课题实验基于YOLOv8+sahi策略(切片、微调、推理)实现遥感数据集上小目标检测python源码.zip

<div align="center"> <h1> SAHI: Slicing Aided Hyper Inference </h1> <h4> A lightweight vision library for performing large scale object detection & instance segmentation </h4> <h4> <img width="700" alt="teaser" src="https://raw.githubusercontent.com/obss/sahi/main/resources/sliced_inference.gif"> </h4> <div> <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi" alt="downloads"></a> <a href="https://pepy.tech/project/sahi"><img src="https://pepy.tech/badge/sahi/month" alt="downloads"></a> <br> <a href="https://badge.fury.io/py/sahi"><img src="https://badge.fury.io/py/sahi.svg" alt="pypi version"></a> <a href="https://anaconda.org/conda-forge/sahi"><img src="https://anaconda.org/conda-forge/sahi/badges/version.svg" alt="conda version"></a> <a href="https://github.com/obss/sahi/actions/workflows/package_testing.yml"><img src="https://github.com/obss/sahi/actions/workflows/package_testing.yml/badge.svg" alt="package testing"></a> <br> <a href="https://ieeexplore.ieee.org/document/9897990"><img src="https://img.shields.io/badge/DOI-10.1109%2FICIP46576.2022.9897990-orange.svg" alt="ci"></a> <br> <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img src="https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg" alt="HuggingFace Spaces"></a> ​ </div> </div> ## <div align="center">Overview</div> Object detection and instance segmentation are by far the most important applications in Computer Vision. However, the detection of small objects and inference on large images still need to be improved in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities. | Command | Description | |---|---| | [predict](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-command-usage) | perform sliced/standard video/image prediction using any [yolov5](https://github.com/ultralytics/yolov5)/[mmdet](https://github.com/open-mmlab/mmdetection)/[detectron2](https://github.com/facebookresearch/detectron2)/[huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) model | | [predict-fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#predict-fiftyone-command-usage) | perform sliced/standard prediction using any [yolov5](https://github.com/ultralytics/yolov5)/[mmdet](https://github.com/open-mmlab/mmdetection)/[detectron2](https://github.com/facebookresearch/detectron2)/[huggingface](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) model and explore results in [fiftyone app](https://github.com/voxel51/fiftyone) | | [coco slice](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-slice-command-usage) | automatically slice COCO annotation and image files | | [coco fiftyone](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-fiftyone-command-usage) | explore multiple prediction results on your COCO dataset with [fiftyone ui](https://github.com/voxel51/fiftyone) ordered by number of misdetections | | [coco evaluate](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-evaluate-command-usage) | evaluate classwise COCO AP and AR for given predictions and ground truth | | [coco analyse](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-analyse-command-usage) | calculate and export many error analysis plots | | [coco yolov5](https://github.com/obss/sahi/blob/main/docs/cli.md#coco-yolov5-command-usage) | automatically convert any COCO dataset to [yolov5](https://github.com/ultralytics/yolov5) format | ## <div align="center">Quick Start Examples</div> [📜 List of publications that cite SAHI (currently 100+)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0,5&cites=14065474760484865747&scipsc=&q=&scisbd=1) [🏆 List of competition winners that used SAHI](https://github.com/obss/sahi/discussions/688) ### Tutorials - [Introduction to SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80) - [Official paper](https://ieeexplore.ieee.org/document/9897990) (ICIP 2022 oral) (NEW) - [Pretrained weights and ICIP 2022 paper files](https://github.com/fcakyon/small-object-detection-benchmark) - ['Exploring SAHI' Research Article from 'learnopencv.com'](https://learnopencv.com/slicing-aided-hyper-inference/) (2023) (NEW) - ['VIDEO TUTORIAL: Slicing Aided Hyper Inference for Small Object Detection - SAHI'](https://www.youtube.com/watch?v=UuOjJKxn-M8&t=270s) (2023) (NEW) - [Video inference support is live](https://github.com/obss/sahi/discussions/626) - [Kaggle notebook](https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx) - [Satellite object detection](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98) - [Error analysis plots & evaluation](https://github.com/obss/sahi/discussions/622) (NEW) - [Interactive result visualization and inspection](https://github.com/obss/sahi/discussions/624) (NEW) - [COCO dataset conversion](https://medium.com/codable/convert-any-dataset-to-coco-object-detection-format-with-sahi-95349e1fe2b7) - [Slicing operation notebook](demo/slicing.ipynb) - `YOLOX` + `SAHI` demo: <a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img src="https://raw.githubusercontent.com/obss/sahi/main/resources/hf_spaces_badge.svg" alt="sahi-yolox"></a> (RECOMMENDED) - `YOLOv5` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-yolov5"></a> - `MMDetection` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-mmdetection"></a> - `Detectron2` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_detectron2.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-detectron2"></a> - `HuggingFace` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_huggingface.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-huggingface"></a> (NEW) - `TorchVision` + `SAHI` walkthrough: <a href="https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_torchvision.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="sahi-torchvision"></a> (NEW) <a href="https://huggingface.co/spaces/fcakyon/sahi-yolox"><img width="600" src="https://user-images.githubusercontent.com/34196005/144092739-c1d9bade-a128-4346-947f-424ce00e5c4f.gif" alt="sahi-yolox"></a> </details> ### Installation <img width="700" alt="sahi-installation" src="https://user-images.githubusercontent.com/34196005/149311602-b44e6fe1-f496-40f2-a7ae-5ea1f66e1550.gif"> <details closed> <summary> <big><b>Installation details:</b></big> </summary> - Install `sahi` using pip: ```console pip install sahi ``` - On Windows, `Shapely` needs to be installed via Conda: ```console conda install -c conda-forge shapely ``` - Install your desired version of pytorch and torchvision (cuda 11.3 for detectron2, cuda 11.7 for rest): ```console conda install pytorch=1.10.2 torchvision=0.11.3 cudatoolkit=11.3 -c pytorch ``` ```console conda install pytorch=1.13.1 torchvision=0.14.1 pytorch-cuda=11.7 -c pytorch -c nvidia ``` - Install your desired detection framework (yolov5): ```console pip install yolov5==7.0.13 ``` - Install your desired detection framework (mmdet): ```console pip install mim mim install m