@@ -139,7 +139,7 @@ A notebook demo can be found in [demo/demo.ipynb](/demo/demo.ipynb)
### Build a model with basic components
In MMAction, model components are basically categorized as 4 types.
In MMAction2, model components are basically categorized as 4 types.
- recognizer: the whole recognizer model pipeline, usually contains a backbone and cls_head.
- backbone: usually an FCN network to extract feature maps, e.g., ResNet, BNInception.
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@@ -216,7 +216,7 @@ are good examples which show how to do that.
### Iteration pipeline
MMAction implements distributed training and non-distributed training,
MMAction2 implements distributed training and non-distributed training,
which uses `MMDistributedDataParallel` and `MMDataParallel` respectively.
We adopt distributed training for both single machine and multiple machines.
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@@ -278,7 +278,7 @@ Here is an example of using 8 GPUs to load TSN checkpoint.
### Train with multiple machines
If you can run MMAction on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
If you can run MMAction2 on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)