| --- |
| tags: |
| - vision |
| - image-classification |
| datasets: |
| - OttoYu/Treecondition |
| widget: |
| - src: https://bit.ly/3KbaDNI |
| example_title: Canker Diseases |
| - src: https://bit.ly/40FN317 |
| example_title: Bacterial canker |
| - src: https://bit.ly/3LYSGn6 |
| example_title: Citrus canker |
| - src: https://www.elitetreecare.com/wp-content/uploads/2016/06/black-knot.jpg |
| example_title: Black knot |
| - src: https://gtr-arbor.com.hk/wp-content/uploads/2015/01/fungi2-5.jpg |
| example_title: Fungi |
| - src: https://gtr-arbor.com.hk/wp-content/uploads/2011/05/insectA2-2.jpg |
| example_title: Termite |
| co2_eq_emissions: |
| emissions: 1.3038362907488008 |
| --- |
| |
| # 🌳 Tree Condition Classification 樹況分類 (bilingual) |
| ### Model Description |
| This online application covers 22 most typical tree disease over 290+ images. If you find any trees that has hidden injures, you can classifies with our model and report the tree condition via this form (https://rb.gy/c1sfja). 此在線程式涵蓋22種官方部門樹況分類的標準，超過290張圖像。如果您發現任何樹木有隱傷，您可以使用我們的模型進行分類並通過此表格報告樹木狀況。 |
|
|
| - **Developed by:** Yu Kai Him Otto |
| - **Shared via:** Huggingface.co |
| - **Model type:** Opensource |
|
|
| ## Uses |
| You can use the this model for tree condition image classification. |
|
|
| ## Training Details |
| ### Training Data |
|
|
| - Loss: 0.355 |
| - Accuracy: 0.852 |
| - Macro F1: 0.787 |
| - Micro F1: 0.852 |
| - Weighted F1: 0.825 |
| - Macro Precision: 0.808 |
| - Micro Precision: 0.852 |
| - Weighted Precision: 0.854 |
| - Macro Recall: 0.811 |
| - Micro Recall: 0.852 |
| - Weighted Recall: 0.852 |
