--- library_name: lucid license: apache-2.0 tags: - object-detection - detr - lucid datasets: - coco pipeline_tag: object-detection model-index: - name: detr-resnet101 results: - task: { type: object-detection } dataset: { name: COCO, type: coco } metrics: - { type: box mAP, value: 43.5 } --- # DETR (ResNet-101) > Carion et al., 2020 — *End-to-End Object Detection with Transformers* (arXiv:2005.12872) [Lucid](https://github.com/ChanLumerico/lucid) port of `facebookresearch/detr/detr_resnet101`, converted to Lucid-native safetensors. ## Available weights | Tag | box mAP | Params | GFLOPs | Size | Source | |---|---|---|---|---|---| | `COCO_2017` *(default)* | 43.5 | 60.5M | — | 231.53 MB | facebookresearch | ## Usage ```python import lucid.models as models from lucid.models.weights import DETRResNet101Weights # default tag model = models.detr_resnet101(pretrained=True) # explicit tag (enum or string) model = models.detr_resnet101(weights=DETRResNet101Weights.COCO_2017) model = models.detr_resnet101(pretrained="COCO_2017") # preprocessing travels with the weights weights = DETRResNet101Weights.COCO_2017 preprocess = weights.transforms() out = model(preprocess(image)[None]) # ObjectDetectionOutput: per-query/proposal class logits + boxes logits, boxes = out.logits, out.pred_boxes ``` ## Conversion Converted from `facebookresearch/detr/detr_resnet101` via `python -m tools.convert_weights detr_resnet101 --tag COCO_2017`. Key mapping + numerical parity verified against the source. ## License `apache-2.0` — inherited from the original weights. ## Citation ``` @inproceedings{carion2020detr, title={End-to-End Object Detection with Transformers}, author={Carion, Nicolas and Massa, Francisco and Synnaeve, Gabriel and Usunier, Nicolas and Kirillov, Alexander and Zagoruyko, Sergey}, booktitle={European Conference on Computer Vision (ECCV)}, year={2020} } ```
