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Changelog

RF-DETR release notes are maintained on GitHub Releases. Use the release feed to review versioned package changes, migration notes, and model updates.

Changelog

All notable changes to RF-DETR are documented here.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[1.7.0] — 2026-04-29

Added

  • augmentation_backend field on TrainConfig ("cpu" / "auto" / "gpu"): opt-in GPU-side augmentation via Kornia applied in RFDETRDataModule.on_after_batch_transfer after the batch is resident on the GPU. CPU path is unchanged and remains the default. Install with pip install 'rfdetr[kornia]'. Supports detection and segmentation (see below). (#1003)
  • Kornia GPU augmentation now supports instance segmentation: images, boxes, and per-instance masks are augmented in sync on the GPU. New public helper collate_masks packs [N_i, H, W] boolean masks into a [B, N_max, H, W] float32 tensor for Kornia; build_kornia_pipeline gains a with_masks: bool = False parameter; unpack_boxes gains an optional masks_aug tensor that re-binarises and filters masks in sync with boxes. Previously augmentation_backend="gpu"/"auto" was silently ignored for segmentation models; now it works identically to detection. Note: the mask buffer is [B, N_max, H, W] float32 — approximately 500 MB at B=8, N_max=50, H=W=560; use augmentation_backend="cpu" on cards with limited VRAM. (#1003, closes #997)
  • BuilderArgs — a @runtime_checkable typing.Protocol documenting the minimum attribute set consumed by build_model(), build_backbone(), build_transformer(), and build_criterion_and_postprocessors(). Enables static type-checker support for custom builder integrations. Exported from rfdetr.models. (#841)
  • build_model_from_config(model_config, train_config=None, defaults=MODEL_DEFAULTS) — config-native alternative to build_model(build_namespace(mc, tc)); accepts Pydantic config objects directly and constructs the internal namespace automatically. Exported from rfdetr.models. (#845)
  • build_criterion_from_config(model_config, train_config, defaults=MODEL_DEFAULTS) — config-native alternative to build_criterion_and_postprocessors(build_namespace(mc, tc)); returns a (SetCriterion, PostProcess) tuple. Exported from rfdetr.models. (#845)
  • ModelDefaults dataclass — exposes the 35 hardcoded architectural constants previously buried inside build_namespace(). Pass a dataclasses.replace(MODEL_DEFAULTS, ...) override to the new config-native builders to customise individual constants. Note: fields may be promoted to ModelConfig/TrainConfig in future phases. Exported from rfdetr.models. (#845)
  • MODEL_DEFAULTS — the canonical ModelDefaults singleton with production defaults. Exported from rfdetr.models. (#845)
  • RFDETR.predict(include_source_image=...) — opt-out flag (default True) to skip storing the source image in detections.metadata["source_image"]; set to False to reduce memory use when the image is not needed for annotation. (#912)
  • model_name is now stored in checkpoint files during training so that RFDETR.from_checkpoint() can resolve the correct model class directly from the checkpoint, without requiring the caller to know or pass a class hint. strip_checkpoint() preserves this key. Backward-compatible: checkpoints without model_name continue to resolve via pretrain_weights filename matching. (#895)
  • rfdetr_version is now stored in checkpoint files during training for provenance tracking and compatibility hints. strip_checkpoint() preserves this key. The key is omitted gracefully when the package version cannot be resolved (e.g. editable install without metadata). Backward-compatible: checkpoints without rfdetr_version continue to load normally. (#918)
  • notes parameter on RFDETR.train() and RFDETR.export() — embed arbitrary JSON-serialisable provenance metadata (labeller, date, class names, etc.) into best-model .pth checkpoints (under checkpoint["args"]["notes"]) and ONNX files (under the "rfdetr_notes" metadata property). String values are stored verbatim; all other types are JSON-encoded. (#1025, closes #1021)
  • RF_HOME environment variable controls where pretrained model weights are cached (default: ~/.roboflow/models). Bare filenames passed as pretrain_weights (e.g. "rf-detr-base.pth") are now resolved relative to this directory; paths with a directory component are used as-is with parent directories created automatically. (#130)
  • Grayscale and multispectral imagery support: RF-DETR models now accept inputs with any number of channels (not just 3). The pretrained DINOv2 patch-embedding weights are automatically adapted to the specified channel count at model construction time — no additional dependencies required. (#180, closes #75)
  • Training configuration is now saved to training_config.json in the output directory after training completes. The file captures the full TrainConfig, ModelConfig, effective training parameters, class names, and number of classes — useful for reproducibility and debugging predictions from older checkpoints. (#194)
  • dinov2_registers_windowed_small backbone is now available as a config option in ModelConfig.encoder. (#236)
  • rfdetr.from_checkpoint(path) — new top-level convenience function that loads a checkpoint and infers the correct model subclass automatically, without requiring the caller to specify a class. Equivalent to RFDETR.from_checkpoint(path) but importable directly from the rfdetr package. (#664)
  • ONNX export filenames now include the model variant name (e.g. rfdetr-medium.onnx) instead of the generic inference_model.onnx. Exporting multiple variants to the same directory no longer overwrites previous exports. (#910)
  • Background images (images without a matching label file) are now included in YOLO detection datasets as empty-detection samples instead of being silently dropped. Both detection and segmentation paths now use _LazyYoloDetectionDataset for consistent behaviour. (#915)
  • TFLite export via model.export(format="tflite"). Converts through ONNX using onnx2tf; FP32 and FP16 outputs are always produced, INT8 quantization is available with a calibration image directory: model.export(format="tflite", quantization="int8", calibration_data="path/to/images/"). Requires pip install 'rfdetr[onnx,tflite]'. (#920)
  • PyTorch Lightning .ckpt files are now accepted as pretrain_weights. Keys are automatically normalized from PTL format (state_dict with model.-prefixed keys, hyper_parameters → args) so that load_pretrain_weights, class-name extraction, and compatibility checks work without manual conversion. (#951)
  • skip_best_epochs parameter for RFDETR.train() and TrainConfig: the first N epochs are excluded from best-checkpoint selection and early-stopping comparison, preventing strong pretrained weights or resumed checkpoints from locking in a suboptimal early score. (#1000, closes #789)
  • TFLite inference now decodes segmentation mask outputs into sv.Detections.mask. Mask logits are upsampled to the source image size using Pillow bilinear resampling and thresholded at zero, matching the behaviour of PostProcess.forward. The mask tensor is detected by output name ("masks" substring) with a rank-4 shape fallback. (#1053)
  • PretrainWeightsCompatibilityWarning — new warning class emitted when a ModelConfig override (e.g. custom encoder, num_queries, or num_feature_levels) risks breaking pretrained weight loading. Importable as from rfdetr.config import PretrainWeightsCompatibilityWarning for targeted filtering. (#1017)

