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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 (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)
+