Global Model Accuracy and Forgetting Rate
- Citation Author(s):
-
Benteng Zhang
- Submitted by:
- Benteng Zhang
- Last updated:
- DOI:
- 10.21227/5jb0-pf84
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Abstract
The Tiny-ImageNet dataset contains 200 categories and approximately 120,000 samples. The CIFAR-10 and CIFAR-100 datasets respectively contain 10 and 100 categories.
All experiments were conducted on a server equipped with two NVIDIA A100 GPUs (each with 80GB memory), running the Ubuntu 20.04 operating system and the CUDA 11.8 computing platform under the Pytorch 1.8 framework. The server has 256GB of memory and is powered by a 64-core Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz.
Instructions:
The Tiny-ImageNet dataset contains 200 categories and approximately 120,000 samples. The CIFAR-10 and CIFAR-100 datasets respectively contain 10 and 100 categories.
All experiments were conducted on a server equipped with two NVIDIA A100 GPUs (each with 80GB memory), running the Ubuntu 20.04 operating system and the CUDA 11.8 computing platform under the Pytorch 1.8 framework. The server has 256GB of memory and is powered by a 64-core Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz.
