* Under Repairing
This is pytorch implementation code for realizing the technical part of CAusal Unsupervised Semantic sEgmentation (CAUSE) to improve performance of unsupervised semantic segmentation.
This code is further developed by two baseline codes of HP: Leveraging Hidden Positives for Unsupervised Semantic Segmentation accepted in CVPR 2023
and STEGO: Unsupervised Semantic Segmentation by Distilling Feature Correspondences accepted in ICLR 2022 .
You can see the following bundle of images in Appendix.
Further, we explain concrete implementation beyond the description of the main paper.
Figure 1. Visual comparison of USS for COCO-stuff. Note that, in contrast to
true labels, baseline frameworks fail to
achieve targeted level of granularity, while CAUSE successfully clusters person, sports, vehicle, etc.
Figure 2. Qualitative comparison of unsupervised semantic segmentation for Cityscapes.
Figure 3. Log scale of mIoU results for each categories in COCO-Stuff (Black: Thing / Gray: Stuff )
---
## 🚀 Download Visual Quality, Seg Head Parameter, and Concept ClusterBook of CAUSE
You can download the checkpoint files including CAUSE-trained parameters based on
[DINO](https://openaccess.thecvf.com/content/ICCV2021/papers/Caron_Emerging_Properties_in_Self-Supervised_Vision_Transformers_ICCV_2021_paper.pdf), [DINOv2](https://arxiv.org/pdf/2304.07193.pdf), [iBOT](https://openreview.net/pdf?id=ydopy-e6Dg), [MSN](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136910442.pdf), [MAE](https://openaccess.thecvf.com/content/CVPR2022/papers/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.pdf)
in self-supervised vision transformer framework.
If you want to download the pretrained models of DINO in various structures the following CAUSE uses,
you can download them in the following links:
* [DINO](https://github.com/facebookresearch/dino), ICCV 2021
* [DINOv2](https://github.com/facebookresearch/dinov2), ArXiv 2023
* [iBOT](https://github.com/bytedance/ibot), ICLR 2022
* [MSN](https://github.com/facebookresearch/msn), ECCV 2022
* [MAE](https://github.com/facebookresearch/mae), CVPR 2022
---
| Dataset | Method | Baseline | mIoU(%) | pAcc(%) | Visual Quality | Seg Head Parameter | Concept ClusterBook |
|:------------|---------------|------------|:-------:|:-------:|:---------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
| COCO-Stuff | DINO+**CAUSE-MLP** | ViT-S/8 | 27.9 | 66.8 | [[link]](https://drive.google.com/file/d/1Z0Zj9JWJQQk6qeRctcdAk9MfyZQCwkvW/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1ByLMYly-lLAa4vBQZ8Sv8nLSWBLPbev-?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/14bq-B4Xj4V3Usl2b2SfobCOaap4lzIXl?usp=drive_link) |
| COCO-Stuff | DINO+**CAUSE-TR** | ViT-S/8 | 32.4 | 69.6 | [[link]](https://drive.google.com/file/d/1x9LNwCiXtZel-fTh8TqtRgHmmmrIFPgg/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1ByLMYly-lLAa4vBQZ8Sv8nLSWBLPbev-?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/14bq-B4Xj4V3Usl2b2SfobCOaap4lzIXl?usp=drive_link) |
| COCO-Stuff | DINO+**CAUSE-MLP** | ViT-S/16 | 25.9 | 66.3 | [[link]](https://drive.google.com/file/d/1wcMomwarw5gQ3sSSmQlZICtP4r3kZMN8/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1PfOHDxWF_YcPVOApUSK-xHDUSY32domZ?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1GnVOgtOZdt8N7M6cudd5d59FqZAQDDG5?usp=drive_link)|
