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--- |
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tags: |
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- vision |
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- image-classification |
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datasets: |
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- OttoYu/Treecondition |
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widget: |
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- src: https://bit.ly/3KbaDNI |
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example_title: Canker Diseases |
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- src: https://bit.ly/40FN317 |
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example_title: Bacterial canker |
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- src: https://bit.ly/3LYSGn6 |
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example_title: Citrus canker |
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- src: https://www.elitetreecare.com/wp-content/uploads/2016/06/black-knot.jpg |
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example_title: Black knot |
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- src: https://gtr-arbor.com.hk/wp-content/uploads/2015/01/fungi2-5.jpg |
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example_title: Fungi |
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- src: https://gtr-arbor.com.hk/wp-content/uploads/2011/05/insectA2-2.jpg |
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example_title: Termite |
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co2_eq_emissions: |
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emissions: 1.3038362907488008 |
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--- |
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# 🌳 Tree Condition Classification 樹況分類 (bilingual) |
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### Model Description |
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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。 |
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- **Developed by:** Yu Kai Him Otto |
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- **Shared via:** Huggingface.co |
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- **Model type:** Opensource |
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## Uses |
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You can use the this model for tree condition image classification. |
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## Training Details |
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### Training Data |
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- Loss: 0.355 |
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- Accuracy: 0.852 |
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- Macro F1: 0.787 |
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- Micro F1: 0.852 |
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- Weighted F1: 0.825 |
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- Macro Precision: 0.808 |
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- Micro Precision: 0.852 |
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- Weighted Precision: 0.854 |
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- Macro Recall: 0.811 |
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- Micro Recall: 0.852 |
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- Weighted Recall: 0.852 |
