---
license: other
license_name: insightface-non-commercial
license_link: https://github.com/deepinsight/insightface#license
tags:
  - face-detection
  - face-recognition
  - scrfd
  - arcface
  - onnx
  - batch-inference
  - tensorrt
library_name: onnx
pipeline_tag: image-classification
---

# InsightFace Batch-Optimized Models (Max Batch 64)

Re-exported InsightFace models with **proper dynamic batch support** and **no cross-frame contamination**.

## ⚠️ Version Difference

| Repository | Max Batch | Best For |
|------------|-----------|----------|
| [alonsorobots/scrfd_320_batched](https://huggingface.co/alonsorobots/scrfd_320_batched) | 1-32 | Standard use, tested extensively |
| **This repo** | **1-64** | Experimentation with larger batches |

**Recommendation:** Use max batch=32 for optimal performance. Batch=64 provides similar throughput but uses more VRAM.

## Why These Models?

The original InsightFace ONNX models have issues with batch inference:

- `buffalo_l` detection model: hardcoded batch=1
- `buffalo_l_batch` detection model: **broken** - has cross-frame contamination due to reshape operations that flatten the batch dimension

These re-exports fix the `dynamic_axes` in the ONNX graph for **true batch inference**.

## Models

| Model | Task | Input Shape | Output | Batch | Speedup |
|-------|------|-------------|--------|-------|---------|
| `scrfd_10g_320_batch64.onnx` | Face Detection | `[N, 3, 320, 320]` | boxes, landmarks | 1-64 | **6×** |
| `arcface_w600k_r50_batch64.onnx` | Face Embedding | `[N, 3, 112, 112]` | 512-dim vectors | 1-64 | **10×** |

## Performance (TensorRT FP16, RTX 5090)

### Batch Size Comparison (Full Video, 12,263 frames)

| Batch Size | FPS | Relative |
|------------|-----|----------|
| 16 | 2,007 | 1.00× |
| **32** | **2,097** | **1.05×** ✅ Optimal |
| 64 | 2,034 | 1.01× |

**Key Finding:** Batch=32 is optimal. Batch=64 provides no additional benefit due to GPU memory bandwidth saturation.

### With Pipelined Preprocessing (4 workers)

| Configuration | FPS | Speedup |
|---------------|-----|---------|
| Sequential batch=16 | 1,211 | baseline |
| **Pipelined batch=32** | **2,097** | **1.73×** |

## Usage

```python
import numpy as np
import onnxruntime as ort

# Load model
sess = ort.InferenceSession("scrfd_10g_320_batch64.onnx", 
                            providers=["TensorrtExecutionProvider", "CUDAExecutionProvider"])

# Batch inference (any size from 1-64)
batch = np.random.randn(32, 3, 320, 320).astype(np.float32)
outputs = sess.run(None, {"input.1": batch})

# outputs[0-2]: scores per FPN level (stride 8, 16, 32)
# outputs[3-5]: bboxes per FPN level
# outputs[6-8]: keypoints per FPN level
```

## TensorRT Configuration

When using TensorRT, set profile shapes to support your desired batch range:

```python
providers = [
    ("TensorrtExecutionProvider", {
        "trt_fp16_enable": True,
        "trt_engine_cache_enable": True,
        "trt_profile_min_shapes": "input.1:1x3x320x320",
        "trt_profile_opt_shapes": "input.1:32x3x320x320",  # Optimize for batch=32
        "trt_profile_max_shapes": "input.1:64x3x320x320",  # Support up to 64
    }),
    "CUDAExecutionProvider",
]
```

## Verified: No Batch Contamination

```python
# Same frame processed alone vs in batch = identical results
single_output = sess.run(None, {"input.1": frame[np.newaxis, ...]})
batch[7] = frame
batch_output = sess.run(None, {"input.1": batch})

max_diff = np.max(np.abs(single_output[0] - batch_output[0][7]))
# max_diff < 1e-5 ✓
```

## Re-export Process

These models were re-exported from InsightFace's PyTorch source using MMDetection with proper `dynamic_axes`:

```python
dynamic_axes = {
    "input.1": {0: "batch"},
    "score_8": {0: "batch"},
    "score_16": {0: "batch"},
    # ... all outputs
}
```

## License

**Non-commercial research purposes only** - per [InsightFace license](https://github.com/deepinsight/insightface#license).

For commercial licensing, contact: `recognition-oss-pack@insightface.ai`

## Credits

- Original models: [InsightFace](https://github.com/deepinsight/insightface) by Jia Guo et al.
- SCRFD paper: [Sample and Computation Redistribution for Efficient Face Detection](https://arxiv.org/abs/2105.04714)
- ArcFace paper: [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)