Changed

  • peft is no longer installed as part of the default rfdetr package. It has moved to the [lora] and [train] optional extras. If you use LoRA fine-tuning, install with pip install 'rfdetr[lora]'. (#838)
  • Native RLE annotation support in the COCO segmentation pipeline: convert_coco_poly_to_mask now explicitly detects and decodes both compressed (string counts) and uncompressed (int-list counts) RLE formats alongside existing polygon support. Malformed annotations now raise instead of being silently swallowed. (#897)
  • Pinned PyTorch Lightning to exclude known-compromised versions. (#1020)

Deprecated

  • build_namespace(model_config, train_config) — no longer used internally and deprecated in this release; use build_model_from_config, build_criterion_from_config, or _namespace_from_configs directly. It will be removed in v1.9 and currently emits a DeprecationWarning on use. (#845)
  • load_pretrain_weights(nn_model, model_config, train_config) — the train_config positional argument is deprecated and will be removed in v1.9; it is no longer used internally. Omit it: load_pretrain_weights(nn_model, model_config). Passing a non-None value emits a DeprecationWarning. (#845)
  • TrainConfig.group_detr (architecture decision → ModelConfig), TrainConfig.ia_bce_loss (loss type tied to architecture family → ModelConfig), TrainConfig.segmentation_head (architecture flag → ModelConfig), TrainConfig.num_select (postprocessor count → ModelConfig; SegmentationTrainConfig users: remove the num_select override — the model config value is always used), ModelConfig.cls_loss_coef (training hyperparameter → TrainConfig) — each now emits DeprecationWarning when set on the wrong config object and will be removed in v1.9. (#841)
  • RFDETRBase — use RFDETRNano, RFDETRSmall, RFDETRMedium, or RFDETRLarge instead. Emits FutureWarning on instantiation; scheduled for removal in v2.0. (#900)
  • RFDETRSegPreview — use RFDETRSegNano, RFDETRSegSmall, RFDETRSegMedium, or RFDETRSegLarge instead. Emits FutureWarning on instantiation; scheduled for removal in v2.0. (#900)
  • rfdetr.util and rfdetr.deploy sub-modules are deprecated and will be removed in v1.9. A __getattr__ hook on the rfdetr package now emits a clear ImportError with migration guidance when these legacy paths are accessed. (#839)