| COCO-Stuff | DINO+**CAUSE-TR** | ViT-S/16 | 33.1 | 70.4 | [[link]](https://drive.google.com/file/d/198_-3BvN_GCI63_Mx4lEPCHl0L9Fk2p2/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1PfOHDxWF_YcPVOApUSK-xHDUSY32domZ?usp=drive_link) |[[link]](https://drive.google.com/drive/folders/1GnVOgtOZdt8N7M6cudd5d59FqZAQDDG5?usp=drive_link) |
| COCO-Stuff | DINO+**CAUSE-MLP** | ViT-B/8 | 34.3 | 72.8 | [[link]](https://drive.google.com/file/d/1fmUs3UOsWVhOXvcbxjG9c-VT2vEaVzte/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1Og2U2ihbPBrxpAAeWuped_FH4u_ecb0P?usp=drive_link) |[[link]](https://drive.google.com/drive/folders/10bZecU1EzgOISoi0RkajqSR-ebfAWr_N?usp=drive_link) |
| COCO-Stuff | DINO+**CAUSE-TR** | ViT-B/8 | 41.9 | 74.9 | [[link]](https://drive.google.com/file/d/107jUAW4Y6xCMB7AgtgMIFcBitLMmQHaT/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1Og2U2ihbPBrxpAAeWuped_FH4u_ecb0P?usp=drive_link) |[[link]](https://drive.google.com/drive/folders/10bZecU1EzgOISoi0RkajqSR-ebfAWr_N?usp=drive_link) |
| COCO-Stuff | DINOv2+**CAUSE-TR** | ViT-B/14| 45.3 | 78.0 | [[link]](https://drive.google.com/file/d/1e_Mub-u1EJOqzI7umk4BGgFApWukixmb/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1CWMussJAvGulg94lUrNn3EooILbtgIOb?usp=drive_link) |[[link]](https://drive.google.com/drive/folders/1nBjBJnucRiBYFiJHeOJUuE5e1YSoYKaf?usp=drive_link) |
| COCO-Stuff | iBOT+**CAUSE-TR** | ViT-B/16 | 39.5 | 73.8 | [[link]](https://drive.google.com/file/d/1px6M068h3TH4wAxhH9sHSKrMZqreL9z2/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1BAMopzQNU7cmiaCyFv73SBLj2F6gikuU?usp=drive_link) |[[link]](https://drive.google.com/drive/folders/1mbJdzpOrR-sjmAk0O1hnqzsStXDU3CL9?usp=drive_link) |
| COCO-Stuff | MSN+**CAUSE-TR** | ViT-S/16 | 34.1 | 72.1 | [[link]](https://drive.google.com/file/d/1R9KH3q9SxyitMzDGoKYQK4GkxuFMn7HQ/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/15F2aaVMbG40ISSXTL0f_UrVkX3UAFwZw?usp=drive_link) |[[link]](https://drive.google.com/drive/folders/19Mv7_5sM6e48eH80bAZSagXhO9CfEbCS?usp=drive_link) |
| COCO-Stuff | MAE+**CAUSE-TR** | ViT-B/16 | 21.5 | 59.1 | [[link]](https://drive.google.com/file/d/1_vwGG51DN5rJliDKUcc-9DKLbklroJw9/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1ubUbmSliqrN19v6Abqsb_djtWDbx9qbV?usp=drive_link) |[[link]](https://drive.google.com/drive/folders/1G91qCJx-Z3IpYFYLFAUyG1zqMMZOhQWx?usp=drive_link) |
---
| Dataset | Method | Baseline | mIoU(%) | pAcc(%) | Visual Quality | Seg Head Parameter | Concept ClusterBook |
|:------------|---------------|------------|:-------:|:-------:|:---------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
| Cityscapes | DINO+**CAUSE-MLP** | ViT-S/8 | 21.7 | 87.7 | [[link]](https://drive.google.com/file/d/1sC7OltZGfXCCyPhEaHJ596mczBUMGiEr/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1MT_HPyZvn09jEsvnlGci9ZLDB2e6h4PI?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1-ZfobyjlUGx5nltnBnjSzKzLQLTqcD_r?usp=drive_link) |
| Cityscapes | DINO+**CAUSE-TR** | ViT-S/8 | 24.6 | 89.4 | [[link]](https://drive.google.com/file/d/1HEk9DSFHV0i-9SNqCDtmKhQUcPhSsu2P/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1MT_HPyZvn09jEsvnlGci9ZLDB2e6h4PI?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1-ZfobyjlUGx5nltnBnjSzKzLQLTqcD_r?usp=drive_link) |
