Few-Shot目标检测数据集-已经整理成MS-COCO数据格式-含60000+张图-可直接用于目标检测算法训练.zip

# Few-Shot-Object-Detection-Dataset ## Introduction: Few-Shot Object Detection Dataset (FSOD) is a high-diverse dataset specifically designed for few-shot object detection and intrinsically designed to evaluate thegenerality of a model on novel categories.  To build this dataset, we first summarize a label system from ImageNet and OpenImage. By merging the leaf labels in their original label trees, group those of same semantics, such as the ice bear and polar bear, to one category, and remove some semantics that does not belong to any leaf categories. Then, we remove the images with bad labeling quality and those with boxes of improper size. We remove boxes smaller than 0.05% of image size which is usually in bad visual quality and unsuitable to serve as support examples. We follow the few-shot learning principle to split our data into the training set and test set whose categories has no overlap. We construct the training set with categories in MS COCO Dataset and ImageNet Dataset in case researchers need a pretraining stage. We then split the test set which contains 200 categories by choosing those with the largest distance with existing training categories, where the distance calculates the shortest path that connects the senses of two phrase in the is-a taxonomy. The remaining categories are merged into the training set that in total contains 800 categories. In all, we construct a dataset of 1000 categories with very clear category split for training and testing, where 531 categories come from ImageNet Dataset and 469 from Open Image Dataset. ## Download FSOD: Please see the `datasets.txt` ## FSOD Dataset Format and Usage: The FSOD dataset is in MS COCO format (under debug), so place the FSOD dataset as the COCO dataset. And you can use the FSOD dataset like COCO dataset. Put the FSOD dataset as the following structure: ``` YOUR_PATH └── your code dir ├── your code ├── ... │  └── datasets ├──── fsod | ├── annotations │ │ ├── fsod_train.json │   │  └── fsod_test.json │   └── images │ ├── part_1 │ └── part_2 │  ├──── coco | ├── annotations │ │ ├── instances_train2017.json │   │  └── instances_val2017.json │   └── images │  └── other datasets ``` ## Dataset Summary: | | Train | Test | | ---------- | :-----------: | :-----------: | |No. Class | 800 | 200 | |No. Image | 52350 | 14152 | |No. Box | 147489 | 35102 | |Avg No. Box / Img | 2.82 | 2.48 | |Min No. Img / Cls | 22 | 30 | |Max No. Img / Cls | 208 | 199 | |Avg No. Img / Cls | 75.65 | 74.31 | |Box Size | [6, 6828] | [13, 4605] | |Box Area Ratio | [0.0009, 1] | [0.0009, 1] | |Box W/H Ratio | [0.0216, 89] | [0.0199, 51.5] |