Fixed

  • Fixed TFLite export (format="tflite") producing detection scores that collapse to ~0.02 (vs ~0.62 from ONNX) on real inputs. Root cause is a long-standing onnx2tf bug (PINTO0309/onnx2tf#274) where the GridSample lowering diverges numerically from ONNX while onnx2tf's own validator silently passes. RF-DETR's deformable cross-attention uses F.grid_sample once per decoder layer; drift compounds and is amplified by the attention softmax. The converter now detects onnx2tf's GridSample → pseudo-GridSample replacement kwarg at runtime (introspecting convert() via inspect.signature) and passes it as True; a warning is logged when the kwarg is absent. (#1041)

  • WindowedDinov2WithRegistersEmbeddings.forward() now raises ValueError (instead of silently failing under -O) when input spatial dimensions are not divisible by patch_size * num_windows, with a clear message identifying the divisor and actual shape. (#167)

  • Fixed _namespace.py: num_select in the builder namespace now always reads from ModelConfig, eliminating a regression where TrainConfig.num_select (default 300) silently overrode model-specific values of 100–200 for segmentation variants (RFDETRSegNano, RFDETRSegSmall, RFDETRSegMedium, RFDETRSegLarge, RFDETRSegPreview). Post-processing now uses the correct top-k count for each model. (#841)

  • Fixed models/weights.py: load_pretrain_weights now correctly auto-aligns the model head when the checkpoint has fewer classes than the configured default, preventing a silent mismatch when num_classes was not explicitly set by the caller. (#845)

  • Fixed models/weights.py: load_pretrain_weights now slices refpoint_embed.weight and query_feat.weight per-group when reshaping checkpoint queries, instead of taking a flat tensor[: num_queries * group_detr] slice. The flat slice silently scrambled groups 1+ when num_queries decreased and group_detr > 1; inference (which only reads group 0) was unaffected, but training-resume corrupted query embeddings for groups 1 onward. (#1019)

  • Fixed YOLO segmentation training on large datasets hitting OS out-of-memory: supervision.DetectionDataset.from_yolo(force_masks=True) was eager-rasterising H×W boolean masks for every image at dataset construction time (≈1 GB/1 000 images at 1024 px). A new _LazyYoloDetectionDataset stores polygon coordinates only and defers dense mask rasterisation to __getitem__, keeping RAM proportional to annotation count rather than (N × H × W). (#851)

  • Fixed ONNX/TRT dynamic batch inference: gen_encoder_output_proposals and Transformer.forward extracted the batch size as a Python int and passed it to torch.full, .view(N_, ...), .expand(N_, ...), and .repeat(bs, ...), causing the ONNX tracer to bake the training batch size (e.g. 8) as a compile-time constant. TRT engines built with --minShapes smaller than the trace batch would fail at inference with Reshape: reshaping failed. All six call sites are now replaced with ONNX-symbolic equivalents (zeros_like, -1 reshapes, expand(memory.shape[0], ...)), keeping the batch dimension fully dynamic. (#950, closes #949)

  • Fixed training failure when square_resize_div_64=False: the non-square resize pipeline (SmallestMaxSize + LongestMaxSize) did not guarantee output dimensions divisible by patch_size * num_windows, causing WindowedDinov2WithRegistersEmbeddings.forward to raise ValueError. A PadIfNeeded step (with pad_height_divisor and pad_width_divisor set to patch_size * num_windows) is now appended after the resize pair in both the train and val/test pipelines. (#991, closes #983)

  • Fixed non-square batch padding correctness: batch-level block_size rounding is now applied in the DataLoader collator (nested_tensor_from_tensor_list via make_collate_fn) in addition to the transform-level PadIfNeeded, ensuring divisibility by patch_size * num_windows survives any Compose reordering and applies uniformly to custom evaluation harnesses. (#992)

