H11-M13_CODE.zip
From the dataset abstract
Method 由於現有的病理影像資料集常是以實例分割的方式提供，為了達成更好的持續學習物件偵測效果，本方法於持續學習步驟中每次分為兩階段，於第一階段，先利用現有的持續學習語意分割方法SSUL, NeurIPS 2021生成語意分割先驗知識，再於第二階段以此先驗知識為額外輸入協助達成更好的持續學習物件偵測結果。 Usage 能用於持續性學習之細胞偵測模型 Release...
Source: H11-M13_持續性學習之物體偵測模型
Additional Information
| Field | Value |
|---|---|
| Data last updated | July 11, 2023 |
| Metadata last updated | July 11, 2023 |
| Created | July 11, 2023 |
| Format | application/zip |
| License | Other (Non-Commercial) |
| created | over 2 years ago |
| format | ZIP |
| id | e50d143c-e89a-4d6b-a1d0-19cd37e2449e |
| last modified | over 2 years ago |
| md5 | 4c99c65f61cb7bdf2b98b8aa34430bda |
| mimetype | application/zip |
| on same domain | True |
| package id | ad94fdd8-6c5f-409b-a802-d637b457f8d8 |
| proxy url | https://scidm.nchc.org.tw/en/dataset/ad94fdd8-6c5f-409b-a802-d637b457f8d8/resource/e50d143c-e89a-4d6b-a1d0-19cd37e2449e/nchcproxy/H11-M13_CODE.zip |
| revision id | 7f6e89ce-9eb2-45eb-a950-0f297ef0f95a |
| sha256 | bd3854f1f03e61afa06097f018fe713cf505559272b2606cf5cefdbbf8340080 |
| size | 4.8 MiB |
| state | active |
| url type | upload |
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