# 对图像进行分类 在为某个模型运行 pipeline 命令之前，请遵循所需的[先决条件](https://docs.qualcomm.com/doc/80-70020-15BY/topic/ai-hub-qualcomm-im-sdk.html#prerequisites)。 在运行分类命令之前，在 SSH shell 中运行以下命令。 export XDG_RUNTIME_DIR=/dev/socket/weston && export WAYLAND_DISPLAY=wayland-1 Copy to clipboard ## GoogleNet GoogLeNet 是一种机器学习模型，可以对 Imagenet 数据集中的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [GoogLeNet](https://github.com/pytorch/vision/blob/main/torchvision/models/googlenet.py) 的实现。 - 模型：[GoogLeNet](https://aihub.qualcomm.com/iot/models/googlenet) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) Note 当使用此模型进行推理时，分类标签可能不会显示 gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/googlenet_quantized.tflite ! queue ! \ qtimlvclassification threshold=51.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Mobilenet,q-offsets=<53.0>,q-scales=<0.08174873143434525>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## Inception-v3 InceptionNetV3 是一种机器学习模型，可以对 Imagenet 数据集中的图像进行分类。它还可用于为特定用例构建更复杂的模型。 该模型使用来自 Google 开放图像数据集的样本并在训练后量化为 int8。 AI Hub 模型基于 [Inception-v3](https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py) 的实现。 - 型号：[Inception-v3](https://aihub.qualcomm.com/iot/models/inception_v3) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/inception_v3_quantized.tflite ! queue ! \ qtimlvclassification threshold=51.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Inception,q-offsets=<33.0>,q-scales=<0.18740029633045197>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## MobileNet-v2 MobileNetV2 是一种机器学习模型，可以对 Imagenet 数据集中的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [MobileNet-v2](https://github.com/tonylins/pytorch-mobilenet-v2/tree/master) 的实现。 - 型号：[MobileNet-v2](https://aihub.qualcomm.com/iot/models/mobilenet_v2) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/mobilenet_v2_quantized.tflite ! queue ! \ qtimlvclassification threshold=51.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Mobilenet,q-offsets=<69.0>,q-scales=<0.2386164367198944>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## MobileNet-v3-Large MobileNet-v3-Large 是一种机器学习模型，可以对来自 Imagenet 数据集的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [MobileNet-v3-Large](https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv3.py) 的实现。 - 型号：[MobileNet-v3-Large](https://aihub.qualcomm.com/iot/models/mobilenet_v3_large) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/mobilenet_v3_large_quantized.tflite ! queue ! \ qtimlvclassification threshold=51.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Mobilenet,q-offsets=<99.0>,q-scales=<0.18705224990844727>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## ResNet18 ResNet18 是一种机器学习模型，可以对 Imagenet 数据集中的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [ResNet18](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 的实现。 - 型号：[ResNet18](https://aihub.qualcomm.com/iot/models/resnet18) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/resnet18_quantized.tflite ! queue ! \ qtimlvclassification threshold=30.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Resnetnet,q-offsets=<68.0>,q-scales=<0.14944985508918762>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## ResNet101 ResNet101 是一种机器学习模型，可以对 Imagenet 数据集中的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [ResNet101](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 的实现。 - 型号：[ResNet101](https://aihub.qualcomm.com/iot/models/resnet101) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/resnet101_quantized.tflite ! queue ! \ qtimlvclassification threshold=51.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Resnet,q-offsets=<46.0>,q-scales=<0.2186901867389679 >;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## ResNeXt50 ResNeXt50 是一种机器学习模型，可以对 Imagenet 数据集中的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [ResNeXt50](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 的实现。 - 型号：[ResNeXt50](https://aihub.qualcomm.com/iot/models/resnext50) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/resnext50_quantized.tflite ! queue ! \ qtimlvclassification threshold=35.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Resnetnet,q-offsets=<30.0>,q-scales=<0.06314703077077866>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## ResNeXt101 ResNeXt101 是一种机器学习模型，可以对 Imagenet 数据集中的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [ResNeXt101](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 的实现。 - 型号：[ResNeXt101](https://aihub.qualcomm.com/iot/models/resnext101) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/resnext101_quantized.tflite ! queue ! \ qtimlvclassification threshold=35.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Resnetnet,q-offsets=<37.0>,q-scales=<0.1848793774843216>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## Shufflenet-v2 ShufflenetV2 是一种机器学习模型，可以对来自 Imagenet 数据集的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [Shufflenet-v2 的实现](https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py)。 - 型号：[Shufflenet-v2](https://aihub.qualcomm.com/iot/models/shufflenet_v2) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) Note 当使用此模型进行推理时，分类标签可能不会显示 gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/shufflenet_v2_quantized.tflite ! queue ! \ qtimlvclassification threshold=35.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Resnetnet,q-offsets=<69.0>,q-scales=<0.14428946375846863>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## SqueezeNet-1\_1 SqueezeNet 是一种机器学习模型，可以对来自 Imagenet 数据集的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [SqueezeNet-1_1](https://github.com/pytorch/vision/blob/main/torchvision/models/squeezenet.py) 的实现。 - 型号：[SqueezeNet-1_1](https://aihub.qualcomm.com/iot/models/squeezenet1_1) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) Note 当使用此模型进行推理时，分类标签可能不会显示 gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/squeezenet1_1_quantized.tflite ! queue ! \ qtimlvclassification threshold=25.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Resnetnet,q-offsets=<0.0>,q-scales=<0.16435524821281433>;" ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer. Copy to clipboard ## WideResNet50 WideResNet50 是一种机器学习模型，可以对来自 Imagenet 数据集的图像进行分类。它还可用于为特定用例构建更复杂的模型。 AI Hub 模型基于 [WideResNet50](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 的实现。 - 型号：[WideResNet50](https://aihub.qualcomm.com/iot/models/wideresnet50) - 标签：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) gst-launch-1.0 -e --gst-debug=2 \ filesrc location=/etc/media/video.mp4 ! qtdemux ! queue ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::position="<30, 30>" sink_1::dimensions="<640, 360>" ! queue ! waylandsink sync=true fullscreen=true \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/wideresnet50_quantized.tflite ! queue ! \ qtimlvclassification threshold=35.0 results=5 module=mobilenet labels=/etc/labels/imagenet_labels.txt \ extra-operation=softmax constants="Resnet,q-offsets=<44.0>,q-scales=<0.1439792960882187>;" ! video/x-raw,format=BGRA,width=640,height=360 ! queue ! mixer. Copy to clipboard Last Published: Oct 12, 2025 [Previous Topic 使用 AI Hub 模型和标签与 GStreamer API](https://docs.qualcomm.com/bundle/publicresource/80-70020-15BY/topics/ai-hub-qualcomm-im-sdk.md) [Next Topic 检测对象](https://docs.qualcomm.com/bundle/publicresource/80-70020-15BY/topics/object-detection.md)