Update readme (#38)

README.md

@@ -8,30 +8,30 @@ This repo contains the supported code and configuration files to reproduce objec
 ## MODELs ## 
 ### Pretained based on COCO dataset ###
 
-Those are the base model used for the YONOD work. You can download them from the official SWIN transformer repo, you can also download a backup from the link(Google Drive) provided here:
+Those are the base model used for the UODDM work. You can download them from the official SWIN transformer repo, you can also download a backup from the link(Google Drive) provided here:
 | Finetune dataset | model         | checkpoint   | 
 |--------------|-----------|------------|
 | COCO | Swin-T| [swin-t-model](https://drive.google.com/file/d/1kJ_4Bc2qh7mZdG3E_QhtqMNiOZb-Dj-l/view?usp=sharing)|
 | COCO | Swin-S| [swin-s-model](https://drive.google.com/file/d/1tggozECuNp8_Jcj3fKKKq_npQy78EH2X/view?usp=sharing)|
 
-### YONOD finetune on SUN RGBD dataset ###
-The YONOD work was finetuning the above model based on SUN RGBD dataset. It has two models based on different modalities:
+### UODDM finetune on SUN RGBD dataset ###
+The UODDM work was finetuning the above model based on SUN RGBD dataset. It has two models based on different modalities:
 - INPUT A: RGB.
 - INPUT B: RGB and DHS and RGB DHS mixed based on chessboard mixture.    
 
-We also had a input only as DHS model can be found in the [simCrossTrans](https://arxiv.org/abs/2203.10456) work. Here the performance based on mAP50 for SUNRGBD10, which includes a 10 common categories. Details please check the [YONOD](https://arxiv.org/pdf/2207.01071.pdf) paper.
+We also had a input only as DHS model can be found in the [simCrossTrans](https://arxiv.org/abs/2203.10456) work. Here the performance based on mAP50 for SUNRGBD10, which includes a 10 common categories. Details please check the [UODDM](https://arxiv.org/pdf/2207.01071.pdf) paper.
 
 | Finetune dataset |input| model         | checkpoint   | configure file| performance on RGB validation | performance on DHS validation |performance when both RGB and DHS are available|log| 
 |--------------|-----------|------------|--------|-------|---------|--------|-----|------| 
-|SUN RGBD | INPUT A| swin-t|[basedRGB](https://drive.google.com/file/d/1cfIxRG4vumIAX3T1cem79gqUZcp1o1-n/view?usp=sharing)|[cfg](https://github.com/liketheflower/YONOD/blob/master/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_RGB_load_from/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_RGB_load_from.py)|53.9|N/A|N/A|[log](https://raw.githubusercontent.com/liketheflower/YONOD/update_readme/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_RGB_load_from/20220211_002440.log)|
-|SUN RGBD | INPUT B| swin-t|[basedRGBandDHSandRGBDHSmixed](https://drive.google.com/file/d/1cfIxRG4vumIAX3T1cem79gqUZcp1o1-n/view?usp=sharing)|[cfg](https://github.com/liketheflower/YONOD/blob/master/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_sunrgbd_rgb_dhs_mixed_with_chess1/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_sunrgbd_rgb_dhs_mixed_with_chess1.py)|54.2|55.8|58.1|[test_on_RGB](https://github.com/liketheflower/YONOD/blob/master/test_logs_unshow/rgb_dhs_mixed_with_chess1_on_rgb_on_val_chess1/test_swin_rgb_dhs_mixed_with_chess1_yonod_on_rgb_epoch_99.log) [test on RGB DHS mixed](https://raw.githubusercontent.com/liketheflower/YONOD/master/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_sunrgbd_rgb_dhs_mixed_with_chess1/20220510_174307.log)|
+|SUN RGBD | INPUT A| swin-t|[basedRGB](https://drive.google.com/file/d/1cfIxRG4vumIAX3T1cem79gqUZcp1o1-n/view?usp=sharing)|[cfg](https://github.com/liketheflower/UODDM/blob/master/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_RGB_load_from/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_RGB_load_from.py)|53.9|N/A|N/A|[log](https://raw.githubusercontent.com/liketheflower/UODDM/update_readme/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_RGB_load_from/20220211_002440.log)|
+|SUN RGBD | INPUT B| swin-t|[basedRGBandDHSandRGBDHSmixed](https://drive.google.com/file/d/1cfIxRG4vumIAX3T1cem79gqUZcp1o1-n/view?usp=sharing)|[cfg](https://github.com/liketheflower/UODDM/blob/master/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_sunrgbd_rgb_dhs_mixed_with_chess1/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_sunrgbd_rgb_dhs_mixed_with_chess1.py)|54.2|55.8|58.1|[test_on_RGB](https://github.com/liketheflower/UODDM/blob/master/test_logs_unshow/rgb_dhs_mixed_with_chess1_on_rgb_on_val_chess1/test_swin_rgb_dhs_mixed_with_chess1_yonod_on_rgb_epoch_99.log) [test on RGB DHS mixed](https://raw.githubusercontent.com/liketheflower/UODDM/master/work_dirs/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_sunrgbd_rgb_dhs_mixed_with_chess1/20220510_174307.log)|
 
 ## Usage ##
 ### Finetune based on COCO for SUNRGBD ### 
 The sun rgbd dataset training and test can be found in the sunrgbd folder, if you want to train the sunrgbd dataset based on pretrained model on COCO, please do the following:
 ```bash
 cd sunrgbd
-./shell_script/yonod/train_swin_transform.sh
+./shell_script/uoddm/train_swin_transform.sh
 ```
 You need download a pretrained model from the COCO dataset and you can find the models in the MODEL session.
 If you want to train a RGB image, please use:# train RGB with pretrained weights from coco for 100 epochs"
@@ -46,7 +46,7 @@ https://github.com/liketheflower/mmdetection_beta
 ### Inference ###
 Run the following shell script:
 ```bash
-./sunrgbd/shell_script/yonod/inference/inference.sh
+./sunrgbd/shell_script/uoddm/inference/inference.sh
 ```
 ### Installation ##
 Please refer to [get_started.md](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/get_started.md) for installation.