35 lines
1.3 KiB
Markdown
35 lines
1.3 KiB
Markdown
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# People Talking YOLOv8x ONNX v2
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这是 `people-talking-yolov8x@1` 的静态 CPU FP32 ONNX 部署版本。
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| 字段 | 值 |
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|---|---|
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| Model ID | `people-talking-yolov8x@2` |
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| Backend | `onnxruntime-yolov8` |
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| 输入 | `float32[1,3,640,640]` |
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| 输出 | `float32[1,6,8400]`，NMS 外置 |
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| ONNX opset | 17 |
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| 参数量/FLOPs | 68,125,494 / 257.4 GFLOPs |
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| 文件大小 | 272,787,930 bytes |
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| SHA256 | `b4387307dbdf1083c0229a12e833ccdc4a3ad0063d5bdb7908765072470e840b` |
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| 来源 | [`v1/best.pt`](../v1/README.md) |
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部署只选择 `talking on phone`；含义不清的 class 0 `label` 仅为保持类别编号一致而保留。
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`manifest.json` 由运行时在 session 创建前校验。
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```yaml
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model: people-talking-yolov8x@2
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detect_labels: [talking on phone]
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model_options:
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weights: models/detection/people-talking-yolov8x/v2/model.onnx
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providers: [CPUExecutionProvider]
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intra_op_threads: 1
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inter_op_threads: 1
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imgsz: 640
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```
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ONNX 化移除了边缘运行时的 Torch/Ultralytics 依赖，但没有降低网络参数量或 FLOPs，
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FP32 文件也约为 v1 PT 的两倍。合成图 CPU smoke 可运行，但它不是现场精度或实时性
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验收；资源不足时应训练 n/s 级模型或使用现场校准集开展 INT8，而不是宣称格式转换已
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让 YOLOv8x 轻量化。许可与数据限制继承 v1 模型卡。
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