OttoYu
/

Tree-ConditionHK

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 ### 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:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
 ### Training Data
-- Loss: 1.355
 - Accuracy: 0.852
 - Macro F1: 0.487
 - Micro F1: 0.552
@@ -81,123 +39,4 @@ Use the code below to get started with the model.
 - Micro Recall: 0.552
 - Weighted Recall: 0.552
 -
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Data Card if possible. -->
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-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 ### 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:** Opensourse
 ## Uses
+You can use the this model for tree condition image classification.
 ## Training Details
 ### Training Data
+- Loss: 0.355
 - Accuracy: 0.852
 - Macro F1: 0.487
 - Micro F1: 0.552
 - Micro Recall: 0.552
 - Weighted Recall: 0.552
 -