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coreml-detr-semantic-segmentation

Image Segmentation
Core ML
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coreml-detr-semantic-segmentation
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---
license: apache-2.0
---

# DETR-Resnet50 (semantic segmentation) Core ML Models

See [the Files tab](https://huggingface.co/coreml-projects/detr-resnet50-semantic-segmentation/tree/main) 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](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr). 

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.

![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/detr_architecture.png)

## Intended uses & limitations

You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models.

## Download

Install `huggingface-hub`

```bash
pip install huggingface-hub
```

To download one of the `.mlpackage` folders to the `models` directory:

```bash
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.