COCO dataset and neural network weights for micro-FTIR particle detection on filters.

The IMPTOX project has received funding from the EU's H2020 framework programme for research and innovation under grant agreement n. 965173. Imptox is part of the European MNP cluster on human health. More information about the project here. Description: This repository includes the trained weights and a custom COCO-formatted dataset used for developing and testing a Faster R-CNN R_50_FPN_3x object detector, specifically designed to identify particles in micro-FTIR filter images. Contents: Weights File (neuralNetWeights_V3.pth): Format: .pth Description: This file contains the trained weights for a Faster R-CNN model with a ResNet-50 backbone and a Feature Pyramid Network (FPN), trained for 3x schedule. These weights are specifically tuned for detecting particles in micro-FTIR filter images. Custom COCO Dataset (uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip): Format: .zip Description: This zip archive contains a custom COCO-formatted dataset, including JPEG images and their corresponding annotation file. The dataset consists of images of micro-FTIR filters with annotated particles. Contents: Images: JPEG format images of micro-FTIR filters. Annotations: A JSON file in COCO format providing detailed annotations of the particles in the images. Management: The dataset can be managed and manipulated using the Pycocotools library, facilitating easy integration with existing COCO tools and workflows. Applications: The provided weights and dataset are intended for researchers and practitioners in the field of microscopy and particle detection. The dataset and model can be used for further training, validation, and fine-tuning of object detection models in similar domains. Usage Notes: The neuralNetWeights_V3.pth file should be loaded into a PyTorch model compatible with the Faster R-CNN architecture, such as Detectron2. The contents of uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip should be extracted and can be used with any COCO-compatible object detection framework for training and evaluation purposes. Code can be found on the related Github repository.IMPTOX项目已获得欧盟“地平线2020”（Horizon 2020）研究与创新框架计划资助，资助协议编号为965173。Imptox为欧洲MNP（微纳米塑料，Micro Nano Plastics）人类健康研究集群的组成部分。 更多项目详情请见此处。 ### 数据集描述：本仓库包含用于开发与测试Faster R-CNN R_50_FPN_3x目标检测器的预训练权重与自定义COCO格式（公共对象上下文格式，Common Objects in Context）数据集，该检测器专为识别显微傅里叶变换红外光谱（micro-Fourier Transform Infrared，micro-FTIR）滤膜图像中的颗粒而设计。 #### 内容清单 ##### 权重文件（neuralNetWeights_V3.pth） - 格式：.pth - 描述：本文件包含基于ResNet-50骨干网络与特征金字塔网络（Feature Pyramid Network，FPN）的Faster R-CNN模型预训练权重，采用3x训练调度策略完成训练。该权重已针对micro-FTIR滤膜图像的颗粒检测任务进行专属调优。 ##### 自定义COCO格式数据集（uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip） - 格式：.zip - 描述：该压缩归档包含自定义COCO格式数据集，内含JPEG图像及对应标注文件。本数据集由带有颗粒标注的micro-FTIR滤膜图像组成。 - 图像：micro-FTIR滤膜的JPEG格式图像。 - 标注：采用COCO格式的JSON文件，用于详细标注图像中的颗粒。 #### 数据集管理 可使用Pycocotools库对该数据集进行管理与操作，便于与现有COCO工具及工作流无缝集成。 #### 应用场景 本项目提供的权重与数据集面向显微学与颗粒检测领域的研究人员与从业者，可用于同类应用场景下目标检测模型的进一步训练、验证与微调。 #### 使用注意事项 1. 需将neuralNetWeights_V3.pth加载至与Faster R-CNN架构兼容的PyTorch模型中，例如Detectron2。 2. 需解压uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip的内容，可配合任意兼容COCO的目标检测框架用于训练与评估。 相关代码可在关联的GitHub仓库中获取。