# ------------------------------------------------------------------------ # RF-DETR # Copyright (c) 2025 Roboflow. All Rights Reserved. # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ import glob import json import os import warnings from collections import defaultdict from copy import deepcopy from typing import List, Union import numpy as np import requests import supervision as sv import torch import torchvision.transforms.functional as F import yaml from PIL import Image from rfdetr.datasets.coco import is_valid_coco_dataset from rfdetr.datasets.yolo import is_valid_yolo_dataset from rfdetr.util.logger import get_logger try: torch.set_float32_matmul_precision('high') except: pass from rfdetr.config import ( ModelConfig, RFDETRBaseConfig, RFDETRLargeConfig, RFDETRLargeDeprecatedConfig, RFDETRMediumConfig, RFDETRNanoConfig, RFDETRSeg2XLargeConfig, RFDETRSegLargeConfig, RFDETRSegMediumConfig, RFDETRSegNanoConfig, RFDETRSegPreviewConfig, RFDETRSegSmallConfig, RFDETRSegXLargeConfig, RFDETRSmallConfig, SegmentationTrainConfig, TrainConfig, ) from rfdetr.main import Model, download_pretrain_weights from rfdetr.util.coco_classes import COCO_CLASSES from rfdetr.util.metrics import MetricsPlotSink, MetricsTensorBoardSink, MetricsWandBSink logger = get_logger() class RFDETR: """ The base RF-DETR class implements the core methods for training RF-DETR models, running inference on the models, optimising models, and uploading trained models for deployment. """ means = [0.485, 0.456, 0.406] stds = [0.229, 0.224, 0.225] size = None def __init__(self, **kwargs): self.model_config = self.get_model_config(**kwargs) self.maybe_download_pretrain_weights() self.model = self.get_model(self.model_config) self.callbacks = defaultdict(list) self.model.inference_model = None self._is_optimized_for_inference = False self._has_warned_about_not_being_optimized_for_inference = False self._optimized_has_been_compiled = False self._optimized_batch_size = None self._optimized_resolution = None self._optimized_dtype = None def maybe_download_pretrain_weights(self): """ Download pre-trained weights if they are not already downloaded. """ download_pretrain_weights(self.model_config.pretrain_weights) def get_model_config(self, **kwargs): """ Retrieve the configuration parameters used by the model. """ return ModelConfig(**kwargs) def train(self, **kwargs): """ Train an RF-DETR model. """ config = self.get_train_config(**kwargs) self.train_from_config(config, **kwargs) def optimize_for_inference(self, compile=True, batch_size=1, dtype=torch.float32): self.remove_optimized_model() self.model.inference_model = deepcopy(self.model.model) self.model.inference_model.eval() self.model.inference_model.export() self._optimized_resolution = self.model.resolution self._is_optimized_for_inference = True self.model.inference_model = self.model.inference_model.to(dtype=dtype) self._optimized_dtype = dtype if compile: self.model.inference_model = torch.jit.trace( self.model.inference_model, torch.randn( batch_size, 3, self.model.resolution, self.model.resolution, device=self.model.device, dtype=dtype ) ) self._optimized_has_been_compiled = True self._optimized_batch_size = batch_size def remove_optimized_model(self): self.model.inference_model = None self._is_optimized_for_inference = False self._optimized_has_been_compiled = False self._optimized_batch_size = None self._optimized_resolution = None self._optimized_half = False def export(self, **kwargs): """ Export your model to an ONNX file. See [the ONNX export documentation](https://rfdetr.roboflow.com/learn/export/) for more information. """ self.model.export(**kwargs) @staticmethod def _load_classes(dataset_dir) -> List[str]: """Load class names from a COCO or YOLO dataset directory.""" if is_valid_coco_dataset(dataset_dir): coco_path = os.path.join(dataset_dir, "train", "_annotations.coco.json") with open(coco_path, "r") as f: anns = json.load(f) class_names = [c["name"] for c in anns["categories"] if c["supercategory"] != "none"] return class_names # list all YAML files in the folder if is_valid_yolo_dataset(dataset_dir): yaml_paths = glob.glob(os.path.join(dataset_dir, "*.yaml")) + glob.glob(os.path.join(dataset_dir, "*.yml")) # any YAML file starting with data e.g. data.yaml, dataset.yaml yaml_data_files = [yp for yp in yaml_paths if os.path.basename(yp).startswith("data")] yaml_path = yaml_data_files[0] with open(yaml_path, "r") as f: data = yaml.safe_load(f) if "names" in data: if isinstance(data["names"], dict): return [data["names"][i] for