license: apache-2.0
DETR-Resnet50 (semantic segmentation) Core ML Models
See the Files tab for converted models.
DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.
Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
Intended uses & limitations
You can use the raw model for object detection. See the model hub to look for all available DETR models.
Download
Install huggingface-hub
pip install huggingface-hub
To download one of the .mlpackage folders to the models directory:
huggingface-cli download \
--local-dir models --local-dir-use-symlinks False \
coreml-projects/detr-resnet50-semantic-segmentation \
--include "detr-resnet50-semantic-400-float16.mlpackage/*"
To download everything, skip the --include argument.