Smart-City-Sample
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How to run segmentation models on platform?
Hi @nnshah1 , @xwu2git,
I am stuck with implementing "mask_rcnn_inception_v2_coco" on Smart-City-Sample platform. I am sharing my current pipeline.json and model-proc for the model that I have created as per my understanding. I am also attaching error I am receiving on the analytics pod with current Implementation, My interest is merely to check the compute and results on the base model.
PIPELINE.JSON
{ "name": "object_detection", "version": 2, "type": "GStreamer", "template":"rtspsrc udp-buffer-size=212992 name=source ! queue ! rtph264depay ! h264parse ! video/x-h264 ! tee name=t ! queue ! decodebin ! videoconvert name=\"videoconvert\" ! video/x-raw,format=BGRx ! queue leaky=upstream ! gvadetect ie-config=CPU_BIND_THREAD=NO model=\"{models[mask_rcnn_inception_v2_coco][1][network]}\" model-proc=\"{models[mask_rcnn_inception_v2_coco][1][proc]}\" name=\"detection\" ! gvametaconvert name=\"metaconvert\" ! queue ! gvametapublish name=\"destination\" ! appsink name=appsink t. ! queue ! splitmuxsink max-size-time=60500000000 name=\"splitmuxsink\"", "description": "Object Detection Pipeline", "parameters": { "type" : "object", "properties" : { "inference-interval": { "element":"detection", "type": "integer", "minimum": 0, "maximum": 4294967295 }, "cpu-throughput-streams": { "element":"detection", "type": "string" }, "n-threads": { "element":"videoconvert", "type": "integer" }, "nireq": { "element":"detection", "type": "integer", "minimum": 1, "maximum": 64 }, "recording_prefix": { "type":"string", "default":"recording" } } } }
MODEL-PROC `{ "json_schema_version": "1.0.0", "input_preproc":[ { "color_format":"BGR", "layer_name": "image_tensor" }, { "format": "image_info", "layer_name": "image_info" } ], "output_postproc":[ { "converter":"DetectionOutput"
},
{
"converter": "tensor_to_bbox_ssd",
"labels":["","person"]
},
{
"layer_name":"detection_output"
}
]
}`
Error:
Please guide me in this. Thanks.
Review https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Model-preparation and update your model-proc accordingly.
There has been no update on this issue since the request to update the model-proc, recommend closing if no additional information is given.