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README.md

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+---
+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
+
+Re-exported InsightFace models with **proper dynamic batch support** and **no cross-frame contamination**.
+
+## 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_batch.onnx` | Face Detection | `[N, 3, 320, 320]` | boxes, landmarks | 1-32 | **6×** |
+| `arcface_w600k_r50_batch.onnx` | Face Embedding | `[N, 3, 112, 112]` | 512-dim vectors | 1-32 | **10×** |
+
+## Performance (TensorRT FP16, RTX 5090)
+
+### SCRFD Face Detection
+| Batch Size | FPS | ms/frame |
+|------------|-----|----------|
+| 1 | 867 | 1.15 |
+| 16 | **5,498** | 0.18 |
+
+### ArcFace Embeddings
+| Batch Size | FPS | ms/embedding |
+|------------|-----|--------------|
+| 1 | 292 | 3.4 |
+| 16 | **3,029** | 0.33 |
+
+## Usage
+
+```python
+import numpy as np
+import onnxruntime as ort
+
+# Load model
+sess = ort.InferenceSession("scrfd_10g_320_batch.onnx", 
+                            providers=["TensorrtExecutionProvider", "CUDAExecutionProvider"])
+
+# Batch inference
+batch = np.random.randn(16, 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
+```
+
+## 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
+}
+```
+
+See [SCRFD_320_EXPORT_INSTRUCTIONS.md](https://github.com/deepinsight/insightface/issues/1573) for details.
+
+## 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)
+