> For the complete documentation index, see [llms.txt](https://picsellia.gitbook.io/picsellia/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://picsellia.gitbook.io/picsellia/models-1/model-hub.md).

# Model HUB

On Picsellia, you willl found what we call the Model HUB which is a place where we put every available pre-trained architectures from SOTA benchmarks and where YOU can also upload the trained models you want to share with the community.

## Architecture list

Here we have listed all the available architectures that we are responsible of for different frameworks.

### Tensorflow 2

* efficientdet-d0
* efficientdet-d1
* efficientdet-d2
* efficientdet-d3
* efficientdet-d4
* efficientdet-d5
* faster-rcnn-inception-resnet-v2-640x640
* faster-rcnn-inception-resnet-v2-1024x1024
* faster-rcnn-resnet50-v1-640x640
* faster-rcnn-resnet50-v1-800x1333
* faster-rcnn-resnet50-v1-1024x1024
* faster-rcnn-resnet101-v1-640x640
* faster-rcnn-resnet101-v1-800x1333
* faster-rcnn-resnet101-v1-1024x1024
* faster-rcnn-resnet152-v1-640x640
* faster-rcnn-resnet152-v1-800x1333
* faster-rcnn-resnet152-v1-1024x1024
* ssd-mobilenet-v1-fpn-640
* ssd-mobilenet-v2-fpnlite-320
* ssd-mobilenet-v2-fpnlite-640
* ssd-resnet50-v1-640x640
* ssd-resnet50-v1-1024-1024
* ssd-resnet101-v1-640x640
* ssd-resnet101-v1-1024-1024
* ssd-resnet152-v1-640x640
* ssd-resnet152-v1-1024-1024

## Pytorch

* YOLOv5-s
* YOLOv5-m
* YOLOv5-l
* YOLOv5-xl