@@ -17,3 +17,34 @@ Using a model with smaller patch size will also in the end lead to a better reso
We've selected some state of the art models that seem to work well on our usecases so far. If you are short in RAM, prefer using the ``ViT Tiny model``, that is almost ten times smaller than the others (but can provide a less nuanced map).
## Can I use any model from HuggingFace ?
[HuggingFace](https://huggingface.co/) is one of the largest hub where pre-trained deep learning models are published (at of time of writing, they have over 1 million models available).
It is an essential tool to follow the deep learning state of the art and one of the *de facto* standards for sharing models.
They have now partnered with the `timm` library, that was one of the most used libraries used by the community to initialize models and load pre-trained weights.
While this opens a very wide range of model usable with the plugin, not all models can be used out of the box.
Indeed, our code is optimized for ViT-like models that output patch-level features. If you use another type of architecture, it may work but it probably won't.
It is best to use the models provided directly by `timm`, you can find the list [there](https://huggingface.co/timm).
However, you can also use other pre-trained models available on HuggingFace. [As stated in their docs](https://huggingface.co/docs/hub/timm), timm can recognise some.
Then, when you input the name of the model in the plugin, you have to add the `hf-hub:` prefix before the model card name.
```
'hf-hub:nateraw/resnet50-oxford-iiit-pet'
```
If a model is unavailable, you can try to update `timm`.
## Why doesn't this projection, clustering or classification algorithm work ?
All algorithms provided by `sklearn` used in this plugin are not yet extensively tested.
We select all algorithms that share a common API and should a priori work correctly.
However, some algorithms may expect other data format and might be unusable out of the box.
In this case, your feedback is more than welcome, do not hesitate to [fill an issue on github](https://github.com/umr-amap/iamap/issues).
The algorithms that are proprely tested for now are PCA, UMAP, Kmeans, HDBSCAN and RandomForestClassifier.
However, it is in our roadmap to test more extensively.
The similarity tool however, is coded in pure pytorch and is tested automatically.
`umap` has `numba` as a dependdencie, which may require `numpy < 2.0` and conflict with other librairies depending on how you installed them. According to `numba` developpers, this
(should be resolved in comming numba releases)[https://github.com/numba/numba/issues/9708].
[should be resolved in comming numba releases](https://github.com/numba/numba/issues/9708).
In the mean time, you can use a conda environement or uninstall and reinstall numpy at a previous version.
