OpenVINO IR model export

docs/learn/export.md

@@ -1,20 +1,21 @@
 ---
-description: Export RF-DETR models to ONNX, TensorRT, TFLite, ExecuTorch, and native CoreML (FP32/FP16/INT8) for high-performance inference on GPUs, mobile, and edge devices.
+description: Export RF-DETR models to ONNX, TensorRT, TFLite, ExecuTorch, native CoreML and OpenVINO IR (FP32/FP16/INT8) for high-performance inference on GPUs, mobile, and edge devices.
 ---
 
 # Export RF-DETR Model
 
 !!! tip "Key Takeaways"
 
     - Export to ONNX for cross-platform inference with ONNX Runtime, OpenVINO, or TensorRT
+    - Export to OpenVINO IR for optimized inference on CPU (x86, ARM), GPU (Intel integrated & discrete GPU) and AI accelerators (Intel NPU)
     - Export to TFLite (FP32, FP16, INT8) for mobile and edge deployment
     - TensorRT conversion delivers lowest latency on NVIDIA GPUs (2.3 ms for Nano)
     - INT8 quantization requires calibration data from your dataset for accurate results
     - Custom input resolutions supported (must be divisible by `patch_size × num_windows`, which varies by model variant)
     - Export to ExecuTorch for on-device PyTorch inference (XNNPACK, CoreML, QNN)
     - Export directly to native CoreML (`.mlpackage`) for Xcode / Apple-platform deployment — see [Native CoreML Export](#native-coreml-export-mlpackage)
 
-RF-DETR supports exporting models to ONNX, TFLite, ExecuTorch, and native CoreML formats, enabling deployment across a wide range of inference frameworks, edge devices, and hardware accelerators.
+RF-DETR supports exporting models to ONNX, TFLite, ExecuTorch, native CoreML and OpenVINO IR formats, enabling deployment across a wide range of inference frameworks, edge devices, and hardware accelerators.
 
 ## Installation
 
@@ -24,6 +25,9 @@ Install the export dependencies you need:
 # ONNX export only
 pip install "rfdetr[onnx]"
 
+# OpenVINO IR export
+pip install "rfdetr[openvino]"
+
 # TFLite export
 pip install "rfdetr[tflite]"
 
@@ -67,7 +71,7 @@ The `export()` method accepts several parameters to customize the export process
 | Parameter          | Default    | Description                                                                                                                                                                                      |
 | ------------------ | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `output_dir`       | `"output"` | Directory where the exported model will be saved.                                                                                                                                                |
-| `format`           | `"onnx"`   | Export format: `"onnx"`, `"tflite"`, `"tensorrt"` (alias: `"trt"`), `"executorch"`, or `"coreml"`.                                                                                               |
+| `format`           | `"onnx"`   | Export format: `"onnx"`, `"tflite"`, `"tensorrt"` (alias: `"trt"`), `"executorch"`, `"openvino"` or `"coreml"`.                                                                                  |
 | `quantization`     | `None`     | TFLite quantization mode: `None`/`"fp32"`, `"fp16"`, or `"int8"`. Only used when `format="tflite"`.                                                                                              |
 | `calibration_data` | `None`     | Calibration data for TFLite export. Image directory, `.npy` file path, NumPy array, or `None`. See [TFLite Export](#tflite-export).                                                              |
 | `max_images`       | `100`      | Maximum number of images to load from a calibration directory for TFLite INT8 quantization. Ignored for other calibration data formats.                                                          |
@@ -464,6 +468,104 @@ boxes = interpreter.get_tensor(boxes_detail["index"])
 labels = interpreter.get_tensor(labels_detail["index"])
 ```
 
+## OpenVINO IR Export
+
+OpenVINO IR (Intermediate Representation) is a proprietary model format used by the OpenVINO Toolkit to optimize and deploy deep learning models.
+
+### Prerequisites
+
+```bash
+pip install "rfdetr[openvino]"
+```
+
+### Basic OpenVINO Export
+
+=== "Object Detection"
+
+    ```python
+    from rfdetr import RFDETRMedium
+
+    model = RFDETRMedium(pretrain_weights="<path/to/checkpoint.pth>")
+
+    model.export(format="openvino", output_dir="output")
+    ```
+
+=== "Image Segmentation"
+
+    ```python
+    from rfdetr import RFDETRSegMedium
+
+    model = RFDETRSegMedium(pretrain_weights="<path/to/checkpoint.pth>")
+
+    model.export(format="openvino", output_dir="output")
+    ```
+
+This produces two files (named after the model's variant):
+
+- `output/<model-variant>.xml` - The model structure (Intermediate Representation)
+- `output/<model-variant>.bin` - The model weights
+
+### OpenVINO Export with Custom Resolution
+
+```python
+from rfdetr import RFDETRMedium
+
+model = RFDETRMedium(pretrain_weights="<path/to/checkpoint.pth>")
+
+model.export(format="openvino", shape=(608, 608))
+```
+
+### OpenVINO Inference Example
+
+```python
+import numpy as np
+from PIL import Image
+from rfdetr.export._openvino.inference import OpenVINOInference
+
+# Load the exported model
+model = OpenVINOInference("output/rfdetr-medium.xml")
+
+# Prepare input image (NCHW format, ImageNet normalized)
+image = Image.open("image.jpg").convert("RGB").resize((576, 576))
+image_array = np.array(image).astype(np.float32) / 255.0
+
+# Apply ImageNet normalization
+mean = np.array([0.485, 0.456, 0.406])
+std = np.array([0.229, 0.224, 0.225])
+image_array = (image_array - mean) / std
+
+# Convert to NCHW format
+image_array = np.transpose(image_array, (2, 0, 1))
+image_array = np.expand_dims(image_array, axis=0)
+
+# Run inference
+outputs = model(image_array)
+boxes, labels = outputs
+```
+
+### Benchmark OpenVINO Model
+
+Use OpenVINO's `benchmark_app` tool to measure performance:
+
+```bash
+benchmark_app -m output/rfdetr-medium.xml -data_shape [1,3,576,576]
+```
+
+### OpenVINO Model Outputs
+
+The exported OpenVINO IR model produces the following outputs:
+
+- **Object Detection Models**:
+
+    - Output 0: Bounding boxes `[batch, 300, 4]` (x, y, w, h in normalized coordinates)
+    - Output 1: Class logits `[batch, 300, num_classes]`
+
+- **Segmentation Models**:
+
+    - Output 0: Bounding boxes `[batch, 300, 4]`
+    - Output 1: Class logits `[batch, 300, num_classes]`
+    - Output 2: Instance masks (if segmentation head is present)
+
 ## ExecuTorch Export
 
 !!! warning "Experimental — Use with Caution"

pyproject.toml

@@ -138,6 +138,9 @@ coreml = [
     # cause is understood/fixed upstream; don't treat it as a guaranteed-deterministic fix.
     "torch<2.12",
 ]
+openvino = [
+    "openvino>=2024.0",  # Direct PyTorch -> OpenVINO IR conversion
+]
 loggers = [
     "tensorboard>=2.13.0",
     "protobuf>=3.20.0",  # Cap <4.0.0 removed — tensorflow>=2.16.0 (required by [tflite]) needs protobuf>=3.20.3 and is incompatible with <4.0.0 (see #1041)