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This catalog contains a variety of classification samples for users' reference. The directory structure and specific instructions are as follows.
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 |
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