  • Fixed RFDETRModelModule.on_load_checkpoint crashing with RuntimeError when resuming training from a checkpoint saved at a different image resolution: DINOv2 positional embeddings in the checkpoint are now bicubic-interpolated to match model_config.positional_encoding_size before PyTorch Lightning applies the state dict. (#1002, closes #998)

  • Fixed RFDETRLarge initialization showing two conflicting ValueErrors (for patch_size=14 and patch_size=16) when the deprecated-config fallback retry also fails. The fallback now re-raises the original error without chained context, so users see a single deterministic message. (#975)

  • Fixed RFDETRModelModule.__init__ crashing with RuntimeError: size mismatch for backbone.0.encoder.encoder.embeddings.position_embeddings when training segmentation models at a custom resolution (e.g. RFDETRSegLarge(resolution=1008).train(...)). The training entry path now delegates to the canonical load_pretrain_weights helper, which bicubic-interpolates the DINOv2 positional embeddings before load_state_dict. (#1040, closes #1038, #1023)

  • Fixed TFLite detection scores collapsing for all queries (scores ~0.02 vs ~0.62 from ONNX) when GridSample was used as an onnx2tf pseudo-operator. The GridSample ONNX node is now rewritten to Gather-based integer-index arithmetic before conversion, eliminating all numerical drift from attention position sampling. This supersedes the pseudo-GridSample runtime-kwarg approach added in #1041. (#1054)

  • Fixed class_name lookup for pretrained COCO models: COCO category IDs are sparse (1–90 with gaps for 80 classes), so flat 0-based indexing returned the wrong name (e.g. class_id=18 ("dog") incorrectly returned class_names[18] instead of class_names[16]). Detection now uses a coco_id → class_name mapping built from the canonical COCO_CLASSES list so every COCO category resolves to its correct label. Fine-tuned models continue to use direct 0-based indexing unchanged. (#1051)

[1.6.5] — 2026-04-22

Breaking Changes

  • predict() now stores the source image in detections.metadata["source_image"] instead of detections.data["source_image"]. supervision indexes every value in data by the detection mask; source_image is per-image, not per-detection, so boolean/integer indexing raised IndexError. Moving it to metadata (passed through unchanged) fixes the issue. Update any code that reads detections.data["source_image"]. (#972, #968)

Fixed

  • Fixed segmentation training crash on T4 and P100 GPUs: cuDNN engine selection fails for depthwise convolution backward on some CUDA stacks (Kaggle, Colab). A custom autograd.Function now disables cuDNN in both forward and backward passes. (#967)
  • Fixed ema_segm_mAP_50_95 and ema_segm_mAP_50 being computed from the base (non-EMA) metric accumulator instead of the EMA accumulator, producing misleading validation scores for segmentation models. (#980)
  • Fixed BestModelCallback losing the best EMA score on training resume because _best_ema was not persisted in state_dict(). (#973)
  • Fixed positional_encoding_size not updating when resolution is set at construction time (e.g. RFDETRLarge(resolution=640)), causing shape mismatches during forward. A model validator now auto-syncs PE size. (#956)
  • Fixed pretrained weight loading crash with custom resolution: DINOv2 positional embeddings are now bicubic-interpolated to match the target grid before load_state_dict. (#964)
  • Fixed validate_checkpoint_compatibility producing a cryptic RuntimeError on patch_size mismatch when checkpoint lacks explicit args.patch_size. The function now infers patch_size from the DINOv2 projection weight shape and raises a descriptive ValueError. (#971)
  • Fixed predict() storing detections.data["source_shape"] as a Python tuple, which caused TypeError whenever sv.Detections was iterated. The value is now an np.ndarray of shape (N, 2) and dtype int64. (#966, #963)
  • Fixed predict() emitting a misleading "class_id out of range" warning for the background/no-object class (class index num_classes). Background-class detections now map data["class_name"] to "__background__" without any warning. (#970)

[1.6.4] — 2026-04-10

Changed

  • predict() now includes class_name in detections.data, mapping each detection's 0-indexed class ID to its human-readable name. (#914)

