The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). To date, it contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format).
The benchmarking dataset, GenAI on the Edge, contains performance metrics from evaluating Large Language Models (LLMs) on edge devices, utilizing a distributed testbed of Raspberry Pi devices orchestrated by Kubernetes (K3s). It includes performance data collected from multiple runs of prompt-based evaluations with various LLMs, leveraging Prometheus and the Llama.cpp framework. The dataset captures key metrics such as resource utilization, token generation rates/throughput, and detailed inference timing for stages such as Sample, Prefill, and Decode.
