Evaluating a Darknet model with the test split

How can I use my testing dataset from Roboflow to evaluate the model I trained using Darknet, which was trained on my custom dataset that I generated and preprocessed with Roboflow

To test your model trained on Darknet with your testing dataset from Roboflow, you would typically follow these steps:

  1. Export your dataset from Roboflow: After preprocessing and augmenting your dataset in Roboflow, you need to export it in the YOLO Darknet format. You can do this by selecting the YOLO Darknet format when prompted to choose a data format for your export (source).

  2. Download your dataset into your training notebook: After exporting, you will receive a curl link to download your data into your training notebook. You can use this link to download your dataset into the notebook where you will be testing your model (source).

  3. Load your dataset: You can load your dataset from Roboflow using the Roboflow Data Loader. You will need to provide the Roboflow workspace URL, project URL, and project version. The model type should be “object-detection” (source).

  4. Test your model: Once your dataset is loaded, you can test your model. If you’re using a Python environment, you can use the predict() method on your model and pass in the path to an image from your testing dataset. Here’s an example of how you might do this:

results = target_model.predict("/path/to/your/image.jpg")

This will print out the names of the detected objects and their corresponding probabilities (source).

Please note that the exact steps and code may vary depending on your specific setup and requirements. If you encounter any issues, please provide more details about your project type, operating system, and any error messages you’re seeing.

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Then how to use testing dataset to test model?

Without more details like what notebook you’re using or what Darknet implementation you’re working with, the answer @Lenny is correct.

You need to download the model locally and test your model.

Most model training methods include a way by default to evaluate on your test dataset, so this might not be necessary in the first place.