| Cityscapes | DINO+**CAUSE-MLP** | ViT-B/8 | 25.7 | 90.3 | [[link]](https://drive.google.com/file/d/1T4urliZtG-mJgjr1k-AczlC7c6EmWovP/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1Y7K3v_IUUn82rq5df6cQUagL_sNLZRdT?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1EoidRFHOT1w8LFNt2ws7C1BCkYdI4fw1?usp=drive_link) |
| Cityscapes | DINO+**CAUSE-TR** | ViT-B/8 | 28.0 | 90.8 | [[link]](https://drive.google.com/file/d/1hQUT8jmzj9StBF_3QN87SL2_HO5n9yxp/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1Y7K3v_IUUn82rq5df6cQUagL_sNLZRdT?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1EoidRFHOT1w8LFNt2ws7C1BCkYdI4fw1?usp=drive_link) |
| Cityscapes | DINOv2+**CAUSE-TR** | ViT-B/14 | 29.9 | 89.8 | [[link]](https://drive.google.com/file/d/1SUKv38yrayooAVsW2VWLbg6iy64syWnV/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1fi_DvMD3CLaZEozEgrGhIh6nH7WFq_Sj?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1t66yv8_otlAMwy-QQyff-6fiwP58kCvV?usp=drive_link) |
| Cityscapes | iBOT+**CAUSE-TR** | ViT-B/16 | 23.0 | 89.1 | [[link]](https://drive.google.com/file/d/1ZDCr0k6WdmjWFw6J-S7Y6HFf88tGfEAO/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1T9OqBTc9tw9h3zDzzi137l8ls29_uOrd?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1t4qsGMYlIWoArvkAFr-uUerkwQYPR5u7?usp=drive_link) |
| Cityscapes | MSN+**CAUSE-TR** | ViT-S/16 | 21.2 | 89.1 | [[link]](https://drive.google.com/file/d/1-jSkmwRObBKOHdiMuu3eLaXWgQFMeida/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1UQnhVADQvbnQKLjIXzEpY_hW76Yeeuuj?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1TLGaZjljYoVCFp4EjjOghtvczk-SzVR1?usp=drive_link) |
| Cityscapes | MAE+**CAUSE-TR** | ViT-B/16 | 12.5 | 82.0 | [[link]](https://drive.google.com/file/d/1241UvEi0zc5JS88fga2rZCS4wkDuaE3c/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1Ng9mVhzAipmY5aPzJkX35flqJIQ8rgIp?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1SFYWWo5Khqoy8fIhvxuL2XoZEaKt1UH-?usp=drive_link) |
---
| Dataset | Method | Baseline | mIoU(%) | pAcc(%) | Visual Quality | Seg Head Parameter | Concept ClusterBook |
|:------------|---------------|------------|:-------:|:-------:|:---------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
| Pascal VOC | DINO+**CAUSE-MLP** | ViT-S/8 | 46.0 | - | [[link]](https://drive.google.com/file/d/1nzZMGCqb7mYdSXN59xzMkQUitzjxJt-9/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1o6AkLzqC1J-V4YB_S7BBhfGd2E6qdopO?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1H9dvIDaEW1fsIKsI1HPETD4NC2dj6Z6S?usp=drive_link) |
| Pascal VOC | DINO+**CAUSE-TR** | ViT-S/8 | 50.0 | - | [[link]](https://drive.google.com/file/d/1Q-2ey069mDHnziGlP1olEc-JSHBf7t6N/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1o6AkLzqC1J-V4YB_S7BBhfGd2E6qdopO?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1H9dvIDaEW1fsIKsI1HPETD4NC2dj6Z6S?usp=drive_link) |
| Pascal VOC | DINO+**CAUSE-MLP** | ViT-B/8 | 47.9 | - | [[link]](https://drive.google.com/file/d/1EWlKNbcWGSNXBhZpdezv3ghCcxItR-Zj/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1sPlG9jQ-DljVguPNPDS1g3xnW_rTtUyw?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1zsTx1NOECcJ7DH1wa654wRH_NLV6FHWP?usp=drive_link) |
