“Pixel based galaxy morphology classification”的版本间差异
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无编辑摘要 |
无编辑摘要 |
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| 第5行： | 第5行： | ||
Data comes from Legacy Survey in DECaLS region |
Data comes from Legacy Survey in DECaLS region |
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==Labels== |
==Labels== |
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Mike Walmsley trained on DECaLS region with GZD-1/2/5 catalog and GZD-5 class tree with active learning and then form a more confident catalog called catalog_auto. I set a threshold (e.g. 0.5) on each question to split and get labels of each picture. The GZD-5 label tree showed as follows. |
Mike Walmsley trained on DECaLS region with GZD-1/2/5 catalog and GZD-5 class tree with active learning and then form a more confident catalog called catalog_auto. I set a threshold (e.g. 0.5) on each question to split and get labels of each picture. The GZD-5 label tree showed as follows. |
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[[File:Tree.png|thumb]] |
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GZD-5_Class_tree.png |
GZD-5_Class_tree.png |
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==DECaLS introduction== |
==DECaLS introduction== |
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2022年4月6日 (三) 05:17的版本
Introduction
This is a project about morphological galaxies classification. The amazing point is that we're researching an algorithm to seperate background and sources for decreasing the pollution.
Dataset
Data comes from Legacy Survey in DECaLS region
Labels
Mike Walmsley trained on DECaLS region with GZD-1/2/5 catalog and GZD-5 class tree with active learning and then form a more confident catalog called catalog_auto. I set a threshold (e.g. 0.5) on each question to split and get labels of each picture. The GZD-5 label tree showed as follows.
GZD-5_Class_tree.png
DECaLS introduction
Out DECaLS introduction
Architecture
- Simple CNN
- Dense Net
- Xception
Loss function
- Cross Entropy
- Focal loss
Else
Updating..