AXERA-TECH/bevformer

By AXERA-TECH · ↓ 7 · ♥ 0 · Updated 2026-01-08

What's in this repo

This page lists the files in AXERA-TECH/bevformer, including bevformer_tiny_fixed.onnx.

1 ONNX models (.onnx files)
664 files, including 567 .png, 84 .json, 4 .gz, 2 .py, 2 .mp4, 1 .md
Last updated 2026-01-08

ONNX files

File	Size	Download
bevformer_tiny_fixed.onnx	—	Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

BEVFormer on Axera NPU

This repository contains the BEVFormer model converted for high-performance inference on the Axera NPU. BEVFormer is a paradigm-shifting transformer-based framework for 3D object detection that learns unified spatio-temporal bird's-eye-view (BEV) representations from multi-camera inputs.

This version is optimized with w8a16 quantization and is compatible with Pulsar2 version 4.2.

Convert Tools Links

For model conversion and deployment guidance:

AXera Platform GitHub Repo: Sample code and optimization guides for Axera NPU.
Pulsar2 Documentation: Guide for converting ONNX models to .axmodel.

Support Platforms

AX650
- M4N-Dock (爱芯派Pro)
- M.2 Accelerator card

Chips	Model Variant	NPU1 Latency (Per Frame)	NPU3 Latency (Per Frame)
AX650	BEVFormer-Tiny	253.966 ms	91.209 ms

How to Use

BEVFormer requires multi-view camera inputs (typically 6 views: front, front-left, front-right, back, back-left, back-right).

Prerequisites

Environment: Ensure you have the required Python environment activated (e.g., using Conda or a virtual environment) with the following core packages installed:
- NPU Runtime: axengine (PyAXEngine)
- Core Libraries: numpy (>= 1.22.0), opencv-python (cv2), tqdm, and cffi.
(Recommended: Use a dedicated Conda environment to manage these dependencies.)
Model/Data: Ensure the compiled .axmodel, inference_config.json, and input data (inference_data/) are available on the host.

Inference Command

Run the inference script by providing the compiled model, configuration, and data directory.

python inference_axmodel.py compiled.axmodel inference_config.json inference_data/ --output-dir inference_results

Inference with AX650 Host

(base) root@ax650:~/data# python inference_axmodel.py compiled.axmodel inference_config.json inference_data/ --output-dir ./inference_results
[INFO] Available providers:  ['AXCLRTExecutionProvider', 'AxEngineExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.12.0s
[INFO] Model type: 0 (single core)
[INFO] Compiler version: 5.1-patch1 82190926
Processing scene 1/2: fcbccedd61424f1b85dcbf8f897f9754 (40 frames)
Scene fcbccedd61424f1b85dcbf8f897f9754:  28%|██████████████████████                                                          | 11/40 [00:12<00:33,  1.15s/it]/root/guofangming/inference_axmodel.py:389: RuntimeWarning: invalid value encountered in cast
  corners = imgfov_pts_2d[i].astype(np.int32)
Scene fcbccedd61424f1b85dcbf8f897f9754: 100%|████████████████████████████████████████████████████████████████████████████████| 40/40 [00:47<00:00,  1.18s/it]Processing scene 2/2: 325cef682f064c55a255f2625c533b75 (41 frames)
Scene 325cef682f064c55a255f2625c533b75: 100%|████████████████████████████████████████████████████████████████████████████████| 41/41 [00:48<00:00,  1.18s/it]Creating video: fcbccedd61424f1b85dcbf8f897f9754_result.mp4: 100%|███████████████████████████████████████████████████████████| 40/40 [00:08<00:00,  4.83it/s]✓ Scene fcbccedd61424f1b85dcbf8f897f9754: 40 frames, video: ./inference_results/fcbccedd61424f1b85dcbf8f897f9754/fcbccedd61424f1b85dcbf8f897f9754_result.mp4]Save scene results:  50%|███████████████████████████████████████████████████                                                   | 1/2 [00:23<00:23, 23.05s/it]
Creating video: 325cef682f064c55a255f2625c533b75_result.mp4:   7%|█▋                     | 3/41 [00:00<00:07,  4.92it/s]
Creating video: 325cef682f064c55a255f2625c533b75_result.mp4: 100%|██████████████████████| 41/41 [00:08<00:00,  4.78it/s]
✓ Scene 325cef682f064c55a255f2625c533b75: 41 frames, video: ./inference_results/325cef682f064c55a255f2625c533b75/325cef682f064c55a255f2625c533b75_result.mp4
Save scene results: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:47<00:00, 23.79s/it]

Results

The model generates a 3D detection map projected onto the Bird's-Eye-View plane. Results are saved as images and videos which visualize the ego-vehicle and surrounding detected objects.

Example Visualization: BEVFormer Detection Result GIF

Model info

object-detection

onnx ONNX Axera NPU Pulsar2 BEVFormer Autonomous-Driving Bird-Eye-View object-detection en arxiv:2203.17270 license:mit

↓ 7 ♥ 0

Created: 2026-01-05

Updated: 2026-01-08

View on Hugging Face

Files (664)

.gitattributes

README.md

ax650/compiled.axmodel

bevformer_tiny_fixed.onnx ONNX

calibration/calibration_can_bus.tar.gz

calibration/calibration_img.tar.gz

calibration/calibration_lidar2img.tar.gz

calibration/calibration_prev_bev.tar.gz

config.json

inference_axmodel.py

inference_config.json

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inference_data/fcbccedd61424f1b85dcbf8f897f9754/meta_000000.json

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inference_data/fcbccedd61424f1b85dcbf8f897f9754/meta_000035.json

inference_data/fcbccedd61424f1b85dcbf8f897f9754/meta_000036.json

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inference_data/fcbccedd61424f1b85dcbf8f897f9754/meta_000039.json

inference_data/scene_index.json

inference_onnx.py

inference_results/325cef682f064c55a255f2625c533b75/325cef682f064c55a255f2625c533b75_result.mp4

inference_results/325cef682f064c55a255f2625c533b75/images/frame_000000.png

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inference_results/325cef682f064c55a255f2625c533b75/images/frame_000040.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/fcbccedd61424f1b85dcbf8f897f9754_result.mp4

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000000.png

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inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000017.png

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inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000020.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000021.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000022.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000023.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000024.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000025.png

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inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000027.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000028.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000029.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000030.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000031.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000032.png

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inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000034.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000035.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000036.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000037.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000038.png

inference_results/fcbccedd61424f1b85dcbf8f897f9754/images/frame_000039.png

output.gif

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