Add TensorRT export support and inference-models inference integration

[Copilot]

RF-DETR had no built-in path to TensorRT export or multi-backend inference. This PR adds a one-step TensorRT export flag and documents inference-models as the recommended inference library.

Changes

• RFDETR.export(tensorrt=True) — chains ONNX → trtexec conversion in a single call, producing both output/inference_model.onnx and output/inference_model.engine
• trtexec() return value fix — was returning None; now returns the engine path (required for the export chaining above)
• inference-models integration — docs/learn/export.md now directs users to inference-models for multi-backend inference (PyTorch, ONNX, TensorRT) with automatic backend selection

Usage

# One-step TensorRT export (requires trtexec in PATH)
model = RFDETRBase(pretrain_weights="checkpoint.pth")
model.export(tensorrt=True)  # → output/inference_model.onnx + .engine

# Inference via inference-models (auto-selects best backend)
from inference_models import AutoModel
model = AutoModel.from_pretrained("rfdetr-base")
predictions = model(image)

# Load a local checkpoint
model = AutoModel.from_pretrained("/path/to/checkpoint.pth", model_type="rfdetr-base")

# Force TensorRT backend
from inference_models import AutoModel, BackendType
model = AutoModel.from_pretrained("rfdetr-base", backend=BackendType.TRT)

Original prompt

---

This section details on the original issue you should resolve

<issue_title>Support for loading TensorRT .engine weights in RF-DETR inference</issue_title>
<issue_description>Hi,

I would like to ask whether RF-DETR supports directly loading TensorRT .engine weights for inference.

Currently, the official sample code only supports loading .pt weights for inference. However, I plan to deploy the model using TensorRT (and later integrate an ID tracking pipeline), so I would like to run inference using the converted .engine weights instead.

The documentation and examples do not seem to mention how to load .engine files. May I ask whether direct TensorRT engine inference is currently supported, or if there is a recommended way to achieve this?

Thanks!</issue_description>

<agent_instructions>explore adding another export format and also loading for inference
keep it simple and focused</agent_instructions>

Comments on the Issue (you are @copilot in this section)

• Fixes Support for loading TensorRT .engine weights in RF-DETR inference #819

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Codecov Report

❌ Patch coverage is 65.00000% with 28 lines in your changes missing coverage. Please review.
✅ Project coverage is 71%. Comparing base (faf8a22) to head (5476071).
⚠️ Report is 24 commits behind head on develop.

❌ Your patch check has failed because the patch coverage (65%) is below the target coverage (95%). You can increase the patch coverage or adjust the target coverage.
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Additional details and impacted files

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- Coverage       76%    71%    -4%     
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  Files           94     94            
  Lines         7269   7348    +79     
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- Hits          5502   5235   -267     
- Misses        1767   2113   +346     

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[Borda]

@copilot pivot it to set up integration with inference-models, see https://github.com/roboflow/inference/tree/main/inference_models

[Copilot]

@copilot pivot it to set up integration with inference-models, see https://github.com/roboflow/inference/tree/main/inference_models

Done in 696747f. Removed the custom RFDETRTensorRT class and replaced it with documentation pointing to inference-models, which already has production-quality RF-DETR support for PyTorch, ONNX, and TensorRT backends.

The docs now show:

• AutoModel.from_pretrained("rfdetr-base") for pretrained models with automatic backend selection
• Loading local .pth checkpoints via model_type="rfdetr-base"
• Forcing TRT backend with backend=BackendType.TRT

The RFDETR.export(tensorrt=True) convenience flag and trtexec() return value fix are retained since they're still useful for producing engine files.

Warning

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• inference-models.roboflow.com
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If you need me to access, download, or install something from one of these locations, you can either:

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[Borda]

@copilot resolve merge conflict or rebase on the develop branch with just relevant chnages

[Copilot]

@copilot resolve merge conflict or rebase on the develop branch with just relevant chnages

Done in b3099a7 — rebased onto develop with only the relevant changes: trtexec() return value fix, tensorrt=True export param, inference-models docs, and tests.