Migration Guide¶
Read each section between your current version and your target — every section covers only the delta between two adjacent releases.
You can apply all changes in one go; working through sections one release at a time and verifying between each step is optional but makes failures easier to isolate. Deprecated APIs and configuration fields emit the warning type stated in their entry until the version marked for removal. See the Changelog for the full list of changes in each release.
Unreleased: native inference returns a predictor¶
RFDETR.inference() now returns an independent RFDETRInference snapshot instead of changing the source model's inference state. Capture the returned predictor and call predict() on it. The predictor snapshots the current base weights, labels, normalization and postprocessing settings; later training or source mutations do not change its predictions or class names. The source model remains unchanged.
from rfdetr import RFDETRSmall
model = RFDETRSmall()
predictor = model.inference(compile=False)
detections = predictor.predict("image.jpg")
The defaults are batch size 1 and float32, compiled with TorchScript, captured as a CUDA graph on CUDA, or compiled with inductor on Apple Silicon (MPS) when the installed PyTorch has its MPS code generator; pass compile_backend="torchscript" on either to keep TorchScript alone. Pass compile=False for eager CPU inference. The removed inplace keyword is rejected. Each predictor owns one copy of the current base weights; deleting it releases its runtime when no other references remain. The legacy remove_optimized_model() and is_optimized_inplace APIs remain for legacy model state and do not manage predictor lifetime. Direct RFDETRInference(live_model) construction still borrows the live model; checkpoint and export paths keep their existing owned loading behavior.
Upgrade 1.11 → 1.12¶
For new image-prediction integrations, use the shared RFDETRInference API with a live native model, checkpoint, or supported full export. It returns decoded Supervision results and handles preprocessing; keep format-specific session APIs for raw-runtime workflows.
Removed in v1.12¶
Removed: rfdetr.datasets.aug_config
The rfdetr.datasets.aug_config compatibility module, deprecated since v1.9.0, is gone. Importing it raises ModuleNotFoundError. The preset constants are unchanged; only the module path moves.
Deprecated in v1.12 → Remove in v1.15¶
Deprecated: raw TensorRT and OpenVINO inference
TRTInference and OpenVINOInference are deprecated in v1.12.0 and will be removed in v1.15.0, following the three-minor-release deprecation window. Both legacy classes remain available until v1.15.0 and emit a FutureWarning when constructed.
Use RFDETRInference for image inputs and decoded Supervision results. The old TensorRT facade accepts already-preprocessed, normalized NCHW CUDA tensors and returns raw engine output tensors; the old OpenVINO facade accepts an already-preprocessed, normalized, contiguous float32 NCHW array and returns raw output arrays. RFDETRInference.predict() accepts images and returns supervision.Detections for detection or segmentation models, or supervision.KeyPoints for keypoint models.
from rfdetr import RFDETRInference
model = RFDETRInference("output/model.trt")
detections = model.predict("image.jpg")
Move the legacy runtime settings into runtime_options when constructing RFDETRInference:
| Legacy runtime | New API | runtime_options keys |
|---|---|---|
TRTInference(engine_path, cuda_graph=True) |
RFDETRInference(engine_path, runtime_options={"cuda_graph": True}).predict(image) |
cuda_graph, engine_host_code_allowed, verbose |
OpenVINOInference(model_path, cache_dir=cache) |
RFDETRInference(model_path, runtime_options={"cache_dir": cache}).predict(image) |
cache_dir, inference_precision, config |
Pass the runtime choice through RFDETRInference's device keyword. TensorRT's former sync_mode flag has no runtime_options key because the shared TensorRT adapter uses execute_v2 by default. For example, OpenVINOInference(model_path, cache_dir=cache) becomes RFDETRInference(model_path, runtime_options={"cache_dir": cache}). The shared API handles preprocessing and postprocessing; use the format-specific session interfaces only when a raw-runtime benchmark or numerical check requires raw tensors.
