# Classify images

Before running the pipeline command for a model, follow the required [Prerequisites](https://docs.qualcomm.com/doc/80-70029-15B/topic/use-ai-hub-models-with-gstreamer.html#prerequisites).

Run the following command in the SSH shell before running the classification commands.

export XDG_RUNTIME_DIR=/dev/socket/weston && export WAYLAND_DISPLAY=wayland-1
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## GoogLeNet

GoogLeNet is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

The AI Hub model is based on [this implementation of GoogLeNet](https://github.com/pytorch/vision/blob/main/torchvision/models/googlenet.py).

- Model: [GoogLeNet](https://aihub.qualcomm.com/iot/models/googlenet)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

Note

Classification labels may not appear when using this model for inference

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 ! \
    qtimlpostprocess settings="{\"confidence\": 51.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json \
    ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer.
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## Inception-v3

InceptionNetV3 is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

This model is post-training quantized to int8 using samples from Google’s open images dataset.

The AI Hub model is based on [this implementation of Inception-v3](https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py).

- Model: [Inception-v3](https://aihub.qualcomm.com/iot/models/inception_v3)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

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 ! \
    qtimlpostprocess settings="{\"confidence\": 51.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json \
    ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer.
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## ResNet18

ResNet18 is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

The AI Hub model is based on [this implementation of ResNet18](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).

- Model: [ResNet18](https://aihub.qualcomm.com/iot/models/resnet18)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

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 ! \
    qtimlpostprocess settings="{\"confidence\": 30.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer.
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## ResNet101

ResNet101 is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

The AI Hub model is based on [this implementation of ResNet101](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).

- Model: [ResNet101](https://aihub.qualcomm.com/iot/models/resnet101)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

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 ! \
    qtimlpostprocess settings="{\"confidence\": 51.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json ! \
    video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer.
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## ResNeXt50

ResNeXt50 is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

The AI Hub model is based on [this implementation of ResNeXt50](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).

- Model: [ResNeXt50](https://aihub.qualcomm.com/iot/models/resnext50)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

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 ! \
    qtimlpostprocess settings="{\"confidence\": 35.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json ! video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer.
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## ResNeXt101

ResNeXt101 is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

The AI Hub model is based on [this implementation of ResNeXt101](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).

- Model: [ResNeXt101](https://aihub.qualcomm.com/iot/models/resnext101)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

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 ! \
    qtimlpostprocess settings="{\"confidence\": 35.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json ! \
    video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer.
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## Shufflenet-v2

ShufflenetV2 is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

The AI Hub model is based on [this implementation of Shufflenet-v2](https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py).

- Model: [Shufflenet-v2](https://aihub.qualcomm.com/iot/models/shufflenet_v2)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

Note

Classification labels may not appear when using this model for inference

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 ! \
    qtimlpostprocess settings="{\"confidence\": 35.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json ! \
    video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer.
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## SqueezeNet-1\_1

SqueezeNet is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

The AI Hub model is based on [this implementation of SqueezeNet-1_1](https://github.com/pytorch/vision/blob/main/torchvision/models/squeezenet.py).

- Model: [SqueezeNet-1_1](https://aihub.qualcomm.com/iot/models/squeezenet1_1)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

Note

Classification labels may not appear when using this model for inference

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 ! \
    qtimlpostprocess settings="{\"confidence\": 25.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json ! \
    video/x-raw,format=BGRA,width=640, height=360 ! queue ! mixer.
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## WideResNet50

WideResNet50 is a machine learning model that can classify images from the Imagenet dataset. It can also be used to build more complex models for specific use cases.

The AI Hub model is based on [this implementation of WideResNet50](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).

- Model: [WideResNet50](https://aihub.qualcomm.com/iot/models/wideresnet50)
- Label: [classification.json](https://github.com/quic/sample-apps-for-qualcomm-linux/blob/main/qualcomm-linux/artifacts/json_labels/classification.json)

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 ! \
    qtimlpostprocess settings="{\"confidence\": 35.0}" results=5 module=mobilenet-softmax labels=/etc/labels/classification.json ! \
    video/x-raw,format=BGRA,width=640,height=360 ! queue ! mixer.
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Last Published: Apr 02, 2026

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