Fixed

  • Fixed segmentation multi-GPU DDP training crash: build_trainer() now wraps strategy="ddp" with DDPStrategy(find_unused_parameters=True) when segmentation_head=True. The segmentation head's sparse_forward() leaves parameters unused on some forward steps; plain "ddp" raised RuntimeError: It looks like your LightningModule has parameters that were not used in producing the loss. Non-segmentation DDP and other strategies are unchanged. (#942, #947)
  • Fixed fused AdamW crash under FP32 multi-GPU training: configure_optimizers() and clip_gradients() now gate fused AdamW on the trainer's actual precision (requiring a BF16 variant) rather than GPU capability alone. On Ampere+ hardware torch.cuda.is_bf16_supported() is always True, so the old code enabled fused AdamW even with precision="32-true", causing RuntimeError: params, grads, exp_avgs, and exp_avg_sqs must have same dtype, device, and layout from DDP gradient bucket view stride mismatches. (#942, #947)
  • Fixed multi-GPU DDP training crashing in Jupyter notebooks and Kaggle: replaced fork-based ddp_notebook strategy with a spawn-based DDP strategy that avoids OpenMP thread pool corruption after fork(). (#928)
  • Fixed RFDETR.train(resolution=...) being silently ignored — the kwarg is now applied to model_config before training begins, with validation that the value is divisible by patch_size * num_windows. (#933)
  • Fixed save_dataset_grids being silently a no-op — DatasetGridSaver is now wired into the training loop, saving sample grids to {output_dir}/dataset_grids/ when enabled. Grid save failures are caught without interrupting training. (#946)
  • Fixed partial gradient-accumulation windows at the tail of training epochs: the training dataset is now padded to an exact multiple of effective_batch_size * world_size, ensuring every optimizer step uses a full gradient window. Workaround for pytorch-lightning#19987. (#937)
  • Fixed torch.export.export failing on the transformer decoder by threading spatial_shapes_hw through all decoder layers. (#936)
  • download_pretrain_weights() no longer overwrites fine-tuned checkpoints that share a filename with a registry model (e.g. rf-detr-nano.pth). Previously, an MD5 mismatch would fall through to _download_file() and silently replace the user's weights with the original COCO checkpoint. The function now returns early whenever the file exists and redownload=False, regardless of MD5 status — a warning is emitted when the hash differs. Pass redownload=True to force a fresh download. (#935)

[1.6.3] — 2026-04-02

Changed

  • predict() now stores the original image and its shape on returned sv.Detections objects — detections.data["source_image"] (NumPy array) and detections.data["source_shape"] (NumPy array of shape (N, 2) where each row is [height, width]) let you annotate results without loading the image separately. (#892)
  • RFDETR.train() auto-detects num_classes from the dataset directory when not explicitly set, reinitializing the detection head to the correct class count automatically. A warning is emitted when the configured value differs from the dataset count. (#893)
  • optimize_for_inference() now accepts dtype as a string name (e.g. "float16") in addition to a torch.dtype object; invalid dtype inputs uniformly raise TypeError. (#899)

Fixed

  • Fixed models/lwdetr.py: reinitialize_detection_head now replaces nn.Linear modules instead of mutating .data tensors in-place, ensuring out_features metadata stays consistent with the actual weight shape. This prevents ONNX export and torch.jit.trace from emitting stale (pre-fine-tuning) class counts for fine-tuned models. (#904)
  • Fixed RFDETR.optimize_for_inference() leaking a CUDA context on multi-GPU setups: the deep-copy, export, and JIT-trace steps now run inside torch.cuda.device(device) to pin the context to the correct device. (#899)
  • Fixed optimize_for_inference() leaving inconsistent state on failure: prior optimized state is now reset and flags are committed only after a successful build/trace; temp download files use unique per-process paths to avoid parallel worker collisions.
  • Fixed deploy_to_roboflow failing with FileNotFoundError after PyTorch Lightning migration: class_names.txt is now written to the upload directory and args.class_names is populated before saving the checkpoint. (#890)

[1.6.2] — 2026-03-27

Added

  • RFDETR.predict(shape=...) — optional (height, width) tuple overrides the default square inference resolution; useful when matching a non-square ONNX export. Both dimensions must be positive integers divisible by patch_size × num_windows as determined by the model configuration. (#866)