Deprecated in v1.12 → Remove in v2.0¶
Deprecated: unknown keyword arguments to RFDETR.predict()
RFDETR.predict() used to accept and silently ignore any extra keyword argument. Since v1.12.0 it still ignores them but emits a FutureWarning that names them; in v2.0.0 they will raise TypeError, as RFDETRInference.predict() already does. Remove the extra keywords from your calls.
Upgrade 1.10 → 1.11¶
Breaking changes¶
Breaking: the export internals are now exporter classes
Each export format is written by a <Format>Exporter class built from that format's configuration, and RFDETR.export() is a facade over them. Its own signature and return value are unchanged — only the internal modules moved. Public exports are unaffected; this matters only if you imported the converter functions directly.
| Removed | Replacement |
|---|---|
rfdetr.export.main (whole module, including main() and make_infer_image) |
rfdetr.export.prepare.make_infer_image; the main() CLI had no entry point and is gone |
rfdetr.export.protocols.ExporterProtocol |
rfdetr.export.base.Exporter |
rfdetr.export._onnx.exporter.export_onnx |
rfdetr.export._onnx.exporter.OnnxExporter |
rfdetr.export._openvino.exporter.export_openvino |
rfdetr.export._openvino.exporter.OpenVINOExporter |
rfdetr.export._coreml.converter.export_coreml |
rfdetr.export._coreml.exporter.CoreMLExporter |
rfdetr.export._executorch.converter.export_executorch |
rfdetr.export._executorch.exporter.ExecuTorchExporter |
rfdetr.export._tflite.converter.export_tflite |
rfdetr.export._tflite.exporter.TFLiteExporter |
rfdetr.export._tensorrt.build_engine |
rfdetr.export._tensorrt.exporter.TensorRTExporter |
rfdetr.export.benchmark.TRTInference |
rfdetr.export._tensorrt.inference.TRTInference |
Every path with a leading underscore was already internal and carried no stability guarantee; they are listed so the move is discoverable rather than a silent break. Prefer RFDETR.export(format=...). The contract each exporter implements is documented in the Exporter Blueprint.
Breaking: format=\"tensorrt\" with backbone_only=True and output_name now marks the engine
The engine is named {output_name}-backbone.trt instead of {output_name}.trt, matching what RFDETR.export()'s documentation already described and what every other format does. Without the marker a backbone engine silently overwrites a full-detector engine exported under the same name. Scripts that rebuilt the path from output_name need the suffix added.
Breaking: the WebDataset streaming extra is now data, not webdataset
pip install rfdetr[webdataset] never shipped — the extra bundling webdataset (for dataset_file="webdataset" streaming training input) is named data.
Breaking: TrainConfig.multi_scale is now a three-state enum, do_random_resize_via_padding is gone
multi_scale accepts MultiScale.OFF / PER_BATCH / PER_SAMPLE (or the equivalent strings "off" / "per-batch" / "per-sample") instead of a plain bool; the default is PER_BATCH. Booleans still work as input — True normalizes to PER_BATCH (the old default's behavior), False to OFF — so an existing multi_scale=True/False config keeps its behavior unchanged. do_random_resize_via_padding is removed: its True setting is now multi_scale="per-sample".
# Before
TrainConfig(multi_scale=True, do_random_resize_via_padding=True)
# After
TrainConfig(multi_scale="per-sample")
A training_config.json written by v1.10 or earlier carries do_random_resize_via_padding explicitly; TrainConfig rejects unknown fields, so drop it before reloading (same pattern as the lr_drop/lr_min_factor removal below).
Deprecated¶
Deprecated: ModelConfig.amp, removal in v1.14
TrainConfig.amp_dtype is now the sole authority for enabling and choosing mixed precision; it accepts None to disable autocast entirely, which ModelConfig.amp=False used to do alone. An explicit (non-default) amp_dtype always wins over the deprecated amp flag; amp is only consulted when amp_dtype is left at its default "auto" and amp=False.
Deprecated: TrainConfig.fp16_eval, removal in v1.14
Has had no runtime consumer since the PyTorch Lightning migration — evaluation precision now follows amp_dtype. Setting it emits a FutureWarning; drop it from configs.
