BBIT-Kai
50 lines
1.8 KiB
YAML
50 lines
1.8 KiB
YAML
property:
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# 官方文档中的配置=============================
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gpu-id: 0
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infer-dims: 3;544;960
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net-scale-factor: 0.00392156862745098
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tlt-model-key: tlt_encode
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# 模型网络类型 0: Detector 1: Classifier 2: Segmentation 3: Instance Segmentation
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network-type: 0
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num-detected-classes: 4
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# 模型颜色格式 0：RGB 1：BGR 2：灰度
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model-color-format: 0
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# 保持宽高比 0：不保持 1：保持
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maintain-aspect-ratio: 0
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# 是否输出张量元数据 0：否 1：是
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output-tensor-meta: 0
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# 文件路径配置=============================
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onnx-file: ../models/traffic_cam_net/resnet18_trafficcamnet_pruned.onnx
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labelfile-path: ../models/traffic_cam_net/labels.txt
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model-engine-file: ../models/traffic_cam_net/resnet18_trafficcamnet_pruned.onnx_b4_gpu0_fp16.engine
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gie-unique-id: 1
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batch-size: 4
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# 推理模式配置 1：整帧 2：上游ROI （1：主推理 2：次推理（基于主推理））
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process-mode: 1
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## 0=FP32, 1=INT8, 2=FP16 mode
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network-mode: 2
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# 推理间隔 0：每帧 1：每1帧 2：每2帧
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interval: 0
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output-blob-names: output_cov/Sigmoid:0;output_bbox/BiasAdd:0
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# 集群模式 0: OpenCV groupRectangles() 1: DBSCAN 2: Non Maximum Suppression 3: DBSCAN + NMS Hybrid 4: No clustering
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cluster-mode: 2
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# 计算硬件 0: Platform default – GPU (dGPU), VIC (Jetson) 1: GPU 2: VIC (Jetson only)
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scaling-compute-hw: 0
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# 设置具体类别NMS等参数
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class-attrs-all:
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# 保留的最多对象数
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topk: 20
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# 两个方案之间的最大 IOU 分数，超过该分数后，置信度较低的方案将被拒绝。
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nms-iou-threshold: 0.5
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# 预聚类阈值，范围为0到1。较高的值会导致更多的边界框被聚类在一起。
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pre-cluster-threshold: 0.2
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## Per class configurations
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class-attrs-0:
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topk: 20
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nms-iou-threshold: 0.6
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pre-cluster-threshold: 0.4
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