Changed

  • ModelConfig.device and RFDETR.train(device=...) now accept torch.device objects and indexed device strings such as "cuda:0". Values are normalized to canonical torch-style strings. RFDETR.train() warns when an unmapped device type is passed to PyTorch Lightning auto-detection. (#872)

Fixed

  • Fixed ONNX export ignoring an explicit patch_size argument: export() and predict() now resolve patch_size from model_config by default, validate it strictly (positive integer, not bool), and enforce that (H, W) dimensions are divisible by patch_size × num_windows. (#876)
  • Fixed ONNX export for models with dynamic batch dimensions — replaced H_.expand(N_) with torch.full for Python-int spatial dims to eliminate tracer failures. (#871)

[1.6.1] — 2026-03-25

Deprecated

  • RFDETR.export(..., simplify=..., force=...) — both arguments are now no-ops and emit a DeprecationWarning. RF-DETR no longer runs ONNX simplification automatically; remove these arguments from your calls. They will be removed in v1.8. (#861)

Fixed

  • Fixed RFDETR.train(): a missing rfdetr[train] install (e.g. plain pip install rfdetr in Colab) now raises an ImportError with an actionable message — pip install "rfdetr[train,loggers]" — instead of a raw ModuleNotFoundError with no install hint. (#858)
  • Fixed AUG_AGGRESSIVE preset: translate_percent was (0.1, 0.1) — a degenerate range that forced Albumentations Affine to always translate right/down by exactly 10%. Corrected to (-0.1, 0.1) for symmetric bidirectional translation. (#863)
  • Fixed PTL training path: latest.ckpt and per-interval checkpoints (checkpoint_interval_N.ckpt) are now properly written and restored on resume. (#847)
  • Fixed BestModelCallback and checkpoint monitor raising MisconfigurationException on non-eval epochs when eval_interval > 1 — monitor key absence is now handled gracefully. (#848)
  • Fixed protobuf version constraint in the loggers extra to guard against TensorBoard descriptor crash (TypeError: Descriptors cannot be created directly) with protobuf ≥ 4. (#846)
  • Fixed duplicate ModelCheckpoint state keys when checkpoint_interval=1; last.ckpt is omitted in that configuration to avoid collision. (#859)

[1.6.0] — 2026-03-20

Added

  • PyTorch Lightning training building blocks: RFDETRModelModule, RFDETRDataModule, build_trainer(), and individual callbacks (RFDETREMACallback, COCOEvalCallback, BestModelCallback, DropPathCallback, MetricsPlotCallback) — all standard PTL components, swap/subclass/extend any piece. Level 3: rfdetr fit --config CLI with zero Python required. (#757, #794)
  • Multi-GPU DDP via model.train(): strategy, devices, and num_nodes added to TrainConfig; single-GPU behaviour unchanged when omitted. (#808)
  • batch_size='auto': CUDA memory probe finds the largest safe micro-batch size, then recommends grad_accum_steps to reach a configurable effective batch target (default 16 via auto_batch_target_effective). (#814)
  • ModelContext promoted from _ModelContext to a public, exported API — inspect class_names, num_classes, and related metadata via model.context after training. (#835)
  • backbone_lora and freeze_encoder added as first-class fields in ModelConfig. (#829)
  • generate_coco_dataset(with_segmentation=True) produces COCO polygon annotations alongside bounding boxes for segmentation fine-tuning with synthetic data. (#781)
  • set_attn_implementation("eager" | "sdpa") on the DINOv2 backbone — switch attention implementation at runtime. (#760)
  • eval_max_dets, eval_interval, and log_per_class_metrics added to TrainConfig.
  • python -m rfdetr entry point alongside the rfdetr console script.
  • py.typed marker — RF-DETR is now PEP 561–compliant.

Changed

  • Breaking: Minimum transformers version bumped to >=5.1.0,<6.0.0. The DINOv2 windowed-attention backbone now uses the transformers v5 API (BackboneMixin._init_transformers_backbone(), removed head_mask plumbing). Projects still on transformers v4 must pin rfdetr<1.6.0. (#760)
  • Breaking: PyPI install extras renamed — rfdetr[metrics] → rfdetr[loggers], rfdetr[onnxexport] → rfdetr[onnx].
  • draw_synthetic_shape now returns Tuple[np.ndarray, List[float]] instead of np.ndarray. The second element is a flat COCO-style polygon list [x1, y1, x2, y2, …]. Any caller that previously did img = draw_synthetic_shape(...) must be updated to img, polygon = draw_synthetic_shape(...). (#781)
  • Albumentations version constraint broadened to >=1.4.24,<3.0.0; RandomSizedCrop configs using height/width kwargs are automatically adapted to the 2.x size=(height, width) API. (#786)
  • Current learning rate is now shown in the training progress bar alongside loss. (#809)
  • supervision, pytorch_lightning, and other heavy dependencies are now imported lazily (on first use) rather than at module load, reducing cold-import time in inference-only environments. (#801)

