
# 🌳 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
{"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}}
transformers pytorch swin image-classification vision dataset:OttoYu/Treecondition co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #swin #image-classification #vision #dataset-OttoYu/Treecondition #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


#  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 (URL 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: URL
- 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
#  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 (URL 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: URL
- 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
TAGS
#transformers #pytorch #swin #image-classification #vision #dataset-OttoYu/Treecondition #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 #  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 (URL 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: URL
- 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
TAGS
#transformers #pytorch #swin #image-classification #vision #dataset-OttoYu/Treecondition #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
#  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 (URL 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: URL
- 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