@@ -5,18 +5,22 @@ We compare our results with some popular frameworks and official releases in ter
## Comparision Rules
Here we compare our MMAction repo with other video understanding toolboxes in the same data and model settings
by the training time per iteration.
by the training time per iteration. We use the
commit id [7f3490d](https://github.com/open-mmlab/mmaction/tree/7f3490d3db6a67fe7b87bfef238b757403b670e3)(1/5/2020) of MMAction V0.1,
commit id [8d53d6f](https://github.com/mit-han-lab/temporal-shift-module/tree/8d53d6fda40bea2f1b37a6095279c4b454d672bd) of Temporal-Shift-Module,
commit id [133e40f](https://github.com/facebookresearch/SlowFast/tree/133e40f8349ce37b0e6168639da0811a413579c8) of PySlowFast,
commit id [f13707f](https://github.com/wzmsltw/BSN-boundary-sensitive-network/tree/f13707fbc362486e93178c39f9c4d398afe2cb2f)(12/12/2018) of BSN(boundary sensitive network).
To ensure the fairness of the comparison, the comparison experiments were conducted under the same hardware environment and using the same dataset.
For each model setting, we kept the same data preprocessing methods to make sure the same feature input.
In addition, we also used MemCache, a distributed cached system, to load the data for the same IO time.
The time we measured is the average training time for an iteration, including data processing and model training.
The training speed is measure with s/iter. The lower, the better.