Deprecated

  • rfdetr.deploy.* — redirects to rfdetr.export.* with a DeprecationWarning. Migrate before v1.7.
  • rfdetr.util.* — redirects to rfdetr.utilities.* with a DeprecationWarning. Migrate before v1.7.

Fixed

  • Raised a descriptive ValueError instead of a cryptic RuntimeError / tensor-size mismatch when a checkpoint is incompatible with the current model architecture — covers segmentation_head mismatch and patch_size mismatch. (#810)
  • Fixed class_names not reflecting dataset labels on model.predict() after training — class names are now synced from the dataset so inference always uses the correct label list. (#816)
  • Fixed detection head reinitialization overwriting fine-tuned weights when loading a checkpoint with fewer classes than the model default. The second reinitialize_detection_head call now fires only in the backbone-pretrain scenario. (#815, #509)
  • Fixed grid_sample and bicubic interpolation silently falling back to CPU on MPS (Apple Silicon) — both now run natively on the MPS device. (#821)
  • Fixed early_stopping=False in TrainConfig being silently ignored — the setting now propagates correctly. (#835)
  • Fixed AttributeError crash in update_drop_path when the DINOv2 backbone layer structure does not match any known pattern.
  • Added warning when drop_path_rate > 0.0 is configured with a non-windowed DINOv2 backbone, where drop-path is silently ignored.
  • Fixed ValueError: matrix entries are not finite in HungarianMatcher when the cost matrix contains NaN or Inf — non-finite entries are replaced with a finite sentinel before linear_sum_assignment, warning emitted at most once per matcher instance. (#787)
  • Fixed YOLO dataset validation rejecting data.yml — both .yaml and .yml are now accepted. (#777)
  • Silently dropped degenerate bounding boxes (zero width or height) before Albumentations validation instead of raising ValueError. (#825)

[1.5.2] — 2026-03-04

Added

  • Added peak GPU memory (max_mem in MB) to training and evaluation progress bars on CUDA; omitted on CPU and MPS. (#773)

Fixed

  • Fixed aug_config being silently ignored when training on YOLO-format datasets — build_roboflow_from_yolo never forwarded the value, so transforms always fell back to the default. (#774)
  • Fixed segmentation evaluation metrics not being written to results_mask.json during validation and test runs. (#772)
  • Fixed AttributeError crash in update_drop_path when the DINOv2 backbone layer structure does not match any known pattern — _get_backbone_encoder_layers now returns None for unrecognised architectures. (#762)
  • Fixed drop_path_rate not being forwarded to the DINOv2 model configuration; stochastic depth was never applied even when explicitly set. Added a warning when drop_path_rate > 0.0 is used with a non-windowed backbone. (#762)
  • Fixed incorrect COCO hierarchy filtering that excluded parent categories from the class list. (#759)
  • Fixed evaluation metric corruption on 1-indexed Roboflow datasets caused by a flawed contiguity check in _should_use_raw_category_ids. (#755)

[1.5.1] — 2026-02-27

Added

  • Added support for nested Albumentations containers (OneOf, Sequential) inside aug_config. (#752)

Changed

  • Migrated dataset transform pipeline to torchvision-native Compose, ToImage, and ToDtype; Normalize now defaults to ImageNet mean/std. (#745)

Fixed

  • Fixed RFDETRMedium missing from the public API — __all__ contained a duplicate RFDETRSmall entry. (#748)
  • Fixed AR50_90 reporting an incorrect value in MetricsMLFlowSink due to a wrong COCO evaluation index. (#735)
  • Fixed supercategory filtering in _load_classes for COCO datasets with flat or mixed supercategory structures. (#744)
  • Fixed crash in geometric transforms when a sample contained zero-area or empty masks. (#727)
  • Fixed segmentation training on Colab — DepthwiseConvBlock now disables cuDNN for depthwise separable convolutions. (#728)
  • Pinned onnxsim<0.6.0 to prevent pip install from hanging indefinitely. (#749)

