From bdd1eb5a98debd92d2483983ba97b6307cb09b3f Mon Sep 17 00:00:00 2001 From: sweemeng Date: Tue, 5 Jan 2021 10:46:28 +0800 Subject: [PATCH] Add OID --- content/blogs/2021/01/11-oid-data.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 content/blogs/2021/01/11-oid-data.md diff --git a/content/blogs/2021/01/11-oid-data.md b/content/blogs/2021/01/11-oid-data.md new file mode 100644 index 0000000..d81a88c --- /dev/null +++ b/content/blogs/2021/01/11-oid-data.md @@ -0,0 +1,16 @@ +--- +title: "Computer Vision The Boring Part - Open Image Dataset" +date: 2021-01-11 +tags: ["tools", "machine learning", "AI", "computer vision"] +image: https://res.cloudinary.com/dty81dwqf/image/upload/c_scale,w_1020/v1609814169/Screenshot_2021-01-05_Open_Images_Dataset_V6_rulr3l.jpg +author: sweemeng +--- +![](https://res.cloudinary.com/dty81dwqf/image/upload/c_scale,w_1020/v1609814169/Screenshot_2021-01-05_Open_Images_Dataset_V6_rulr3l.jpg) + +We have shown how one can create your own {{}}. Another way to get dataset is the [Google Open Image Dataset](https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=segmentation&r=false&c=%2Fm%2F015p6). + +This is a dataset that have 1.5 million annotations for 600 categories. It covers some common thing that trained in YOLO. You can have a shot at trying to see if the category exist. + +To use this, I suggest that you use a tools, the one I use is the OID Toolkit, I use the [fork](https://github.com/theAIGuysCode/OIDv4_ToolKit) by The AI Guy a youtuber talking about these. The annotations is likely not in a format you want. The reason I use the fork is the creator of the fork have a converter that convert into YOLO format. + +While 600 category seems a lot, they also don't have everything. Like rice and all that. Which is why it is still worth it to learn to label your own image. Even if it is slow