Removed¶
Removed: rfdetr.datasets.synthetic
The synthetic shape-dataset generator has been removed. It existed to build RF-DETR's own test fixtures, and that job now belongs to the fuse-augmentations package, whose fuse_augmentations.data module generates the same datasets in COCO or YOLO layout for detection, segmentation, and OBB tasks.
Removed names: generate_coco_dataset, generate_synthetic_sample, draw_synthetic_shape, calculate_boundary_overlap, DatasetSplitRatios, DEFAULT_SPLIT_RATIOS, SYNTHETIC_SHAPES, SYNTHETIC_COLORS.
# Before
from rfdetr.datasets.synthetic import DatasetSplitRatios, generate_coco_dataset
generate_coco_dataset(
output_dir="dataset",
num_images=100,
img_size=224,
class_mode="shape",
min_objects=3,
max_objects=7,
split_ratios=DatasetSplitRatios(train=0.8, val=0.2, test=0.0),
with_segmentation=True,
)
# After
from fuse_augmentations.data import SplitRatios, generate_dataset
generate_dataset(
output_dir="dataset",
num_images=100,
fmt="coco",
task="segmentation", # "detection" for boxes only
class_mode="shape",
split_ratios=SplitRatios(train=0.8, val=0.2, test=0.0),
seed=42,
img_size=224,
min_objects=3,
max_objects=7,
)
Two differences to plan for: the replacement writes dense COCO category ids (1, 2, 3, …) where the removed generator wrote sparse ones (1, 3, 5, …), and its shape set adds rectangle, so a fixed class count of 3 becomes 4. Derive the class count from the annotation file rather than hard-coding it. Generation also draws from fresh entropy unless you pass seed.
Removed: RFDETR.optimize_for_inference()
The alias deprecated in v1.9.0 is gone. RFDETR.inference() kept the same signature when that alias was removed; it now returns an independent predictor. Capture the result and call predict() on it. See native inference snapshots for the current behavior.
Removed: TrainConfig.lr_drop and TrainConfig.lr_min_factor
Both fields, deprecated in v1.9.0, are gone together with the shim that folded them into lr_scheduler_kwargs. Pass the values through lr_scheduler_kwargs; the managed "step" / "cosine" presets fall back to lr_drop=100 and min_factor=0.0 when a key is absent, the same defaults the removed fields carried.
# Before (removed)
TrainConfig(lr_scheduler="step", lr_drop=80, lr_min_factor=0.1)
# After
TrainConfig(lr_scheduler="step", lr_scheduler_kwargs={"lr_drop": 80, "min_factor": 0.1})
TrainConfig rejects unknown fields, so a training_config.json written by v1.9 or v1.10 no longer loads as-is — its train_config section carries both keys explicitly. Drop them before reloading:
import json
with open("training_config.json") as f:
saved = json.load(f)
saved["train_config"].pop("lr_drop", None)
saved["train_config"].pop("lr_min_factor", None)
train_config = TrainConfig(**saved["train_config"])
training_config.json's top level is {"train_config": ..., "model_config": ..., "model_config_type": ..., "class_names": ..., "num_classes": ...} (see rfdetr.detr._save_training_config); TrainConfig's own fields live under the train_config key, not at the top level. A config saved by v1.9+ already carries the migrated values in lr_scheduler_kwargs, so nothing is lost by dropping the two keys.
