# 圖像分類 在運行模型的管道命令之前，請遵循必要的 [先決條件](https://docs.qualcomm.com/doc/80-70018-15BT/topic/ai-hub-qualcomm-im-sdk.html#prerequisites)。 在運行分類命令之前，在SSH shell中運行以下命令。 export XDG_RUNTIME_DIR=/dev/socket/weston && export WAYLAND_DISPLAY=wayland-1 Copy to clipboard ## GoogLeNetQuantized GoogLeNet是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更復雜的特定用例模型。 AI Hub模型基於 [GoogLeNetQuantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/googlenet.py)。 - 模型：[googlenet_quantized.tflite](https://aihub.qualcomm.com/iot/models/googlenet_quantized) - 標籤：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) 備註 使用此模型進行推理時可能不會顯示分類標籤。 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-Quantized InceptionNetV3是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 該模型使用來自Google開放圖像數據集的樣本進行訓練後量化為int8。 AI Hub模型基於 [Inception-v3-Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py) 。 - 模型：[inception_v3_quantized.tflite](https://aihub.qualcomm.com/iot/models/inception_v3_quantized) - 標籤：[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-Quantized MobileNetV2是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [MobileNet-v2-Quantized的實現](https://github.com/tonylins/pytorch-mobilenet-v2/tree/master) 。 - 模型：[mobilenet_v2_quantized.tflite](https://aihub.qualcomm.com/iot/models/mobilenet_v2_quantized) - 標籤：[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-Quantized MobileNet-v3-Large是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [MobileNet-v3-Large-Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv3.py) 。 - 模型：[mobilenet_v3_large_quantized.tflite](https://aihub.qualcomm.com/iot/models/mobilenet_v3_large_quantized) - 標籤：[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-Quantized ResNet18是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [ResNet18Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 。 - 模型：[resnet18_quantized.tflite](https://aihub.qualcomm.com/iot/models/resnet18_quantized) - 標籤：[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-Quantized ResNet101是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [ResNet101Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 。 - 模型：[resnet101_quantized.tflite](https://aihub.qualcomm.com/iot/models/resnet101_quantized) - 標籤：[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-Quantized ResNeXt50是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [ResNeXt50Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 。 - 模型：[resnext50_quantized.tflite](https://aihub.qualcomm.com/iot/models/resnext50_quantized) - 標籤：[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-Quantized ResNeXt101是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [ResNeXt101Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 。 - 模型：[resnext101_quantized.tflite](https://aihub.qualcomm.com/iot/models/resnext101_quantized) - 標籤：[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-Quantized ShufflenetV2是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [Shufflenet-v2Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py) 。 - 模型：[shufflenet_v2_quantized.tflite](https://aihub.qualcomm.com/iot/models/shufflenet_v2_quantized) - 標籤：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) 備註 使用此模型進行推理時可能不會顯示分類標籤。 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-Quantized SqueezeNet是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [SqueezeNet-1_1Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/squeezenet.py) 。 - 模型：[squeezenet1_1_quantized.tflite](https://aihub.qualcomm.com/iot/models/squeezenet1_1_quantized) - 標籤：[imagenet_labels.txt](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/labels/imagenet_labels.txt) 備註 使用此模型進行推理時可能不會顯示分類標籤。 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-Quantized WideResNet50是一種機器學習模型，可以對Imagenet數據集中的圖像進行分類。它還可以用於構建更複雜的特定用例模型。 AI Hub模型基於 [WideResNet50-Quantized的實現](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) 。 - 模型：[wideresnet50_quantized.tflite](https://aihub.qualcomm.com/iot/models/wideresnet50_quantized) - 標籤：[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 15, 2025 [Previous Topic 使用 GStreamer API 與 AI Hub 模型和標籤](https://docs.qualcomm.com/bundle/publicresource/80-70018-15BT/topics/ai-hub-qualcomm-im-sdk.md) [Next Topic 偵測對象](https://docs.qualcomm.com/bundle/publicresource/80-70018-15BT/topics/object-detection.md)