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This catalog contains a variety of classification samples for users' reference. The directory structure and specific instructions are as follows.
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googlenet series sample
Sample name Sample description Characteristic analysis support chip googlenet_imagenet_picture Picture Classification The input and output are all JPG images, and the model is the GoogLeNet model based on Caffe Ascend310 googlenet_mindspore_picture Picture Classification Both input and output are JPG images, and the model is the GoogLeNet model based on MindSpore Ascend310 googlenet_onnx_picture Picture Classification Both input and output are JPG images, and the model is GoogLeNet model based on pytorch Ascend310 googlenet_imagenet_multi_batch Picture Classification The input and output are all JPG images, and the model is the GoogLeNet model based on Caffe, which uses the feature of multiple batches Ascend310 -
resnet50 series sample
Sample name Sample description Characteristic analysis support chip resnet50_imagenet_classification Picture Classification The input is a JPG picture, and the output is a screen print. Image classification based on Caffe ResNet-50 network (synchronous reasoning) Ascend310，Ascend310P，Ascend910 resnet50_async_imagenet_classification Picture Classification The input is a JPG picture, and the output is a screen print. Image classification based on Caffe ResNet-50 network (asynchronous reasoning) Ascend310，Ascend310P，Ascend910 resnet50_mindspore_picture Picture Classification Both input and output are JPG images. Use the MindSpore-based resnet50 model to classify and infer input images Ascend310 vdec_resnet50_classification Picture Classification The input is an h264 file, and the output is a screen print. Image classification based on Caffe ResNet-50 network (video decoding + synchronous reasoning) Ascend310，Ascend310P，Ascend910 vpc_jpeg_resnet50_imagenet_classification Picture Classification Input is YUV picture, output is screen printing/JPG picture. Realize image classification based on Caffe ResNet-50 network (image decoding + matting zoom + image encoding + synchronous reasoning) Ascend310，Ascend310P，Ascend910 vpc_resnet50_imagenet_classification Picture Classification The input is a JPG picture, and the output is a screen print. Image classification based on Caffe ResNet-50 network (picture decoding + scaling + synchronous reasoning) Ascend310，Ascend310P，Ascend910 resnet50_imagenet_dynamic_hw Picture Classification The input is a JPG picture, and the output is a screen print. Image classification based on TensorFlow ResNet-50 network (synchronous reasoning),which uses the feature of Dynamic resolution Ascend310 -
other sample
sample description support chip inceptionv3_picture Image classification example of IncpetionV3 model based on Pytorch framework Ascend310 lenet_mindspore_picture Image classification example of lenet model based on Mindspore framework Ascend310 vgg16_cat_dog_picture Example of cat and dog classification based on the vgg16 model of the caffe framework Ascend310