('ground_truth.json')
coco_dt = coco_gt.loadRes('predictions.json')
coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')
coco_eval.params.iouThrs = np.array([0.5])
coco_eval.params.maxDets = [300]
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
mAP = coco_eval.stats[0]
```

## Common pitfalls

- Few-shot training splits are randomly sampled per class; results must be averaged over 5 independent runs to account for selection variance.
- The maximum number of detections per image is explicitly capped at 300, deviating from the default COCO limit of 100.
- LoRA is applied either directly to the pre-trained weights or to the best checkpoint from an intermediate full fine-tuning phase, requiring careful checkpoint selection.

## Evidence (verbatim from paper)

> We evaluated the model performance using the mean average precision (mAP) at an IoU threshold of 0.5, which is a standard metric for object detection tasks. Given that DOTA images often contain more than 100 objects, we set the maximum detection threshold to 300 in the pycocoapi evaluation toolkit [[25]].

## Citation

```bibtex
@misc{talaoubrid2025lora,
  title={Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot Object Detection in Aerial Images},
  author={Talaoubrid et al. (2025)},
  year={2025},
  note={arXiv:2504.06330}
}
```

- arXiv: 2504.06330