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Changelog

0.28.0

Removed (BREAKING)

  • MediaPipe is no longer supported. The mediapipe extra and every symbol coupled to it have been removed. Consumers comparing against BackendType.MEDIAPIPE will hit AttributeError. Roboflow Universe payloads of type mediapipe-model-package-v1 are now silently filtered by MODEL_PACKAGE_PARSERS.get(...). Removed symbols:
  • inference_models.models.mediapipe_face_detection.MediaPipeFaceDetector
  • inference_models.model_pipelines.face_and_gaze_detection.FaceAndGazeDetectionMPAndL2CS
  • BackendType.MEDIAPIPE
  • mediapipe_package_matches_runtime_environment and its entry in MODEL_TO_RUNTIME_COMPATIBILITY_MATCHERS
  • Models registry entry for ("mediapipe-face-detector", KEYPOINT_DETECTION_TASK, BackendType.MEDIAPIPE)
  • BACKEND_PRIORITY[BackendType.MEDIAPIPE]
  • Pipelines registry's face-and-gaze-detection entry + mediapipe/face-detector default parameter
  • MediapipeModelPackageV1, parse_mediapipe_model_package, and the "mediapipe-model-package-v1" entry in MODEL_PACKAGE_PARSERS
  • RuntimeXRayResult.mediapipe_available and is_mediapipe_available()
  • INFERENCE_MODELS_MEDIAPIPE_FACE_DETECTOR_DEFAULT_CONFIDENCE
  • The [project.optional-dependencies] mediapipe extra in pyproject.toml

The standalone L2CSNetOnnx (under inference_models.models.l2cs) is unaffected and remains supported.

Fixed

  • RFDetr pre- and post-processing aligned with training transforms. Pre-processing replaced with a dedicated PIL → F.resize → F.to_tensor → F.normalize chain matching the training pipeline. For model packages with non-stretch dataset_version_resize_dimensions, the dataset-version resize (cv2 letterbox / center-crop) runs first, then the PIL stretch to training_input_size. Post-processing uses topk-flat across (queries × classes) via shared select_topk_predictions. Fixes a cross-backend divergence at low confidence thresholds.
  • Fixed a bug where 'best' and 'default' confidence modes were not correctly handled by RoboflowInstantHF models.

0.27.2

Fixed

  • Temporarily disabled flash-attention in GLM-OCR for Jetsons, due to incompatibility detected before release.

0.27.1

Added

  • Improved logging for auto-negotiation of model packages.

0.27.0

Added

  • COCO RLE masks format for all instance segmentation predictions and all models supported in the library were patched. InstanceDetections mask can now be InstancesRLEMasks object which follows the structure of pycocotools masks (providing memory-efficient alternative for dense representation). Clients who want to use the format, should pass mask_format="rle" to **kwargs of model forward pass.

Changed

  • The change with RLE masks format yielded change to base interface of Instance Segmentation models - new abstract property supported_mask_formats was added, which is a breaking change for local-code instance-segmentation models. We are not aware of anyone using the library in such mode currently, due to the maturity of the library, so we are introducing this change, such that the interface does not implicitly enforce supported format.

  • Representation of InstanceDetections changed - new InstancesRLEMasks format is now an alternative for torch.Tensor used for dense mask representation. This change is considered non-breaking, as alternative representation must be requested by the caller.

0.26.1

Changed

  • For Roboflow weights provider, Roboflow License Server proxy transitioned into Roboflow Secure Gateway, altering naming conventions of all helper functions which are considered private interface of weights provider (hence should not be considered breaking for any clients). Along with this change, LICENSE_SERVER environmental variable controlling the proxy address was replaced to be SECURE_GATEWAY - old variable will be deleted in the release following after the end of Q3 2026.

0.26.0

Added

  • Bringing back changes to filtering proposed in retracted release 0.25.0 along with fixes for bugs which caused retraction.

0.25.2

Fixed

  • OWLv2 compilation procedure clash with transformers~=5.5 brought to dependencies along with 0.25.1 release and Gemma 4.

0.25.1

Added

  • Documentation for Gemma 4 multimodal models (Gemma4HF / gemma4_hf.py): dedicated model page, catalog and site navigation updates, home page pointer, and environment variables for INFERENCE_MODELS_GEMMA4_* defaults.

0.25.0 (retracted)

Added

  • post_process(...) on object detection, instance segmentation, keypoint detection, classification, and semantic segmentation models now accepts confidence as "best" (use per-class or global thresholds from RecommendedParameters when available), "default" (model's built-in default), or a float override. Shared NMS helpers accept a per-class torch.Tensor for single-pass per-class filtering.

0.24.4

Changed

  • Behavior of Roboflow weights provider was changed - instead of throwing error each time any known model package is fetched with manifest not passing validation - it warns about this fact and skips the package. This change is dictated by potential negative impact on stability which malformed manifests could have, in the face of broader change on Roboflow platform making it possible tp externally register packages - sanitization and validation is enabled on registry API side, but we introduce defensive change here to prevent potential instability.

