diff --git a/readme.md b/readme.md
index 85e94b2..8ca286a 100644
--- a/readme.md
+++ b/readme.md
@@ -1,9 +1,14 @@
 # GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection.
+[ECCV2024]The official code of ["GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection"](https://arxiv.org/abs/2406.07487). 
 
-Official implementation of [GLAD](https://arxiv.org/abs/2406.07487)
-
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/glad-towards-better-reconstruction-with/anomaly-detection-on-visa)](https://paperswithcode.com/sota/anomaly-detection-on-visa?p=glad-towards-better-reconstruction-with)
 
+![image](https://github.com/hyao1/GLAD/assets/52654892/62a8d52d-72ab-4bda-8fb4-41b5d8e0a044)
 
+## News
+* [07/13/2024] Support for mixed_precision training.
+* [07/13/2024] Release the [PCB-Bank](https://github.com/SSRheart/PCB-Bank) dataset we integrated.
+  
 ## Requirements
 This repository is implemented and tested on Python 3.10 and PyTorch 2.0.1.
 To install requirements:
@@ -12,15 +17,17 @@ To install requirements:
 pip install -r requirements.txt
 ```
 
-## Train and Evaluation of the Model
-First, you should download the pretrained stable diffusion model from [pretrained model](https://huggingface.co/CompVis/stable-diffusion-v1-4), and datasets. (In addition, [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/) dataset is required for anomaly synthesis) 
+## Models Trained by Us
+Models (VAE, Unet, DINO) trained by us are here: [OneDrive](https://stuhiteducn-my.sharepoint.com/:f:/g/personal/23b903042_stu_hit_edu_cn/Etg1bdDSnOZBt7AydlkCzMUBYKxgmM_9tB-g5M70PJhAVQ).
+
+## Training and Evaluation of the Model for Single-class
+First, you should download the pretrained stable diffusion model from [pretrained model](https://huggingface.co/CompVis/stable-diffusion-v1-4), and datasets. (In addition, [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/) dataset is required for anomaly synthesis). If you can not download pretrained stable diffusion model, we provided it in our [OneDrive](https://stuhiteducn-my.sharepoint.com/:f:/g/personal/23b903042_stu_hit_edu_cn/Etg1bdDSnOZBt7AydlkCzMUBYKxgmM_9tB-g5M70PJhAVQ).
 
 To train the UNet of stable diffusion, modify the settings in the train.sh and train the model on different categories:
 
 ```train
 bash train.sh
 ```
-Model checkpoints trained by us are [Baidu Netdisk](https://pan.baidu.com/s/1xH24M4OxAmutRxVPlc011w?pwd=lpk3)
 
 To evaluate and test the model, modify the path of models in the main.py and test.sh, and run:
 
@@ -28,19 +35,44 @@ To evaluate and test the model, modify the path of models in the main.py and tes
 bash test.sh
 ```
 
-In particular, considering the large differences between the visa dataset and the pre-trained model, we fine-tune VAE of stable diffusion and DINO.
+In particular, considering the large differences between the VisA and PCB-Bank dataset and the pre-trained model, we fine-tune VAE of stable diffusion and DINO.
 You can refer to the [DiffAD](https://github.com/Loco-Roco/DiffAD) for fine-tuning VAE. 
 
-To fine-tune DINO, run:
+To fine-tune DINO (referring to [DDAD](https://github.com/arimousa/DDAD)), run:
 
 ```fine-tune DINO
-python train-dino.py --dataset VisA
+python train_dino.py --dataset VisA
 ```
-To test DINO, run:
+Quantitative results on MVTec-AD, MPDD, VisA and PCB-Bank datasets. Metrics are I-AUROC/I-AP/I-F1-max at first raw (for detection) and P-AUROC/PAP/P-F1-max/PRO at second raw (for localization).
+![image](https://github.com/hyao1/GLAD/assets/52654892/522bf587-8471-4e7e-be3e-c0b080915691)
+
+## Training and Evaluation of the Model for Multi-class
+We also test our method at multi-class setting. Pretrained stable diffusion model also is required,  and models (VAE, Unet, DINO) trained by us can be download from [OneDrive](https://stuhiteducn-my.sharepoint.com/:f:/g/personal/23b903042_stu_hit_edu_cn/Etg1bdDSnOZBt7AydlkCzMUBYKxgmM_9tB-g5M70PJhAVQ).
+
+To train the UNet of stable diffusion, modify the settings in the train.sh and train the model on different categories:
+
+```train
+bash train_multi.sh
+```
+
+To evaluate and test the model, modify the path of models in the main.py and test.sh, and run:
+
+```test
+bash test_multi.sh
+```
+In particular, we fine-tune VAE of stable diffusion for VisA and PCB-Bank referring to [DiAD](https://github.com/lewandofskee/DiAD).
+
+To fine-tune DINO (referring to [DDAD](https://github.com/arimousa/DDAD)), run:
 
 ```fine-tune DINO
-python test-dino.py --dataset VisA
+python train_dino_multi.py --dataset VisA
 ```
+Quantitative results of multi-category setting on MVTec-AD, MPDD, VisA and PCB-Bank datasets. Metrics are I-AUROC/I-AP/I-F1-max at first raw (for detection) and P-AUROC/P-AP/P-F1-max/PRO at second raw (for localization).
+![image](https://github.com/hyao1/GLAD/assets/52654892/b38fe5af-1cb4-4d89-95f3-9ff59f98d96f)
+
+## Training and Evaluation of the Model for Multi-class
+
+The PCB-Bank dataset of the printing circuit board we integrated can be downloaded from here: [PCB-Bank](https://github.com/SSRheart/PCB-Bank)
 
 ## Citation
 
@@ -56,3 +88,4 @@ python test-dino.py --dataset VisA
 ## Feedback
 
 For any feedback or inquiries, please contact yaohang_1@outlook.com
+