Upgrade 1.9 → 1.10¶
Breaking changes¶
Breaking: log_per_class_metrics now defaults to False
TrainConfig.log_per_class_metrics defaults to False (was True). Per-class AP keys (test/AP/<class>, val/AP/<class>) are no longer emitted by default — validation/test now skip that per-class computation. Set it explicitly to restore the old behavior:
Breaking: compute_val_loss now defaults to \"auto\"
TrainConfig.compute_val_loss defaults to "auto" (was True). In "auto" mode, validation loss is computed only when something actually consumes it — a ReduceLROnPlateau scheduler monitoring val/loss, or a callback (e.g. ModelCheckpoint(monitor="val/loss"), early stopping) that requires it — and is skipped otherwise. For a default run with no such consumer, val/loss no longer appears in metrics.csv, TensorBoard, or W&B. Set it explicitly to restore the old unconditional behavior:
Breaking: validation evaluates one model, and val/mAP_* follows it
Validation now runs one forward pass per batch instead of two: through the EMA weights when use_ema=True (the default), through the base weights otherwise. val/mAP_50_95, val/mAP_50, val/mAP_75, val/mAR, and per-class val/AP/<class> therefore report the EMA model on a default run — they used to report the base model — while the matching val/ema_* namespace remains available. Best-checkpoint selection is unaffected in substance (it already preferred EMA), but the "regular" track no longer writes checkpoint_best_regular.pth on such runs; checkpoint_best_total.pth is copied from the EMA checkpoint.
val/F1 and val/loss (when computed) follow the same single forward, so they now describe the EMA model too — consistent with the mAP under the primary key, but a change of meaning if you monitor val/loss with a ReduceLROnPlateau scheduler, ModelCheckpoint, or early stopping.
Restore the previous two-forward behavior, both metric namespaces and both checkpoint tracks:
Breaking: grad_accum_steps now defaults to 1
TrainConfig.grad_accum_steps defaults to 1 (was 4), which changes the default effective batch size from 16 to 4 at the default batch_size=4. This is a training-semantics change, not just a throughput change — the optimization trajectory (and possibly convergence/final mAP) can differ from a run using the old default. batch_size="auto" runs are unaffected, since the auto-batch probe overwrites grad_accum_steps with its own recommendation. Restore the previous default:
Breaking: optimizer parameter groups are now merged by hyperparameter
get_param_dict now builds one parameter group per distinct learning-rate/weight-decay combination instead of one group per parameter (rfdetr-nano goes from 465 groups to 28). Layer-wise LR decay and per-parameter weight decay are preserved, and the resulting AdamW steps are bit-identical; checkpoints saved with the old per-parameter layout resume automatically. If you pass an explicit lr_scheduler whose lr_scheduler_kwargs include a list sized to a specific parameter-group count (e.g. LambdaLR's per-group lr_lambda), resize that list to match the new (smaller) group count.
Breaking: dataset builders require a complete pipeline-option namespace
build_roboflow_from_coco, build_roboflow_from_yolo, and build_o365_raw now raise instead of silently substituting a default when the config namespace passed to them is missing square_resize_div_64, segmentation_head, multi_scale, expanded_scales, do_random_resize_via_padding, patch_size, or num_windows (build_o365_raw doesn't take segmentation_head — detection-only). Calling these with a complete TrainConfig/ModelConfig (the normal .train()/RFDETRDataModule path) is unaffected — this only affects callers who assemble a partial config namespace by hand and pass it to these builders directly. Supply every field on that namespace to fix it:
# Before — missing fields silently fell back to (sometimes wrong) defaults
build_roboflow_from_coco(args=partial_namespace)
# After — supply every pipeline option the builder needs, matching your
# model variant's ModelConfig (patch_size varies by variant — 12, 14, or
# 16 — read it from your ModelConfig rather than hardcoding it)
partial_namespace.square_resize_div_64 = True
partial_namespace.segmentation_head = False
partial_namespace.multi_scale = True
partial_namespace.expanded_scales = True
partial_namespace.do_random_resize_via_padding = False
partial_namespace.patch_size = model_config.patch_size
partial_namespace.num_windows = model_config.num_windows
build_roboflow_from_coco(args=partial_namespace)
Deprecated in v1.10 → Remove in v1.13¶
eval_ema_only is deprecated
Evaluating only the selected model is the default. Explicit legacy TrainConfig(eval_ema_only=True) preserves EMA-only evaluation and emits a FutureWarning; explicit eval_ema_only=False is migrated to the old base-plus-EMA behavior and also warns. Drop the field from new configurations. Use eval_base_model=True if you want the base model evaluated as well; eval_ema_only=True still requires use_ema=True and conflicts with that opt-in.
