Update README.md

README.md

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 # Deep Model Assembling
 
-This repository contains the official code for [Deep Model Assembling](https://arxiv.org/abs/2212.04129).
-
-<p align="center">
-    <img src="imgs/teaser.png" width= "450">
-</p>
+This repository contains the pre-trained models for [Deep Model Assembling](https://arxiv.org/abs/2212.04129).
 
 > **Title**:&emsp;&emsp;[**Deep Model Assembling**](https://arxiv.org/abs/2212.04129)  
 > **Authors**:&nbsp;&nbsp;[Zanlin Ni](https://scholar.google.com/citations?user=Yibz_asAAAAJ&hl=en&oi=ao), [Yulin Wang](https://scholar.google.com/citations?hl=en&user=gBP38gcAAAAJ), Jiangwei Yu, [Haojun Jiang](https://scholar.google.com/citations?hl=en&user=ULmStp8AAAAJ), [Yue Cao](https://scholar.google.com/citations?hl=en&user=iRUO1ckAAAAJ), [Gao Huang](https://scholar.google.com/citations?user=-P9LwcgAAAAJ&hl=en&oi=ao) (Corresponding Author)  
@@ -12,17 +8,10 @@ This repository contains the official code for [Deep Model Assembling](https://a
 > **Publish**:&nbsp;&nbsp;&nbsp;*arXiv preprint ([arXiv 2212.04129](https://arxiv.org/abs/2212.04129))*  
 > **Contact**:&nbsp;&nbsp;nzl22 at mails dot tsinghua dot edu dot cn
 
-## News
-
-- `Dec 10, 2022`: release code for training ViT-B, ViT-L and ViT-H on ImageNet-1K.
-
 ## Overview
 
 In this paper, we present a divide-and-conquer strategy for training large models. Our algorithm, Model Assembling, divides a large model into smaller modules, optimizes them independently, and then assembles them together. Though conceptually simple, our method significantly outperforms end-to-end (E2E) training in terms of both training efficiency and final accuracy. For example, on ViT-H, Model Assembling outperforms E2E training by **2.7%**, while reducing the training cost by **43%**.
 
-<p align="center">
-    <img src="imgs/ours.png" width= "900">
-</p>
 
 ## Data Preparation
 
@@ -153,40 +142,6 @@ python -m torch.distributed.launch --nproc_per_node=${NGPUS} --master_port=23346
 
 </details>
 
-## Results
-
-### Results on ImageNet-1K
-
-<p align="center">
-    <img src="./imgs/in1k.png" width= "900">
-</p>
-
-### Results on CIFAR-100
-
-<p align="center">
-    <img src="./imgs/cifar.png" width= "900">
-</p>
-
-### Training Efficiency
-
-- Comparing different training budgets
-
-<p align="center">
-    <img src="./imgs/efficiency.png" width= "900">
-</p>
-
-- Detailed convergence curves of ViT-Huge
-
-<p align="center">
-    <img src="./imgs/huge_curve.png" width= "450">
-</p>
-
-### Data Efficiency
-
-<p align="center">
-    <img src="./imgs/data_efficiency.png" width= "450">
-</p>
-
 ## Citation
 
 If you find our work helpful, please **star🌟** this repo and **cite📑** our paper. Thanks for your support!