i in sorted(data["names"].keys())] return data["names"] else: raise ValueError(f"Found {yaml_path} but it does not contain 'names' field.") raise FileNotFoundError( f"Could not find class names in {dataset_dir}. " "Checked for COCO (train/_annotations.coco.json) and YOLO (data.yaml, data.yml) styles." ) def train_from_config(self, config: TrainConfig, **kwargs): if config.dataset_file == "roboflow": class_names = self._load_classes(config.dataset_dir) num_classes = len(class_names) + 1 self.model.class_names = class_names elif config.dataset_file == "yolo": class_names = self._load_classes(config.dataset_dir) num_classes = len(class_names) self.model.class_names = class_names elif config.dataset_file == "coco": class_names = COCO_CLASSES num_classes = 90 else: raise ValueError(f"Invalid dataset file: {config.dataset_file}") if self.model_config.num_classes != num_classes: logger.warning( f"Reinitializing your detection head with {num_classes} classes." ) self.model.reinitialize_detection_head(num_classes) train_config = config.dict() model_config = self.model_config.dict() model_config.pop("num_classes") if "class_names" in model_config: model_config.pop("class_names") if "class_names" in train_config and train_config["class_names"] is None: train_config["class_names"] = class_names for k, v in train_config.items(): if k in model_config: model_config.pop(k) if k in kwargs: kwargs.pop(k) all_kwargs = {**model_config, **train_config, **kwargs, "num_classes": num_classes} metrics_plot_sink = MetricsPlotSink(output_dir=config.output_dir) self.callbacks["on_fit_epoch_end"].append(metrics_plot_sink.update) self.callbacks["on_train_end"].append(metrics_plot_sink.save) if config.tensorboard: metrics_tensor_board_sink = MetricsTensorBoardSink(output_dir=config.output_dir) self.callbacks["on_fit_epoch_end"].append(metrics_tensor_board_sink.update) self.callbacks["on_train_end"].append(metrics_tensor_board_sink.close) if config.wandb: metrics_wandb_sink = MetricsWandBSink( output_dir=config.output_dir, project=config.project, run=config.run, config=config.model_dump() ) self.callbacks["on_fit_epoch_end"].append(metrics_wandb_sink.update) self.callbacks["on_train_end"].append(metrics_wandb_sink.close) if config.early_stopping: from rfdetr.util.early_stopping import EarlyStoppingCallback early_stopping_callback = EarlyStoppingCallback( model=self.model, patience=config.early_stopping_patience, min_delta=config.early_stopping_min_delta, use_ema=config.early_stopping_use_ema, segmentation_head=config.segmentation_head ) self.callbacks["on_fit_epoch_end"].append(early_stopping_callback.update) self.model.train( **all_kwargs, callbacks=self.callbacks, ) def get_train_config(self, **kwargs): """ Retrieve the configuration parameters that will be used for training. """ return TrainConfig(**kwargs) def get_model(self, config: ModelConfig): """ Retrieve a model instance based on the provided configuration. """ return Model(**config.dict()) # Get class_names from the model @property def class_names(self): """ Retrieve the class names supported by the loaded model. Returns: dict: A dictionary mapping class IDs to class names. The keys are integers starting from """ if hasattr(self.model, 'class_names') and self.model.class_names: return {i+1: name for i, name in enumerate(self.model.class_names)} return COCO_CLASSES def predict( self, images: Union[str, Image.Image, np.ndarray, torch.Tensor, List[Union[str, np.ndarray, Image.Image, torch.Tensor]]], threshold: float = 0.5, **kwargs, ) -> Union[sv.Detections, List[sv.Detections]]: """Performs object detection on the input images and returns bounding box predictions. This method accepts a single image or a list of images in various formats (file path, image url, PIL Image, NumPy array, or torch.Tensor). The images should be in RGB channel order. If a torch.Tensor is provided, it must already be normalized to values in the [0, 1] range and have the shape (C, H, W). Args: images (Union[str, Image.Image, np.ndarray, torch.Tensor, List[Union[str, np.ndarray, Image.Image, torch.Tensor]]]): A single image or a list of images to process. Images can be provided as file paths, PIL Images, NumPy arrays, or torch.Tensors. threshold (float, optional): The minimum confidence score needed to consider a detected bounding box valid. **kwargs: Additional keyword arguments. Returns: Union[sv.Detections, List[sv.Detections]]: A single or multiple Detections objects, each containing bounding box coordinates, confidence scores, and class