| Pascal VOC | DINO+**CAUSE-TR** | ViT-B/8 | 53.3 | - | [[link]](https://drive.google.com/file/d/1pqJNoCpCz3wMMjIMxQJ-WOxtdwvsaJWM/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1sPlG9jQ-DljVguPNPDS1g3xnW_rTtUyw?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1zsTx1NOECcJ7DH1wa654wRH_NLV6FHWP?usp=drive_link) |
| Pascal VOC | DINOv2+**CAUSE-TR** | ViT-B/14 | 53.2 | 91.5 | [[link]](https://drive.google.com/file/d/17FBfHfyML6jyeY5NvPJXUDI_vaxC87vk/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1qsuKMVvpqsaYcVvZj3rDecmhZBAHOTeK?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1aGs3KSExQrdQytVFWigOGRs2yC3YLO12?usp=drive_link) |
| Pascal VOC | iBOT+**CAUSE-TR** | ViT-B/16 | 53.4 | 89.6 | [[link]](https://drive.google.com/file/d/1UjkvZ0MFxL-P0kaeUQKSGnVrjPsaeHaY/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1G9zvKcLNbhAyqKlJXUpt80CR6MBtuSdi?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1zA9d5eo41GerRWuBOnHY6_AjhtjGETCy?usp=drive_link) |
| Pascal VOC | MSN+**CAUSE-TR** | ViT-S/16 | 30.2 | 84.2 | [[link]](https://drive.google.com/file/d/1by4USHNiEzem17s7jWKKUZTfUylQWyIy/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1s6nzSmzt_ZTt_tCDvf8vmhU2RmE3Cdy0?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1bwLogZo3vJOJrpSanRpgZ_1WHTeB3e4q?usp=drive_link) |
| Pascal VOC | MAE+**CAUSE-TR** | ViT-B/16 | 25.8 | 83.7 | [[link]](https://drive.google.com/file/d/1odjO5dgTTdmsWGuGG7xFPi3ZLsV6-stl/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1Re_f8QgIdXDnrNwP_5g-kFL-6SoE9YPU?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1wOVWzTnfH58My8PT8rXW0sw2oVdc4xkL?usp=drive_link) |
---
| Dataset | Method | Baseline | mIoU(%) | pAcc(%) | Visual Quality | Seg Head Parameter | Concept ClusterBook |
|:------------|---------------|------------|:-------:|:-------:|:---------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
| COCO-81 | DINO+**CAUSE-MLP** | ViT-S/8 | 19.1 | 78.8 | [[link]](https://drive.google.com/file/d/1Glxb7DHHhjxPQjkGygQM2prak7oH8dTt/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1SlQ1_3phGBvjaxizjcYD92Nglab7RV6k?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1ON5vDLS_Wc5OGgTxVK_yFopQGHbKaOar?usp=drive_link) |
| COCO-81 | DINO+**CAUSE-TR** | ViT-S/8 | 21.2 | 75.2 | [[link]](https://drive.google.com/file/d/1QJmkV57mhKx6_A0E-yQcrQifX8lMRspO/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1SlQ1_3phGBvjaxizjcYD92Nglab7RV6k?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1ON5vDLS_Wc5OGgTxVK_yFopQGHbKaOar?usp=drive_link) |
| COCO-171 | DINO+**CAUSE-MLP** | ViT-S/8 | 10.6 | 44.9 | [[link]](https://drive.google.com/file/d/1EUDqFTHVlr2c8cIR9oTbjpS83Js6RW66/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1SlQ1_3phGBvjaxizjcYD92Nglab7RV6k?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1ON5vDLS_Wc5OGgTxVK_yFopQGHbKaOar?usp=drive_link) |
| COCO-171 | DINO+**CAUSE-TR** | ViT-S/8 | 15.2 | 46.6 | [[link]](https://drive.google.com/file/d/1Gv6306XUb-rbWB980O5m5vxQeZKIModT/view?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1SlQ1_3phGBvjaxizjcYD92Nglab7RV6k?usp=drive_link) | [[link]](https://drive.google.com/drive/folders/1ON5vDLS_Wc5OGgTxVK_yFopQGHbKaOar?usp=drive_link) |
---
## 🤖 CAUSE Framework (Top-Level File Directory Layout)
.
