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<title>My static Space</title>
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<h1>MLTest</h1>
<p>
This is a demo of MLTest on the dataset
<a href="https://huggingface.co/datasets/marmal88/skin_cancer"><code>marmal88/skin_cancer</code></a>.
Each image is labeled with one of three different kinds of cancers.
</p>
<p>
The model has been trained on five models: two variants of Swin Transformers, ViT, ResNet, and BEiT. The test results for each model can
be inspected in the dashboard below.
</p>
<p>
<b>Performance tests</b>:
in order to measure how well the model performs, we compute common performance metrics like accuracy,
precision, recall, F1 score, and more.
</p>
<p>
<b>Failure clusters</b>:
these clusters give meaningful insights when the model is failing and can be inspected in the "Failure Clusters" tab.
These failure clusters are automatically detected
for different combinations of metadata.
For example, the BEiT transformer has a significantly lower accuracy on images taken of cancers of the back with class label <code>0</code>.
</p>
<p>
<b>Robustness</b>: these tests help ML developers evaluate how well their model performs under different conditions.
These conditions could include different levels of brightness, compression, and many other types of interference.
</p><p>
The following robustness tests were enabled for this test case:
</p>
<ul>
<li>Brightness</li>
<li>CompressImage</li>
<li>Contrast</li>
<li>DarkSpots</li>
<li>GaussianBlur</li>
<li>GaussianNoise</li>
<li>Glare</li>
<li>GlassBlur</li>
<li>HorizontalFlip</li>
<li>MedianBlur</li>
<li>MotionBlur</li>
<li>OilSpots</li>
<li>Perspective</li>
<li>VerticalFlip</li>
</ul>
<p>
The full list of transforms supported by MLTest can be found in the <a href="https://docs.lakera.ai/configuration/robustness">documentation</a>.
</p>
<p>
<b>Fairness tests</b>: TODO
</p>
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<iframe src="https://hf.lakera.ai/search"></iframe>
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