Added

  • RF-DETR NAS capabilities for Instance Segmentation

0.24.3

Changed

  • Added sigmoid smoothing for instance-segmentation masks in YOLOv8, YOLOv11, YOLOv12 models family. Smoothing can be enabled / disabled via masks_smoothing_enabled parameter of post_process(...) method (which can be passed as **kwarg to forward(...)) with default set with INFERENCE_MODELS_YOLO_ULTRALYTICS_DEFAULT_MASKS_SMOOTHING_ENABLED (set to True). Additionally, the binarization threshold for masks can be controlled via masks_binarization_threshold parameter - default to be controlled with INFERENCE_MODELS_YOLO_ULTRALYTICS_DEFAULT_MASKS_BINARIZATION_THRESHOLD (set to 0.5 or 0.0 depending on INFERENCE_MODELS_YOLO_ULTRALYTICS_DEFAULT_MASKS_SMOOTHING_ENABLED).

Instance-segmentation masks will change

Due to smoothing, there is slight change to segmentation masks expected - mainly regarding edges of predictions which should be smoother now. Change is dictated by alignment to old inference versions behaviour, effectively drifting from ultralytics post-processing.

0.24.2

Fixed

  • Issue with INFERENCE_HOME derived paths issues when running on Windows (lack /tmp/cache dereference to Windows path).

0.24.1

Changed

  • Added optional field alternatives_errors to ModelPackageAlternativesExhaustedError, making it possible to report to the caller what types of errors happened during the load - making it possible to deduce if problem with loading is recoverable.

0.24.0

Added

  • Support for Roboflow License Server proxy in Roboflow weights provider

0.23.0

Added

  • Support for CUDA 13.0 on x86 architecture - as a result of torch 2.11 release which makes CUDA 13.0 default version

0.22.1

Added

  • Ability to restrict maximum input resolution for models

  • Restriction of input resolution for RF-DETR - providing ability for caller to avoid OOM when loading models with large input resolutions

  • New type of error ModelPackageRestrictedError - to manifest restrictions of runtime environment with package

0.22.0

Added

  • GLM-OCR model added to models zoo

0.21.1

Fixed

  • Lack of model package features denoted in auto-negotiation cache entries was causing errors while re-initialization of models which had required_features denoted in model registry.

0.21.0

Added

  • Support for CUDA Graphs in TRT backend - all TRT models got upgraded - added ability to run with CUDA graphs, at the expense of additional VRAM allocation, but with caller control on how many execution contexts for different input shapes should be allowed.

0.20.2

Added

  • Ability to override certain aspects of model pre-processing (like center-crop, contrast enhancement or grayscale which may be performed by caller).

0.20.1

Fixed

  • AnyModel typing regarding semantic segmentation model

0.20.0

Added

  • Support for transformers>=5

  • Model registry feature allowing to treat specific model features as required during auto-negotiation

0.19.4

Fixed

  • CUDA stream synchronization issues in TRT models.

0.19.3

Fixed

  • Post-processing for RF-DETR segmentation model - missing remapping for class ids regarding masks.

0.19.2

Fixed

  • Changed the default ranking for model packages in AutoLoader - ONNX to be preferred over Torch.

0.19.1

Fixed

  • Fixed issue with RF-DETR model post-processing causing all results to be empty (TRT implementation)

0.19.0

First stable release of inference-models library.

Added

  • Locks for thread safety of torch models

Maintenance

  • Established documentation hosting
  • Provided documentation links to error messages
  • Fixed bugs spotted during tests

0.18.5 and earlier versions

Added

  • Initial releases of inference-models library
  • Support for 50+ computer vision models
  • Multi-backend support (ONNX, PyTorch, TensorRT)
  • AutoModel API for automatic model loading
  • AutoModelPipeline for multi-model workflows
  • Comprehensive model package system
  • Automatic backend negotiation
  • Model caching and optimization
  • Support for object detection, instance segmentation, classification, OCR, keypoint detection, and more
  • Vision-language models (Florence-2, PaliGemma, Qwen2.5-VL, etc.)
  • Interactive segmentation (SAM, SAM2)
  • Depth estimation models
  • Gaze detection
  • Open-vocabulary object detection
  • Embeddings models (CLIP, Perception Encoder)

Documentation

  • Complete API reference documentation
  • Getting started guides
  • Model-specific documentation for all supported models
  • How-to guides for common tasks
  • Contributors guide
  • Error reference documentation

Backends

  • ONNX Runtime support (CPU and GPU)
  • PyTorch support (CPU, CUDA, MPS)
  • TensorRT support for NVIDIA GPUs
  • Automatic backend selection based on hardware

Features

  • Automatic model package negotiation
  • Multi-device support (CPU, CUDA, MPS)
  • Batch processing support
  • Quantization support (FP32, FP16, INT8)
  • Model dependency resolution
  • Custom weights provider support
  • Local model loading
  • Docker support with pre-built images