One improvement for existing eval_ema_only users: val/mAP_50_95 is now populated (with the EMA score) instead of staying absent, so monitors pointed at it start receiving values again.
Upgrade 1.8 → 1.9¶
Breaking changes¶
Breaking: albumentations and kornia extras merged into augment
PyPI extras renamed. Default training/validation/prediction/export augmentations now use torchvision-native transforms, so [train] no longer installs Albumentations. Custom CPU (Albumentations) and GPU (Kornia) augmentation both live behind a single new augment extra.
| Old extra | New extra |
|---|---|
rfdetr[train] (implied albumentations) |
rfdetr[train,augment] |
rfdetr[kornia] |
rfdetr[augment] |
augmentation_backend values renamed
augmentation_backend="tv" and "albu" are renamed to "torchvision" and "albumentations". The old strings (and "gpu") still work as aliases, so no code changes are required — see Augmentation Backend Values for the full accepted set.
Breaking: default resize interpolation changed — pixel values and mAP may shift
The default resize backend changed from Albumentations (cv2 INTER_LINEAR, no antialias) to torchvision (BILINEAR + antialias=True). Resized pixel values differ slightly from previous versions, and mAP may drift on existing benchmarks. This affects the training split only — _route_transforms (rfdetr.datasets.coco) routes to Albumentations only when image_set == "train"; validation, test, prediction and export always resize through torchvision, unaffected by aug_config or augmentation_backend.
To restore the previous pixel-exact behaviour for training:
from rfdetr.datasets.aug_configs import AUG_CONFIG
train_config = TrainConfig(aug_config=AUG_CONFIG)
Installing rfdetr[augment] alone is not sufficient to pin this behaviour — with Albumentations installed, augmentation_backend="auto"/"cpu" (the default) auto-selects Albumentations for you, but identical code on a machine without [augment] installed silently falls back to torchvision instead. The only setting that pins the training resize backend regardless of what is installed is:
No setting restores Albumentations for validation/test/predict/export — that path is torchvision-only by design, not a configurable default.
Removed¶
The following APIs were deprecated in earlier releases and are removed as of v1.9. Update your code before upgrading.
Removed: rfdetr.util.* and rfdetr.deploy.* import paths
Deprecated since v1.6, removed in v1.9. Use the canonical replacements listed in the Upgrade 1.5 → 1.6 section.
Removed: build_namespace(model_config, train_config)
Deprecated since v1.7, removed in v1.9. Use build_model_from_config and build_criterion_from_config instead.
Removed: load_pretrain_weights(nn_model, model_config, train_config) with train_config
Deprecated since v1.7, removed in v1.9. Drop the train_config positional argument.
Removed: start_epoch kwarg in train()
Deprecated since v1.7, removed in v1.9. PyTorch Lightning resumes automatically via resume=.
Removed: do_benchmark kwarg in train()
Deprecated since v1.7, removed in v1.9. Use the rfdetr.export.benchmark module instead.
Removed: callbacks dict kwarg in train()
Deprecated since v1.7, removed in v1.9. Pass PTL Callback objects directly via the Lightning API instead.
Removed: misplaced config fields
The following TrainConfig and ModelConfig fields moved to their correct config class in v1.7; the deprecated compatibility shims are removed in v1.9. Passing one of these fields on its old config class now raises a pydantic validation error:
| Field | Removed from | Use in |
|---|---|---|
group_detr |
TrainConfig |
ModelConfig |
ia_bce_loss |
TrainConfig |
ModelConfig |
segmentation_head |
TrainConfig |
ModelConfig |
num_select |
TrainConfig |
ModelConfig |
cls_loss_coef |
ModelConfig |
TrainConfig |
Removed: RFDETRLarge silent fallback to deprecated Large config
RFDETRLarge() no longer catches checkpoint/config incompatibility errors and silently retries with RFDETRLargeDeprecatedConfig. Loading legacy deprecated-Large weights through RFDETRLarge now raises the original ValueError/RuntimeError instead of falling back. RFDETRLargeDeprecated itself is unaffected — use it directly to load those checkpoints.