[1.5.0] — 2026-02-23

Added

  • Added custom training augmentations via aug_config in model.train() — accepts a dict of Albumentations transforms, a built-in preset (AUG_CONSERVATIVE, AUG_AGGRESSIVE, AUG_AERIAL, AUG_INDUSTRIAL), or {} to disable. Bounding boxes and segmentation masks are transformed automatically. (#263, #702)
  • Added save_dataset_grids=True in TrainConfig to write 3×3 JPEG grids of augmented samples to output_dir before training begins. (#153)
  • Added ClearML logger: set clearml=True in TrainConfig to stream per-epoch metrics to ClearML. (#520)
  • Added MLflow logger: set mlflow=True in TrainConfig to log runs and metrics to MLflow with custom tracking URI support. (#109)
  • Added live progress bar for training and validation with structured per-epoch logs. (#204)
  • Added device field to TrainConfig for explicit device selection. (#687)
  • ModelConfig now raises an error on unknown parameters, preventing silent misconfiguration. (#196)

Changed

  • Deprecated OPEN_SOURCE_MODELS constant in favour of ModelWeights enum. (#696)
  • Added MD5 checksum validation for pretrained weight downloads. (#679)

Fixed

  • Fixed Albumentations bool-mask crash during segmentation training. (#706)
  • Fixed UnboundLocalError when resuming training from a completed checkpoint. (#707)
  • Prevented corruption of checkpoint_best_total.pth via atomic checkpoint stripping. (#708)
  • Fixed PyTorch 2.9+ compatibility issue with CUDA capability detection. (#686)
  • Fixed dtype mismatch error when use_position_supervised_loss=True. (#447)
  • Fixed inconsistent return values from build_model. (#519)
  • Fixed positional_encoding_size type annotation (bool → int). (#524)
  • Fixed ONNX export output_names to include masks when exporting segmentation models. (#402)
  • Fixed num_select not being updated correctly during segmentation model fine-tuning. (#399)
  • Fixed np.argwhere → np.argmax misuse. (#536)
  • Fixed COCO sparse category ID remapping for non-contiguous or offset category IDs. (#712)
  • Fixed segmentation mask filtering when using aggressive augmentations. (#717)

[1.4.3] — 2026-02-16

Changed

  • Pretrained weight downloads now validate against an MD5 checksum to detect corrupted files. (#679)

Fixed

  • Fixed deploy_to_roboflow failing for segmentation model exports. (#578)
  • Fixed missing info key in COCO export format. (#681)

[1.4.2] — 2026-02-12

Added

  • Added generate_coco_dataset() utility for generating synthetic COCO-format datasets with configurable class counts, split ratios, and bounding box annotations. (#617)
  • Added run_test=False to TrainConfig — skip test-split evaluation when your dataset has no test set. (#628)

Changed

  • model.predict() now accepts image URLs directly — no need to download images before inference. (#629)
  • Plus models (RFDETRXLarge, RFDETR2XLarge) are now distributed as a separate rfdetr_plus package under the Roboflow Model License. (#645)

Fixed

  • Fixed segmentation ONNX export failure. (#626)

[1.4.1] — 2026-01-30

Added

  • Added native YOLO dataset format support alongside COCO. (#74)
  • Added --print-freq CLI argument to control training log frequency. (#603)

Changed

  • Pinned transformers to <5.0.0 to prevent incompatibility with the transformers v5 API. (#599)

Fixed

  • Fixed class count mismatch in train_from_config for Roboflow-uploaded datasets. (#588)
  • Improved num_classes mismatch warning messages to be actionable rather than misleading. (#261)
  • Fixed CLI crash when specifying the device argument. (#246)

[1.4.0] — 2026-01-22

Headline release introducing new pre-trained model sizes — L, XL, and 2XL for object detection, and the full N/S/M/L/XL/2XL range for instance segmentation. Also added YOLO format training support, simplified the dependency footprint by removing several heavy packages (cython, fairscale, timm, einops, and others), and fixed per-class precision/recall/F1 computation. Drops Python 3.9 support.