├── loader
│ ├── netloader.py # Self-Supervised Pretrained Model Loader & Segmentation Head Loader
│ └── dataloader.py # Dataloader Thanks to STEGO [ICLR 2022]
│
├── models # Model Design of Self-Supervised Pretrained: [DINO/DINOv2/iBOT/MAE/MSN]
│ ├── dinomaevit.py # ViT Structure of DINO and MAE
│ ├── dinov2vit.py # ViT Structure of DINOv2
│ ├── ibotvit.py # ViT Structure of iBOT
│ └── msnvit.py # ViT Structure of MSN
│
├── modules # Segmentation Head and Its Necessary Function
│ └── segment_module.py # [Including Tools with Generating Concept Book and Contrastive Learning
│ └── segment.py # [MLP & TR] Including Tools with Generating Concept Book and Contrastive Learning
│
├── utils
│ └── utils.py # Utility for auxiliary tools
│
├── train_modularity.py # (STEP 1) [MLP & TR] Generating Concept Cluster Book as a Mediator
│
├── train_front_door_mlp.py # (STEP 2) [MLP] Frontdoor Adjustment through Unsupervised Semantic Segmentation
├── fine_tuning_mlp.py # (STEP 3) [MLP] Fine-Tuning Cluster Probe
│
├── train_front_door_tr.py # (STEP 2) [TR] Frontdoor Adjustment through Unsupervised Semantic Segmentation
├── fine_tuning_tr.py # (STEP 3) [TR] Fine-Tuning Cluster Probe
│
├── test_mlp.py # [MLP] Evaluating Unsupervised Semantic Segmantation Performance (Post-Processing)
├── test_tr.py # [TR] Evaluating Unsupervised Semantic Segmantation Performance (Post-Processing)
│
├── requirements.txt
└── README.md
---
## 📊 How to Run CAUSE?
For the first, we should generate the cropped dataset by following [STEGO](https://github.com/mhamilton723/STEGO) in ICLR 2022.