Deprecated in v1.9 → Removed in v1.11¶
Deprecated: RFDETR.optimize_for_inference() renamed to RFDETR.inference()
optimize_for_inference(compile=..., batch_size=..., dtype=..., inplace=..., compile_backend=...) — renamed to inference() with the same signature at the time of deprecation. The current factory returns an independent predictor; see native inference snapshots.
Deprecated: TrainConfig.lr_drop and TrainConfig.lr_min_factor
lr_drop / lr_min_factor — pass them through lr_scheduler_kwargs instead. For the managed "step" / "cosine" presets the fields are still folded into lr_scheduler_kwargs (with a FutureWarning); set with an explicit scheduler they are inert and warn.
Upgrade 1.7 → 1.8¶
Breaking changes¶
Breaking in v1.8.2: default keypoint schema changed to active-first [17]
New checkpoints created from v1.8.2 onwards use class_id=0 for person. Legacy [0, 17] checkpoints are still supported — RF-DETR auto-detects the schema from the checkpoint at load time.
If your post-processing code offsets class IDs by 1 (common for background-first models), update it:
# Before (background-first [0, 17]: person was at class_id=1)
class_name = "person" if detection.class_id == 1 else "other"
# After (active-first [17]: person is at class_id=0)
class_name = "person" if detection.class_id == 0 else "other"
Use detection.data["class_name"] for schema-agnostic name resolution.
Breaking: rfdetr.datasets.aug_config renamed to rfdetr.datasets.aug_configs
The augmentation config module was renamed (singular → plural). If you import from it directly:
# Before
from rfdetr.datasets.aug_config import AUG_AGGRESSIVE
# After
from rfdetr.datasets.aug_configs import AUG_AGGRESSIVE
All preset constants (AUG_AGGRESSIVE, AUG_CONSERVATIVE, etc.) are unchanged.
Breaking: supervision>=0.29.0 now required
Required for sv.KeyPoints support. pip install rfdetr==1.8.0 pulls this automatically. If another dependency pins supervision<0.29.0, resolve the conflict manually.
Breaking: pyDeprecate constraint narrowed to >=0.9,<0.10
Was >=0.6,<0.8. If another package pins an older version, resolve with:
Upgrade 1.6 → 1.7¶
Breaking changes¶
Breaking: peft removed from the default install
LoRA fine-tuning now requires the lora extra. If you use LoRA adapters during training, update your install command.
Breaking: predict() stores source image in detections.metadata
predict() stores the source image in detections.metadata, not detections.data.
Breaking: pyDeprecate constraint changed to >=0.6,<0.8
Was >=0.3,<0.6. If another package pins an older version, resolve with:
Deprecated in v1.7 → Remove in v1.9¶
Deprecated: build_namespace() split into two functions
build_namespace(model_config, train_config) — use build_model_from_config or build_criterion_from_config instead.
# Before (deprecated)
from rfdetr.models import build_namespace
ns = build_namespace(model_config, train_config)
# After
from rfdetr.models import build_model_from_config, build_criterion_from_config
model = build_model_from_config(model_config)
criterion = build_criterion_from_config(model_config, train_config)
Deprecated: load_pretrain_weights() no longer takes train_config
load_pretrain_weights(nn_model, model_config, train_config) — drop the train_config positional argument.
Deprecated: config fields moved between ModelConfig and TrainConfig
Config fields placed in the wrong config object. Move them as shown:
| Field | Was in | Move to |
|---|---|---|
group_detr |
TrainConfig |
ModelConfig |
ia_bce_loss |
TrainConfig |
ModelConfig |
segmentation_head |
TrainConfig |
ModelConfig |
num_select |
TrainConfig |
ModelConfig |
cls_loss_coef |
ModelConfig |
TrainConfig |
Deprecated in v1.7 → Remove in v2.0¶
Deprecated: RFDETRBase replaced by size-specific classes
RFDETRBase defaulted to the small variant and is replaced by size-specific classes. Choose the variant that matches your previous model size. If you used RFDETRBase() without arguments, switch to RFDETRSmall().
