chore: import upstream snapshot with attribution

configs/rfdetr_base.yaml

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+# RF-DETR Base — example training configuration (resolution 560, patch 14).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_base.yaml
+#   rfdetr fit --config configs/rfdetr_base.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRBaseConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.TrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_base
+      epochs: 100
+      batch_size: 4
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_large.yaml

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+# RF-DETR Large — example training configuration (resolution 704, patch 16).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_large.yaml
+#   rfdetr fit --config configs/rfdetr_large.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRLargeConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.TrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_large
+      epochs: 100
+      batch_size: 2
+      grad_accum_steps: 8 # effective batch size 16
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_medium.yaml

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+# RF-DETR Medium — example training configuration (resolution 576, patch 16).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_medium.yaml
+#   rfdetr fit --config configs/rfdetr_medium.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRMediumConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.TrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_medium
+      epochs: 100
+      batch_size: 4
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_nano.yaml

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+# RF-DETR Nano — example training configuration (resolution 384, patch 16).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_nano.yaml
+#   rfdetr fit --config configs/rfdetr_nano.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRNanoConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.TrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_nano
+      epochs: 100
+      batch_size: 8
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_seg_2xlarge.yaml

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+# RF-DETR Seg 2XLarge — example segmentation training configuration (resolution 768, patch 12).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_seg_2xlarge.yaml
+#   rfdetr fit --config configs/rfdetr_seg_2xlarge.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRSeg2XLargeConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.SegmentationTrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_seg_2xlarge
+      epochs: 100
+      batch_size: 1
+      grad_accum_steps: 16 # effective batch size 16
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_seg_large.yaml

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+# RF-DETR Seg Large — example segmentation training configuration (resolution 504, patch 12).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_seg_large.yaml
+#   rfdetr fit --config configs/rfdetr_seg_large.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRSegLargeConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.SegmentationTrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_seg_large
+      epochs: 100
+      batch_size: 2
+      grad_accum_steps: 8 # effective batch size 16
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_seg_medium.yaml

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+# RF-DETR Seg Medium — example segmentation training configuration (resolution 432, patch 12).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_seg_medium.yaml
+#   rfdetr fit --config configs/rfdetr_seg_medium.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRSegMediumConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.SegmentationTrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_seg_medium
+      epochs: 100
+      batch_size: 3
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_seg_nano.yaml

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+# RF-DETR Seg Nano — example segmentation training configuration (resolution 312, patch 12).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_seg_nano.yaml
+#   rfdetr fit --config configs/rfdetr_seg_nano.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRSegNanoConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.SegmentationTrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_seg_nano
+      epochs: 100
+      batch_size: 4
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_seg_small.yaml

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+# RF-DETR Seg Small — example segmentation training configuration (resolution 384, patch 12).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_seg_small.yaml
+#   rfdetr fit --config configs/rfdetr_seg_small.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRSegSmallConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.SegmentationTrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_seg_small
+      epochs: 100
+      batch_size: 4
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_seg_xlarge.yaml

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+# RF-DETR Seg XLarge — example segmentation training configuration (resolution 624, patch 12).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_seg_xlarge.yaml
+#   rfdetr fit --config configs/rfdetr_seg_xlarge.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRSegXLargeConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.SegmentationTrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_seg_xlarge
+      epochs: 100
+      batch_size: 2
+      grad_accum_steps: 8 # effective batch size 16
+      num_workers: 4
+      tensorboard: true

configs/rfdetr_small.yaml

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+# RF-DETR Small — example training configuration (resolution 512, patch 16).
+# Usage:
+#   rfdetr fit --config configs/rfdetr_small.yaml
+#   rfdetr fit --config configs/rfdetr_small.yaml \
+#     --model.train_config.init_args.dataset_dir /data/my_dataset \
+#     --trainer.devices 4
+
+model:
+  model_config:
+    class_path: rfdetr.config.RFDETRSmallConfig
+    init_args:
+      num_classes: 80 # set to your dataset class count
+  train_config:
+    class_path: rfdetr.config.TrainConfig
+    init_args:
+      dataset_dir: /data/coco # required: path to your COCO-format dataset
+      output_dir: output/rfdetr_small
+      epochs: 100
+      batch_size: 6
+      num_workers: 4
+      tensorboard: true