IDs. """ if not self._is_optimized_for_inference and not self._has_warned_about_not_being_optimized_for_inference: logger.warning( "Model is not optimized for inference. " "Latency may be higher than expected. " "You can optimize the model for inference by calling model.optimize_for_inference()." ) self._has_warned_about_not_being_optimized_for_inference = True self.model.model.eval() if not isinstance(images, list): images = [images] orig_sizes = [] processed_images = [] for img in images: if isinstance(img, str): if img.startswith("http"): img = requests.get(img, stream=True).raw img = Image.open(img) if not isinstance(img, torch.Tensor): img = F.to_tensor(img) if (img > 1).any(): raise ValueError( "Image has pixel values above 1. Please ensure the image is " "normalized (scaled to [0, 1])." ) if img.shape[0] != 3: raise ValueError( f"Invalid image shape. Expected 3 channels (RGB), but got " f"{img.shape[0]} channels." ) img_tensor = img h, w = img_tensor.shape[1:] orig_sizes.append((h, w)) img_tensor = img_tensor.to(self.model.device) img_tensor = F.normalize(img_tensor, self.means, self.stds) img_tensor = F.resize(img_tensor, (self.model.resolution, self.model.resolution)) processed_images.append(img_tensor) batch_tensor = torch.stack(processed_images) if self._is_optimized_for_inference: if self._optimized_resolution != batch_tensor.shape[2]: # this could happen if someone manually changes self.model.resolution after optimizing the model raise ValueError(f"Resolution mismatch. " f"Model was optimized for resolution {self._optimized_resolution}, " f"but got {batch_tensor.shape[2]}. " "You can explicitly remove the optimized model by calling model.remove_optimized_model().") if self._optimized_has_been_compiled: if self._optimized_batch_size != batch_tensor.shape[0]: raise ValueError(f"Batch size mismatch. " f"Optimized model was compiled for batch size {self._optimized_batch_size}, " f"but got {batch_tensor.shape[0]}. " "You can explicitly remove the optimized model by calling model.remove_optimized_model(). " "Alternatively, you can recompile the optimized model for a different batch size " "by calling model.optimize_for_inference(batch_size=).") with torch.no_grad(): if self._is_optimized_for_inference: predictions = self.model.inference_model(batch_tensor.to(dtype=self._optimized_dtype)) else: predictions = self.model.model(batch_tensor) if isinstance(predictions, tuple): return_predictions = { "pred_logits": predictions[1], "pred_boxes": predictions[0], } if len(predictions) == 3: return_predictions["pred_masks"] = predictions[2] predictions = return_predictions target_sizes = torch.tensor(orig_sizes, device=self.model.device) results = self.model.postprocess(predictions, target_sizes=target_sizes) detections_list = [] for result in results: scores = result["scores"] labels = result["labels"] boxes = result["boxes"] keep = scores > threshold scores = scores[keep] labels = labels[keep] boxes = boxes[keep] if "masks" in result: masks = result["masks"] masks = masks[keep] detections = sv.Detections( xyxy=boxes.float().cpu().numpy(), confidence=scores.float().cpu().numpy(), class_id=labels.cpu().numpy(), mask=masks.squeeze(1).cpu().numpy(), ) else: detections = sv.Detections( xyxy=boxes.float().cpu().numpy(), confidence=scores.float().cpu().numpy(), class_id=labels.cpu().numpy(), ) detections_list.append(detections) return detections_list if len(detections_list) > 1 else detections_list[0] def deploy_to_roboflow(self, workspace: str, project_id: str, version: str, api_key: str = None, size: str = None): """ Deploy the trained RF-DETR model to Roboflow. Deploying with Roboflow will create a Serverless API to which you can make requests. You can also download weights into a Roboflow Inference deployment for use in Roboflow Workflows and on-device deployment. Args: workspace (str): The name of the Roboflow workspace to deploy to. project_ids (List[str]): A list of project IDs to which the model will be deployed api_key (str, optional): Your Roboflow API key. If not provided, it will be read from the environment variable `ROBOFLOW_API_KEY`. size (str, optional): The size of the model to deploy. If not provided, it will default to the size of the model being trained (e.g., "rfdetr-base", "rfdetr-large", etc.). model_name (str, optional): The name you want to give the uploaded model. If not provided, it will default to "-uploaded". Raises: ValueError: If the `api_key` is not provided and not found in the environment variable `ROBOFLOW_API_KEY`, or if the `size` is not set for custom architectures. """ import shutil from roboflow import Roboflow if api_key is None: api_key = os.getenv("ROBOFLOW_API_KEY") if api_key is None: raise ValueError("Set api_key= in deploy_to_roboflow or export ROBOFLOW_API_KEY=") rf = Roboflow(api_key=api_key) workspace = rf.workspace(workspace) if self.size is None and size is None: raise ValueError("Must set size for custom architectures") size = self.size or size tmp_out_dir = ".roboflow_temp_upload" os.makedirs(tmp_out_dir, exist_ok=True) outpath = os.path.join(tmp_out_dir, "weights.pt") torch.save( { "model": self.model.model.state_dict(), "args": self.model.args }, outpath ) project = workspace.project(project_id) version = project.version(version) version.deploy( model_type=size, model_path=tmp_out_dir, filename="weights.pt" ) shutil.rmtree(tmp_out_dir) class RFDETRBase(RFDETR): """ Train an RF-DETR Base model (29M parameters). """ size = "rfdetr-base" def get_model_config(self, **kwargs): return RFDETRBaseConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) class RFDETRNano(RFDETR): """ Train an RF-DETR Nano model. """ size = "rfdetr-nano" def get_model_config(self, **kwargs): return RFDETRNanoConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) class RFDETRSmall(RFDETR): """ Train an RF-DETR Small model. """ size = "rfdetr-small" def get_model_config(self, **kwargs): return RFDETRSmallConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) class RFDETRMedium(RFDETR): """ Train an RF-DETR Medium model. """ size = "rfdetr-medium" def get_model_config(self, **kwargs): return RFDETRMediumConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) class RFDETRLargeNew(RFDETR): size = "rfdetr-large" def get_model_config(self, **kwargs): return RFDETRLargeConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) class RFDETRLargeDeprecated(RFDETR): """ Train an RF-DETR Large model. """ size = "rfdetr-large" def __init__(self, **kwargs): warnings.warn( "RFDETRLargeDeprecated is deprecated and will be removed in a future version. " "Please use RFDETRLarge instead.", category=DeprecationWarning, stacklevel=2 ) super().__init__(**kwargs) def get_model_config(self, **kwargs): return RFDETRLargeDeprecatedConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) class RFDETRLarge(RFDETR): size = "rfdetr-large" def __init__(self, **kwargs): self.init_error = None self.is_deprecated = False try: super().__init__(**kwargs) except Exception as e: self.init_error = e self.is_deprecated = True try: super().__init__(**kwargs) logger.warning( "\n" "="*100 + "\n" "WARNING: Automatically switched to deprecated model configuration, due to using deprecated weights. " "This will be removed in a future version.\n" "Please retrain your model with the new weights and configuration.\n" "="*100 + "\n" ) except Exception: raise self.init_error def get_model_config(self, **kwargs): if not self.is_deprecated: return RFDETRLargeConfig(**kwargs) else: return RFDETRLargeDeprecatedConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) class RFDETRSegPreview(RFDETR): size = "rfdetr-seg-preview" def get_model_config(self, **kwargs): return RFDETRSegPreviewConfig(**kwargs) def get_train_config(self, **kwargs): return SegmentationTrainConfig(**kwargs) class RFDETRSegNano(RFDETR): size = "rfdetr-seg-nano" def get_model_config(self, **kwargs): return RFDETRSegNanoConfig(**kwargs) def get_train_config(self, **kwargs): return SegmentationTrainConfig(**kwargs) class RFDETRSegSmall(RFDETR): size = "rfdetr-seg-small" def get_model_config(self, **kwargs): return RFDETRSegSmallConfig(**kwargs) def get_train_config(self, **kwargs): return SegmentationTrainConfig(**kwargs) class RFDETRSegMedium(RFDETR): size = "rfdetr-seg-medium" def get_model_config(self, **kwargs): return RFDETRSegMediumConfig(**kwargs) def get_train_config(self, **kwargs): return SegmentationTrainConfig(**kwargs) class RFDETRSegLarge(RFDETR): size = "rfdetr-seg-large" def get_model_config(self, **kwargs): return RFDETRSegLargeConfig(**kwargs) def get_train_config(self, **kwargs): return SegmentationTrainConfig(**kwargs) class RFDETRSegXLarge(RFDETR): size = "rfdetr-seg-xlarge" def get_model_config(self, **kwargs): return RFDETRSegXLargeConfig(**kwargs) def get_train_config(self, **kwargs): return SegmentationTrainConfig(**kwargs) class RFDETRSeg2XLarge(RFDETR): size = "rfdetr-seg-2xlarge" def get_model_config(self, **kwargs): return RFDETRSeg2XLargeConfig(**kwargs) def get_train_config(self, **kwargs): return SegmentationTrainConfig(**kwargs)