[upd] README.md; upload the PPT slides

README.md

@@ -1,4 +1,4 @@
-# SparK: the first successful BERT/MAE-style pretraining on any convolutional networks  [![Reddit](https://img.shields.io/badge/Reddit-🔥%20120k%20views-b31b1b.svg?style=social&logo=reddit)](https://www.reddit.com/r/MachineLearning/comments/10ix0l1/r_iclr2023_spotlight_the_first_bertstyle/) [![Twitter](https://img.shields.io/badge/Twitter-🔥%2020k%2B120k%20views-b31b1b.svg?style=social&logo=twitter)](https://twitter.com/keyutian/status/1616606179144380422)
+# SparK: the first successful BERT/MAE-style pretraining on *any* convolutional networks  [![Reddit](https://img.shields.io/badge/Reddit-🔥%20120k%20views-b31b1b.svg?style=social&logo=reddit)](https://www.reddit.com/r/MachineLearning/comments/10ix0l1/r_iclr2023_spotlight_the_first_bertstyle/) [![Twitter](https://img.shields.io/badge/Twitter-🔥%2020k%2B120k%20views-b31b1b.svg?style=social&logo=twitter)](https://twitter.com/keyutian/status/1616606179144380422)
 
 This is the official implementation of ICLR paper [Designing BERT for Convolutional Networks: ***Spar***se and Hierarchical Mas***k***ed Modeling](https://arxiv.org/abs/2301.03580).
 We've tried our best to make the codebase clean, short, easy to read, state-of-the-art, and only rely on minimal dependencies.
@@ -21,7 +21,8 @@ We've tried our best to make the codebase clean, short, easy to read, state-of-t
 
 ## 🔥 News
 
-- The share on [TechBeat (将门创投)](https://www.techbeat.net/talk-info?id=758) is scheduled on **Mar. 16th (UTC+0 12am)** too! [[`📹Recorded Video`](https://www.techbeat.net/talk-info?id=758)]
+- On **Mar. 22nd (UTC+8 8pm; UTC+0 12am)** another livestream would be held at [极市平台-bilibili](https://live.bilibili.com/3344545).
+- The share on [TechBeat (将门创投)](https://www.techbeat.net/talk-info?id=758) is scheduled on **Mar. 16th (UTC+8 8pm)** too! [[`📹Recorded Video`](https://www.techbeat.net/talk-info?id=758)]
 - We are honored to be invited by Synced ("机器之心机动组 视频号" on WeChat) to give a talk about SparK on **Feb. 27th (UTC+0 11am, UTC+8 7pm)**, welcome! [[`📹Recorded Video`](https://www.bilibili.com/video/BV1J54y1u7U3/)]
 - This work got accepted to ICLR 2023 as a Spotlight (notable-top-25%).
 - Other articles: [[`Synced`](https://syncedreview.com/2023/01/19/bert-style-pretraining-on-convnets-peking-u-bytedance-oxford-us-sparse-masked-modelling-with-hierarchy-leads-the-way/)]
@@ -36,13 +37,13 @@ We've tried our best to make the codebase clean, short, easy to read, state-of-t
  [[`ReadPaper笔记`](https://readpaper.com/paper/4710371282714116097)]
 
 
-## 📺 Video demo
+## 📺 Video demo (ppt slides attached to [slides-animations-demo](https://github.com/keyu-tian/SparK/releases/tag/file_sharing))
 
 https://user-images.githubusercontent.com/6366788/213662770-5f814de0-cbe8-48d9-8235-e8907fd81e0e.mp4
 
 ## 🕹️ CoLab Visualization Demo
 
-Check [pretrain/viz_reconstruction.ipynb](pretrain/viz_reconstruction.ipynb) which visualizes the reconstruction of SparK pre-trained models, like:
+Check [pretrain/viz_reconstruction.ipynb](pretrain/viz_reconstruction.ipynb) for visualizing the reconstruction of SparK pre-trained models, like:
 
 
 <p align="center">
@@ -92,14 +93,14 @@ We also provide [pretrain/viz_spconv.ipynb](pretrain/viz_spconv.ipynb) that show
 - [ ] Weights in `timm`
 
 
-## Pre-trained weights
+## SparK Pre-trained weights
 
 **Note: for network definitions, we directly use `timm.models.ResNet` and [official ConvNeXt](https://github.com/facebookresearch/ConvNeXt/blob/048efcea897d999aed302f2639b6270aedf8d4c8/models/convnext.py).**
 
-`reso.`: the image resolution; `acc@1`: IN1k fine-tuned acc (top-1)
+`reso.`: the image resolution; `acc@1`: ImageNet-1K fine-tuned acc (top-1)
 
 
-| arch. | reso. | acc@1 | #params | flops | weights on google drive  |
+| arch. | reso. | acc@1 | #params | flops | SparK pre-trained weights (self-supervised) |
 |:--------------:|:-----:|:-----:|:-------:|:------:|:------------------------------------------------------------------------------------------------------------------------------------------|
 | ResNet50 | 224 | 80.6 | 26M | 4.1G | [resnet50_1kpretrained_timm_style.pth](https://drive.google.com/file/d/1H8605HbxGvrsu4x4rIoNr-Wkd7JkxFPQ/view?usp=share_link) |
 | ResNet101 | 224 | 82.2 | 45M | 7.9G | [resnet101_1kpretrained_timm_style.pth](https://drive.google.com/file/d/1ZwTztjU-_rfvOVfLoce9SMw2Fx0DQfoO/view?usp=share_link) |