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Searched refs:ImageNet (Results 1 – 10 of 10) sorted by relevance

/external/XNNPACK/bench/
Dprelu.cc231 static void ImageNet(benchmark::internal::Benchmark* b) in ImageNet() function
243 BENCHMARK_CAPTURE(xnnpack_prelu_f32, imagenet, "ImageNet 224x224")->Apply(ImageNet)->UseRealTime();
246 BENCHMARK_CAPTURE(tflite_prelu_f32, imagenet, "ImageNet 224x224")->Apply(ImageNet)->UseRealTime();
Daverage-pooling.cc326 static void ImageNet(benchmark::internal::Benchmark* b) { in ImageNet() function
389 BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime(…
397 BENCHMARK_CAPTURE(tflite_average_pooling_f32, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
406 BENCHMARK_CAPTURE(xnnpack_average_pooling_qu8, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime(…
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_LRN.pbtxt44 For details, see [Krizhevsky et al., ImageNet classification with deep
/external/gemmlowp/meta/
DREADME64 The library shows up to 35% faster gemm execution in some cases (e.g. ImageNet
/external/tensorflow/tensorflow/lite/java/ovic/
DREADME.md131 * change `TEST_IMAGE_GROUNDTRUTH` (ImageNet class ID) to be consistent
268 compute the reference accuracy for ImageNet classification. The naming
/external/tensorflow/tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/
DREADME.md105 In order to use this tool to run evaluation on the full 50K ImageNet dataset,
/external/tensorflow/tensorflow/lite/g3doc/performance/
Ddelegates.md173 * [ImageNet Image Classification](https://storage.googleapis.com/tensorflow-nightly-public/prod/t…
/external/tensorflow/tensorflow/lite/micro/examples/person_detection/
Dtraining_a_model.md65 ImageNet one-thousand class data that's widely used for training image
/external/tensorflow/tensorflow/compiler/mlir/lite/ir/
Dtfl_ops.td1161 For details, see [Krizhevsky et al., ImageNet classification with deep
/external/tensorflow/tensorflow/compiler/mlir/tensorflow/ir/
Dtf_generated_ops.td5976 For details, see [Krizhevsky et al., ImageNet classification with deep