DETR tutorials to use it on custom data :)

Some months ago, I’ve added DETR to HuggingFace Transformers 🤗 I’ve replaced the original torchvision backbones (ResNets) such that you can use any backbone available in the timm repository (like EfficientNets or MobileNets etc.) 🥳 The model is implemented using the same API as other models in HuggingFace like BERT (i.e. you have DetrModel which is the encoder-decoder Transformer without any head on top, DetrForObjectDetection which has the object detection and class labels classifier heads on top and DetrForSegmentation, which adds the mask head on top).

The model weights are hosted on the HuggingFace hub. The documentation can be found here: https://huggingface.co/transformers/model_doc/detr.html

I’ve made 5 different notebooks, illustrating how to use DETR both for inference and training on custom data, both for object detection and panoptic segmentation. You can find them here: https://github.com/NielsRogge/Transformers-Tutorials

I’ve also made a notebook for evaluating the model on COCO. Hope it helps for people to easily use DETR!

Issues for which this might be helpful:

#421 #419 #378 #366 #341 #307 #205 #190 #179 #153 #152 #148 #141 #125 #111 #109 #89 #84 #53 #40 #28 #21 #14 #9

PS the most epic thing was Yann LeCun himself tweeting about this: https://twitter.com/ylecun/status/1405640394143113219

[NielsRogge]

Yes, it takes 30 minutes per epoch if you have 8 GPUs, as stated in the README. On a single GPU, it will take a bit longer. 😉

[ver0z]

So if I pass a big number of batch could it be useful in this case ?