Deprecated: RFDETRSegPreview replaced by size-specific segmentation classes
RFDETRSegPreview defaulted to the small variant and is replaced by size-specific segmentation classes. If you used RFDETRSegPreview() without arguments, switch to RFDETRSegSmall().
Upgrade 1.5 → 1.6¶
Breaking changes¶
Breaking: transformers minimum version raised to >=5.1.0
transformers minimum version raised to >=5.1.0,<6.0.0.
Projects pinned to transformers<5.0.0 must upgrade. If upgrading is not possible, pin rfdetr<1.6.0.
Breaking: PyPI extras renamed
PyPI extras renamed.
Update your pip install commands and requirements*.txt files.
| Old extra | New extra |
|---|---|
rfdetr[metrics] |
rfdetr[loggers] |
rfdetr[onnxexport] |
rfdetr[onnx] |
Breaking: draw_synthetic_shape() now returns a tuple
draw_synthetic_shape() now returns (image, polygon) instead of image.
Update every call site that unpacks only the image.
Deprecated in v1.6 → Removed in v1.8¶
Deprecated: simplify and force arguments in RFDETR.export()
RFDETR.export(..., simplify=..., force=...) — both arguments are no-ops. Remove them from your calls.
Deprecated in v1.6 → Remove in v1.9¶
Deprecated: rfdetr.util.* and rfdetr.deploy.* import paths
Backward-compatibility shims are still active but emit DeprecationWarning on import. Replace with the canonical paths listed in the table below.
| Deprecated module | Canonical replacement |
|---|---|
rfdetr.util.coco_classes |
rfdetr.assets.coco_classes |
rfdetr.util.misc |
rfdetr.utilities |
rfdetr.util.logger |
rfdetr.utilities.logger |
rfdetr.util.box_ops |
rfdetr.utilities.box_ops |
rfdetr.util.files |
rfdetr.utilities.files |
rfdetr.util.package |
rfdetr.utilities.package |
rfdetr.util.get_param_dicts |
rfdetr.training.param_groups |
rfdetr.util.drop_scheduler |
rfdetr.training.drop_schedule |
rfdetr.util.visualize |
rfdetr.visualize.data |
rfdetr.deploy |
rfdetr.export |
rfdetr.models.segmentation_head |
rfdetr.models.heads.segmentation |
Examples:
# Before (deprecated)
from rfdetr.util.coco_classes import COCO_CLASSES
from rfdetr.util.misc import get_rank, get_world_size, is_main_process, save_on_master
from rfdetr.util.logger import get_logger
from rfdetr.util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
from rfdetr.util.get_param_dicts import get_param_dict
from rfdetr.util.drop_scheduler import drop_scheduler
from rfdetr.util.visualize import save_gt_predictions_visualization
from rfdetr.deploy import export_onnx
from rfdetr.models.segmentation_head import SegmentationHead
# After
from rfdetr.assets.coco_classes import COCO_CLASSES
from rfdetr.utilities.distributed import get_rank, get_world_size, is_main_process, save_on_master
from rfdetr.utilities.logger import get_logger
from rfdetr.utilities.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
from rfdetr.training.param_groups import get_param_dict
from rfdetr.training.drop_schedule import drop_scheduler
from rfdetr.visualize.data import save_gt_predictions_visualization
from rfdetr.export._onnx.exporter import OnnxExporter # see Upgrade 1.10 → 1.11
from rfdetr.models.heads.segmentation import SegmentationHead
Upgrade 1.4 → 1.5¶
Breaking changes¶
Breaking: ModelConfig rejects unknown keyword arguments
ModelConfig now raises ValidationError on unknown keyword arguments.
Previously, unrecognised fields were silently ignored. Remove or rename any unrecognised keys you pass to ModelConfig(...).
Deprecated in v1.5 → Removed in v1.7¶
Deprecated: OPEN_SOURCE_MODELS replaced by ModelWeights enum
OPEN_SOURCE_MODELS constant — use the ModelWeights enum instead. A DeprecationWarning is emitted on access. See the API reference for available enum values.