Deep object detection models have achieved notable successes in recent years,
but one major obstacle remains: the requirement for a large amount of training
data. Obtaining such data is a tedious process and is mainly time consuming,
leading to the exploration of new research avenues like synthetic data
generation techniques. In this study, we explore the usability of Stable
Diffusion 2.1-base for generating synthetic datasets of apple trees for object
detection and compare it to a baseline model trained on real-world data. After
creating a dataset of realistic apple trees with prompt engineering and
utilizing a previously trained Stable Diffusion model, the custom dataset was
annotated and evaluated by training a YOLOv5m object detection model to predict
apples in a real-world apple detection dataset. YOLOv5m was chosen for its
rapid inference time and minimal hardware demands. Results demonstrate that the
model trained on generated data is slightly underperforming compared to a
baseline model trained on real-world images when evaluated on a set of
real-world images. However, these findings remain highly promising, as the
average precision difference is only 0.09 and 0.06, respectively. Qualitative
results indicate that the model can accurately predict the location of apples,
except in cases of heavy shading. These findings illustrate the potential of
synthetic data generation techniques as a viable alternative to the collection
of extensive training data for object detection models.

本研究探讨了使用稳定扩散 2.1 基础库生成苹果树合成数据集的可行性，并将其与基于真实数据训练的基准模型进行了比较。通过 YOLOv5m 对模型进行评估，结果表明使用合成数据进行训练的模型在真实图像上的性能略低于基准模型，但这些结果具有高度的前景，证明了合成数据生成技术作为目标检测模型训练数据收集的一种可行替代方法的潜力。

数据集合成效果探索：在果园中应用于苹果检测

Exploring the Effectiveness of Dataset Synthesis: An application of  Apple Detection in Orchards

Despite the notable accomplishments of deep object detection models, a major
challenge that persists is the requirement for extensive amounts of training
data. The process of procuring such real-world data is a laborious undertaking,
which has prompted researchers to explore new avenues of research, such as
synthetic data generation techniques. This study presents a framework for the
generation of synthetic datasets by fine-tuning pretrained stable diffusion
models. The synthetic datasets are then manually annotated and employed for
training various object detection models. These detectors are evaluated on a
real-world test set of 331 images and compared against a baseline model that
was trained on real-world images. The results of this study reveal that the
object detection models trained on synthetic data perform similarly to the
baseline model. In the context of apple detection in orchards, the average
precision deviation with the baseline ranges from 0.09 to 0.12. This study
illustrates the potential of synthetic data generation techniques as a viable
alternative to the collection of extensive training data for the training of
deep models.

本研究提出了一种通过微调预训练的稳定扩散模型生成合成数据集的框架，并用于对象检测模型的训练，研究结果表明，在苹果果园检测中，合成数据训练的对象检测模型的性能与真实世界图像训练得到的基线模型相似，这表明了合成数据生成技术作为深度模型训练的可行替代方法的潜力。

大数据神话：使用扩散模型进行数据集生成以训练深度检测模型

The Big Data Myth: Using Diffusion Models for Dataset Generation to  Train Deep Detection Models

We propose Rank & Sort (RS) Loss, a ranking-based loss function to train deep
object detection and instance segmentation methods (i.e. visual detectors). RS
Loss supervises the classifier, a sub-network of these methods, to rank each
positive above all negatives as well as to sort positives among themselves with
respect to (wrt.) their localisation qualities (e.g. Intersection-over-Union -
IoU). To tackle the non-differentiable nature of ranking and sorting, we
reformulate the incorporation of error-driven update with backpropagation as
Identity Update, which enables us to model our novel sorting error among
positives. With RS Loss, we significantly simplify training: (i) Thanks to our
sorting objective, the positives are prioritized by the classifier without an
additional auxiliary head (e.g. for centerness, IoU, mask-IoU), (ii) due to its
ranking-based nature, RS Loss is robust to class imbalance, and thus, no
sampling heuristic is required, and (iii) we address the multi-task nature of
visual detectors using tuning-free task-balancing coefficients. Using RS Loss,
we train seven diverse visual detectors only by tuning the learning rate, and
show that it consistently outperforms baselines: e.g. our RS Loss improves (i)
Faster R-CNN by ~ 3 box AP and aLRP Loss (ranking-based baseline) by ~ 2 box AP
on COCO dataset, (ii) Mask R-CNN with repeat factor sampling (RFS) by 3.5 mask
AP (~ 7 AP for rare classes) on LVIS dataset; and also outperforms all
counterparts. Code is available at: this https URL

本文提出了 Rank & Sort Loss，旨在解决训练深度目标检测和实例分割模型中存在的类别不平衡问题，通过该算法对分类器进行排名和排序，进一步提高了正样本的优先级和定位质量，且无需使用辅助头，经实验证明该算法优于传统的基线算法。