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---
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license: other
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license_name: insightface-non-commercial
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license_link: https://github.com/deepinsight/insightface#license
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tags:
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- face-detection
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- face-recognition
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- scrfd
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- arcface
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- onnx
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- batch-inference
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- tensorrt
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library_name: onnx
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pipeline_tag: image-classification
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---
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# InsightFace Batch-Optimized Models (Max Batch 32)
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Re-exported InsightFace models with **proper dynamic batch support** and **no cross-frame contamination**.
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## Version Comparison
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| Repository | Max Batch | Recommendation |
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|------------|-----------|----------------|
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| **This repo** | **1-32** | ✅ **Recommended** - Optimal performance |
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| [alonsorobots/scrfd_320_batched_64](https://huggingface.co/alonsorobots/scrfd_320_batched_64) | 1-64 | For experimentation |
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**Batch=32 is optimal.** Testing on RTX 5090 shows batch=64 provides no additional throughput benefit.
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## Why These Models?
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The original InsightFace ONNX models have issues with batch inference:
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- `buffalo_l` detection model: hardcoded batch=1
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- `buffalo_l_batch` detection model: **broken** - has cross-frame contamination due to reshape operations that flatten the batch dimension
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These re-exports fix the `dynamic_axes` in the ONNX graph for **true batch inference**.
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## Models
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| Model | Task | Input Shape | Output | Batch | Speedup |
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|-------|------|-------------|--------|-------|---------|
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| `scrfd_10g_320_batch.onnx` | Face Detection | `[N, 3, 320, 320]` | boxes, landmarks | 1-32 | **6×** |
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| `arcface_w600k_r50_batch.onnx` | Face Embedding | `[N, 3, 112, 112]` | 512-dim vectors | 1-32 | **10×** |
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## Performance (TensorRT FP16, RTX 5090)
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### SCRFD Face Detection
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| Batch Size | FPS | ms/frame |
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|------------|-----|----------|
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| 1 | 867 | 1.15 |
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| 16 | **5,498** | 0.18 |
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### ArcFace Embeddings
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| Batch Size | FPS | ms/embedding |
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|------------|-----|--------------|
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| 1 | 292 | 3.4 |
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| 16 | **3,029** | 0.33 |
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## Usage
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```python
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import numpy as np
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import onnxruntime as ort
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# Load model
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sess = ort.InferenceSession("scrfd_10g_320_batch.onnx",
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providers=["TensorrtExecutionProvider", "CUDAExecutionProvider"])
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# Batch inference
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batch = np.random.randn(16, 3, 320, 320).astype(np.float32)
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outputs = sess.run(None, {"input.1": batch})
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# outputs[0-2]: scores per FPN level (stride 8, 16, 32)
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# outputs[3-5]: bboxes per FPN level
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# outputs[6-8]: keypoints per FPN level
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```
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## Verified: No Batch Contamination
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```python
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# Same frame processed alone vs in batch = identical results
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single_output = sess.run(None, {"input.1": frame[np.newaxis, ...]})
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batch[7] = frame
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batch_output = sess.run(None, {"input.1": batch})
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max_diff = np.max(np.abs(single_output[0] - batch_output[0][7]))
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# max_diff < 1e-5 ✓
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```
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## Re-export Process
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These models were re-exported from InsightFace's PyTorch source using MMDetection with proper `dynamic_axes`:
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```python
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dynamic_axes = {
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"input.1": {0: "batch"},
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"score_8": {0: "batch"},
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"score_16": {0: "batch"},
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# ... all outputs
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}
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```
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See [SCRFD_320_EXPORT_INSTRUCTIONS.md](https://github.com/deepinsight/insightface/issues/1573) for details.
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## License
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**Non-commercial research purposes only** - per [InsightFace license](https://github.com/deepinsight/insightface#license).
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For commercial licensing, contact: `recognition-oss-pack@insightface.ai`
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## Credits
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- Original models: [InsightFace](https://github.com/deepinsight/insightface) by Jia Guo et al.
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- SCRFD paper: [Sample and Computation Redistribution for Efficient Face Detection](https://arxiv.org/abs/2105.04714)
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- ArcFace paper: [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)
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