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Abdelrahman Shaker
2024-01-12 17:00:03 +04:00
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README.md
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@@ -80,7 +80,7 @@ Community-driven results with [Samsung Galaxy S23 Ultra, with Qualcomm Snapdrago
| -------------- | -----| ----- | ------ | | -------------- | -----| ----- | ------ |
| Latency (msec) | 2.17 | 1.69 | 1.7 | | Latency (msec) | 2.17 | 1.69 | 1.7 |
Refer to script above for details of the input & block parameters. Refer to the script above for details of the input & block parameters.
❓ _Interested in reproducing the results above?_ ❓ _Interested in reproducing the results above?_
@@ -100,7 +100,7 @@ pip install timm
pip install coremltools==5.2.0 pip install coremltools==5.2.0
``` ```
### Data preparation ### Data Preparation
Download and extract ImageNet train and val images from http://image-net.org. The training and validation data are expected to be in the `train` folder and `val` folder respectively: Download and extract ImageNet train and val images from http://image-net.org. The training and validation data are expected to be in the `train` folder and `val` folder respectively:
``` ```
@@ -109,7 +109,7 @@ Download and extract ImageNet train and val images from http://image-net.org. Th
|-- val |-- val
``` ```
### Single machine multi-GPU training ### Single-machine multi-GPU training
We provide training script for all models in `dist_train.sh` using PyTorch distributed data parallel (DDP). We provide training script for all models in `dist_train.sh` using PyTorch distributed data parallel (DDP).
@@ -129,7 +129,7 @@ On a Slurm-managed cluster, multi-node training can be launched as
sbatch slurm_train.sh /path/to/imagenet SwiftFormer_XS sbatch slurm_train.sh /path/to/imagenet SwiftFormer_XS
``` ```
Note: specify slurm specific paramters in `slurm_train.sh` script. Note: specify slurm specific parameters in `slurm_train.sh` script.
### Testing ### Testing
@@ -156,7 +156,9 @@ If you have any questions, please create an issue on this repository or contact
## Acknowledgement ## Acknowledgement
Our code base is based on [LeViT](https://github.com/facebookresearch/LeViT) and [EfficientFormer](https://github.com/snap-research/EfficientFormer) repositories. We thank authors for their open-source implementation. Our code base is based on [LeViT](https://github.com/facebookresearch/LeViT) and [EfficientFormer](https://github.com/snap-research/EfficientFormer) repositories. We thank the authors for their open-source implementation.
I'd like to express my sincere appreciation to [Victor Escorcia](https://github.com/escorciav) for measuring & reporting the latency of SwiftFormer on Android (Samsung Galaxy S23 Ultra, with Qualcomm Snapdragon 8 Gen 2). Check [SwiftFormer Meets Android](https://github.com/escorciav/SwiftFormer) for more details!
## Our Related Works ## Our Related Works