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8 changes: 4 additions & 4 deletions README.md
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Expand Up @@ -10,7 +10,7 @@
This is a PyTorch implementation of InceptionNeXt proposed by our paper "[InceptionNeXt: When Inception Meets ConvNeXt](https://arxiv.org/abs/2303.16900)". Many thanks to [Ross Wightman](https://github.com/rwightman), InceptionNeXt is integrated into [timm](https://github.com/huggingface/pytorch-image-models).

![InceptionNeXt](https://user-images.githubusercontent.com/15921929/228630174-1d31ac66-174b-4014-9f6a-b7e6d46af958.jpeg)
**TLDR**: To speed up ConvNeXt, we build InceptionNeXt by decomposing the large kernel dpethweise convolution with Inception style. **Our InceptionNeXt-T enjoys both ResNet-50’s speed and ConvNeXt-T’s accuracy.**
**TLDR**: To speed up ConvNeXt, we build InceptionNeXt by decomposing the large kernel depthwise convolution with Inception style. **Our InceptionNeXt-T enjoys both ResNet-50’s speed and ConvNeXt-T’s accuracy.**


## Requirements
Expand Down Expand Up @@ -60,7 +60,7 @@ Data preparation: ImageNet with the following folder structure, you can extract
| [convnext_tiny_k3_par1_8](https://github.com/sail-sg/inceptionnext/releases/download/model/convnext_tiny_k3_par1_8.pth) | 224 | 28M | 4.4G | 901 | 2833 | 80.8 |
| [convnext_tiny_k3_par1_16](https://github.com/sail-sg/inceptionnext/releases/download/model/convnext_tiny_k3_par1_16.pth) | 224 | 28M | 4.4G | 916 | 2846 | 80.1 |

The throughputs are measured on an A100 with full precisioni and batch size of 128. See [Benchmarking throughput](#benchmarking-throughput).
The throughputs are measured on an A100 with full precision and batch size of 128. See [Benchmarking throughput](#benchmarking-throughput).

#### Usage
We also provide a Colab notebook which run the steps to perform inference with InceptionNeXt: [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1-CAPm6FNKYRbe_lAPxIBxsIH4xowgfg8?usp=sharing)
Expand All @@ -77,7 +77,7 @@ python3 validate.py /path/to/imagenet --model $MODEL -b 128 \
```

## Benchmarking throughput
On the environment described above, we benchmark throughputs on an A100 with batch size of 128. The beter results of "Channel First" and "Channel Last" memory layouts are reported.
On the environment described above, we benchmark throughputs on an A100 with batch size of 128. The better results of "Channel First" and "Channel Last" memory layouts are reported.

For Channel First:
```bash
Expand Down Expand Up @@ -118,7 +118,7 @@ cd $CODE_PATH && sh distributed_train.sh $NUM_GPU $DATA_PATH \
Training (fine-tuning) scripts of other models are shown in [scripts](/scripts/).


## Bibtex
## BibTeX
```
@article{yu2023inceptionnext,
title={InceptionNeXt: when inception meets convnext},
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