```shell script
python crop_dataset.py --dataset "cocostuff27" --crop_type "five"
python crop_dataset.py --dataset "cityscapes" --crop_type "five"
python crop_dataset.py --dataset "pascalvoc" --crop_type "super"
python crop_dataset.py --dataset "cooc81" --crop_type "double"
python crop_dataset.py --dataset "cooc171" --crop_type "double"
```
And then,
```shell bash
bash run # All of the following three steps integrated
```
In this shell script file, you can see the following code
```shell script
#!/bin/bash
######################################
# [OPTION] DATASET
# cocostuff27
dataset="cocostuff27"
#############
######################################
# [OPTION] STRUCTURE
structure="TR"
######################################
######################################
# [OPTION] Self-Supervised Method
ckpt="checkpoint/dino_vit_base_8.pth"
######################################
######################################
# GPU and PORT
if [ "$structure" = "MLP" ]
then
train_gpu="0,1,2,3"
elif [ "$structure" = "TR" ]
then
train_gpu="4,5,6,7"
fi
# Non-Changeable Variable
test_gpu="${train_gpu:0}"
port=$(($RANDOM%800+1200))
######################################
######################################
# [STEP1] MEDIATOR
python train_mediator.py --dataset $dataset --ckpt $ckpt --gpu $train_gpu --port $port
######################################
######################################
# [STEP2] CAUSE
if [ "$structure" = "MLP" ]
then
python train_front_door_mlp.py --dataset $dataset --ckpt $ckpt --gpu $train_gpu --port $port
python fine_tuning_mlp.py --dataset $dataset --ckpt $ckpt --gpu $train_gpu --port $port
elif [ "$structure" = "TR" ]
then
python train_front_door_tr.py --dataset $dataset --ckpt $ckpt --gpu $train_gpu --port $port
python fine_tuning_tr.py --dataset $dataset --ckpt $ckpt --gpu $train_gpu --port $port
fi
######################################
######################################
# TEST
if [ "$structure" = "MLP" ]
then
python test_mlp.py --dataset $dataset --ckpt $ckpt --gpu $test_gpu
elif [ "$structure" = "TR" ]
then
python test_tr.py --dataset $dataset --ckpt $ckpt --gpu $test_gpu
fi
######################################
```
### 1. Training CAUSE
### (STEP 1): Generating Mediator based on Modularity
```shell script
python train_mediator.py # DINO/DINOv2/iBOT/MSN/MAE
```
### (STEP 2): Frontdoor Adjustment through Contrastive Learning
```shell script
python train_front_door_mlp.py # CAUSE-MLP
# or
python train_front_door_tr.py # CAUSE-TR
```
### (STEP 3): *Technical STEP: Fine-Tuning Cluster Probe*
```shell script
python fine_tuning_mlp.py # CAUSE-MLP
# or
python fine_tuning_tr.py # CAUSE-TR
```
---
### 2. Testing CAUSE
```shell script
python test_mlp.py # CAUSE-MLP
# or
python test_tr.py # CAUSE-TR
```
---
## 💡 Environment Settings
* Creating Virtual Environment by Anaconda
> conda create -y -n neurips python=3.9
* Installing [PyTorch]((https://pytorch.org/)) Package in Virtual Envrionment
> pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
* Installing Pip Package
> pip install -r requirements.txt
* [Optional] Removing Conda and PIP Cache if Conda and PIP have been locked by unknown reasons
> conda clean -a && pip cache purge
---
## 🍅 Download Datasets
### Available Datasets
* [COCO-Stuff](https://paperswithcode.com/dataset/coco-stuff)
* [Cityscapes](https://paperswithcode.com/dataset/cityscapes)
* [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html)
*Note: Pascal VOC is not necessary to download because dataloader will automatically download in your own dataset path*
### Try the following scripts
> * wget https://marhamilresearch4.blob.core.windows.net/stego-public/pytorch_data/cityscapes.zip
> * wget https://marhamilresearch4.blob.core.windows.net/stego-public/pytorch_data/cocostuff.zip
### If the above do not work, then download [azcopy](https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10?toc=%2Fazure%2Fstorage%2Fblobs%2Ftoc.json&bc=%2Fazure%2Fstorage%2Fblobs%2Fbreadcrumb%2Ftoc.json) and follow the below scripts
> * azcopy copy "https://marhamilresearch4.blob.core.windows.net/stego-public/pytorch_data/cityscapes.zip" "custom_path" --recursive
> * azcopy copy "https://marhamilresearch4.blob.core.windows.net/stego-public/pytorch_data/cocostuff.zip" "custom_path" --recursive
### Unzip Datasets
```shell script
unzip cocostuff.zip && unzip cityscapes.zip
```
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