Skip to content

Files

Latest commit

42f83de · Jun 28, 2021

History

History

datasets

File Specifics

Model Source Dimensions Min Accuracy File Format # Stimuli
vww01 COCO2014/val2017 96x96 80% U8C3, RGB, where [0]=ulc and [9215]=lrc 500 true, 500 false
ic01 CIFAR-10 32x32 85% U8C3, RGB, where [0]=ulc and [1024]=lrc; this is different from original CiFAR-10 array which is 1024R, 1024G, 1024B 200, 10 classes
ad01 ToyADMOS/car n/a AUC: 0.85 Spectrogram, 5 slices, 128 freq. bins, FP32LE 108 anomaly, 140 normal
kws01 Speech Commands v2 n/a 90% Spectrogram, 49 frames x 10 MFCCs as INT8 1000 features, 12 classes

Ground Truth Format

For everything but anomaly detection, the y_labels.csv file format is:

input file name,total number of classes,predicted class number

For anomaly datection, the y_labels.csv file format specifies a sliding window for the input file

input file name,total number of classes,predicted classes,window width (bytes),stride (bytes)

Where number of classes is always 2 (anomaly, normal), and 0=normal, 1=anomaly.

For each entry, the corresponding file must exist in the same directory as the ground-truths file. Copy those files from the dataset to the folder. For example, if you are using the image classification model, ic01, do this after completing training:

% cd tiny/v0.5/training/image_classification
% mkdir -p ~/eembc/runner/benchmarks/ulp-mlperf/datasets/ic01/
% cp y_labels.c !$
% cp perf_dataset/* !$

Each model has it's own method for constructing the input files. Please refer to the training folder in the tiny repo the model you are interested in, the README's will explain more.