# Start to Evaluate

You can join the evaluation created by yourself or your team members through Sextant. Sextant supports both modes of participation, using a TensorBay dataset and loading a model from GitHub.

{% hint style="info" %}
If you have no permission to use the benchmark data, please apply according to the prompts first.
{% endhint %}

## Use a TensorBay Dataset to Start an Evaluation‌

* Find the evaluation you want to join on the evaluation list page and click **View** to enter the corresponding evaluation details page.

![](/files/-MgLKke1ute_vmx-hWb_)

* Click **Start to Evaluate** on the upper right corner of the evaluation details page.‌

![](/files/-MgLKtdjD2TuZhGbMWxn)

* Click **Choose a Dataset from TensorBay** in the pop-up window and select the dataset that needs to be evaluated and then choose the dataset version. Click **Confirm**, then the evaluation will start automatically. Meanwhile, the system will also automatically generate an evaluation record.

![](/files/-MgLLQ3M46NCoX1T1YUc)

{% hint style="info" %}
The status of evaluations is divided into three types: in progress, completed, and failed. If an evaluation failed, please check its log to troubleshoot and retry. If you need help, please [send us feedback.](https://www.graviti.com/forum/support)
{% endhint %}

## Load a Model from GitHub to Start an Evaluation

* Find the evaluation you want to join on the evaluation list page and click **View** to enter the corresponding evaluation details page.‌

![](/files/-MgLKj4tdoYV8lOg8Rzz)

* Click **Start to Evaluate** on the upper right corner of the evaluation details page.

![](/files/-MgLKtdjD2TuZhGbMWxn)

* Select **Load a Model from GitHub** and add the corresponding GitHub Repo URL, for instance, <https://github.com/Graviti-AI/tensorbay-python-sdk.git>. Click **Confirm**, and then the evaluation will start automatically. Meanwhile, the system will also automatically generate an evaluation record.

![](/files/-MgLLUeyWE7n6VNSpxaP)

## How to prepare a suitable algorithm model for Sextant

1. First prepare the algorithm that needs to be used in evaluation and verify its availability.
2. Write python code according to the following structure:

* There is only one class named Predictor in the python library.
* There is a predict() method in the Predictor class. Please refer to Graviti’s docs for the return value.
* The model on which the algorithm depends must can be used by the algorithm.

```python
class Predict:
    def __init__(self):
        """
        You can initialize your model here
        """
        ...
    def predict(self, img_data: bytes) -> Dict[str, Any]:
        """
        Do the predict job
        :param img_data: the binary data of one image file
        :return: the predict result
        """
        ...

"""
Box2D Example

{
    "BOX2D": [
        {
            "box2d": { "xmin": 1, "ymin": 2, "xmax": 3, "ymax": 4 },
            "category": "cat"
        },
        {
            "box2d": { "xmin": 5, "ymin": 4, "xmax": 6, "ymax": 9},
            "category": "dog"
        }
    ]
}
"""
```

&#x20;   3\. For details, please see the [example](https://github.com/AChenQ/ssd-detection/blob/master/predict/predictor.py).

&#x20;   4\. Upload the code file to Github and copy and paste the .git link to Sextant to start an evaluation.

{% hint style="info" %}
If your code relies on a model, please ensure that the model can be accessed by the code successfully.
{% endhint %}

## View Evaluation Logs

Sextant will record the system logs during the evaluation process for users to track the evaluation process and resolve potential bugs in advance.‌Viewing steps are as following:

* Find the evaluation you want to view on the evaluation list page and click **View** to enter the corresponding evaluation details page.‌

![](/files/-MgLKj4tdoYV8lOg8Rzz)

* Find the record you want to view on the evaluation history page and click **Log** on the right side.

![](/files/-MgLNI7xCMaLMqbF7Qj6)

* Select specific steps of log that you want to view in the pop-up window, and then the required log information will be displayed on the right side.

![](/files/-MgLNd8_XlXn31cuOj71)


---

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