Update README

README.md

@@ -1,37 +1,56 @@
 # STEGO: Unsupervised Semantic Segmentation by Distilling Feature Correspondences
-
-Pytorch implementation of the STEGO unsupervised semantic segmentation system.
-[[Paper]](https://arxiv.org/abs/2203.08414) [[Video]](https://aka.ms/stego-video)
+### [Project Page](https://iclr.cc/virtual/2022/poster/6068) | [Paper](https://arxiv.org/abs/2203.08414) | [Video](https://aka.ms/stego-video) | [ICLR Session](https://iclr.cc/virtual/2022/poster/6068)
 
 
+[Mark Hamilton](https://mhamilton.net/),
+[Zhoutong Zhang](https://ztzhang.info/),
+[Bharath Hariharan](http://home.bharathh.info/),
+[Noah Snavely](https://www.cs.cornell.edu/~snavely/),
+[William T. Freeman](https://billf.mit.edu/about/bio)
+
+This is the official implementation of the paper "Unsupervised Semantic Segmentation by Distilling Feature Correspondences".
+
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/unsupervised-semantic-segmentation-by-2/unsupervised-semantic-segmentation-on)](https://paperswithcode.com/sota/unsupervised-semantic-segmentation-on?p=unsupervised-semantic-segmentation-by-2)\
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/unsupervised-semantic-segmentation-by-2/unsupervised-semantic-segmentation-on-coco-4)](https://paperswithcode.com/sota/unsupervised-semantic-segmentation-on-coco-4?p=unsupervised-semantic-segmentation-by-2) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/unsupervised-semantic-segmentation-by-2/unsupervised-semantic-segmentation-on-potsdam-1)](https://paperswithcode.com/sota/unsupervised-semantic-segmentation-on-potsdam-1?p=unsupervised-semantic-segmentation-by-2)
 
-![Architecture](results/figures/stego.svg)
 
-![Cocostuff results](results/figures/cocostuff27_results.jpg)
+[![Overview Video](https://marhamilresearch4.blob.core.windows.net/stego-public/graphics/STEGO%20Header%20video%20(2).jpg)](https://youtu.be/NPub4E4o8BA)
 
+## Contents
+<!--ts-->
+   * [Install](#install)
+   * [Evaluation](#evaluation)
+   * [Training](#training)
+      * [Bringing your own data](#bringing-your-own-data)
+   * [Understanding STEGO](#understanding-stego)
+      * [Unsupervised Semantic Segmentation](#unsupervised-semantic-segmentation)
+      * [Deep features connect objects across images](#deep-features-connect-objects-across-images)
+      * [The STEGO architecture](#the-stego-architecture)
+      * [Results](#results)
+   * [Citation](#citation)
+   * [Contact](#contact)
+<!--te-->
 
 ## Install
 
 #### Clone this repository:
-```
+```shell script
 git clone https://github.com/mhamilton723/STEGO.git
 cd STEGO
 ```
 
 #### Install Conda Environment
 Please visit the [Anaconda install page](https://docs.anaconda.com/anaconda/install/index.html) if you do not already have conda installed
 
-```
+```shell script
 conda env create -f environment.yml
 conda activate stego
 ```
 
 #### Download Pre-Trained Models
 
-```
+```shell script
 cd src
 python download_models.py
 ```
@@ -41,13 +60,13 @@ python download_models.py
 First, change the `pytorch_data_dir` variable to your 
 systems pytorch data directory where datasets are stored. 
 
-```
+```shell script
 python download_datasets.py
 ```
 
 Once downloaded please navigate to your pytorch data dir and unzip the resulting files:
 
-```
+```shell script
 cd /YOUR/PYTORCH/DATA/DIR
 unzip cocostuff.zip
 unzip cityscapes.zip
@@ -58,17 +77,94 @@ unzip potsdamraw.zip
 
 ## Evaluation
 
-From inside STEGO/src please run the following:
-```
+To evaluate our pretrained models please run the following in `STEGO/src`:
+```shell script
 python eval_segmentation.py
 ```
+One can change the evaluation parameters and model by editing [`STEGO/src/configs/eval_config.yml`](src/configs/eval_config.yml)
 
 ## Training
 
-From inside STEGO/src please run the following:
+To train STEGO from scratch, please first generate the KNN indices for the datasets of interest:
+
+```shell script
+python precompute_knns.py
 ```
+
+Then you can run the following in `STEGO/src`:
+```shell script
 python train_segmentation.py
 ```
+Hyperparameters can be adjusted in [`STEGO/src/configs/train_config.yml`](src/configs/train_config.yml)
+
+To monitor training with tensorboard run the following from `STEGO` directory:
+
+```shell script
+tensorboard --logdir logs
+```
+
+#### Bringing your own data
+
+To train STEGO on your own dataset please create a directory in your pytorch data root with the following structure:
+
+```
+dataset_name
+|── imgs
+|   ├── train
+|   |   |── unique_img_name_1.jpg
+|   |   └── unique_img_name_2.jpg
+|   └── val
+|       |── unique_img_name_3.jpg
+|       └── unique_img_name_4.jpg
+└── labels
+    ├── train
+    |   |── unique_img_name_1.png
+    |   └── unique_img_name_2.png
+    └── val
+        |── unique_img_name_3.png
+        └── unique_img_name_4.png
+```
+
+Next in [`STEGO/src/configs/train_config.yml`](src/configs/train_config.yml) set the following parameters:
+
+```yaml
+dataset_name: "directory"
+dir_dataset_name: "dataset_name"
+dir_dataset_n_classes: 5 # This is the number of object types to find
+```
+
+If you want to train with cropping to increase spatial resolution run our [cropping utility](src/crop_datasets.py).
+
+Finally, uncomment the custom dataset code and run `python precompute_knns.py`
+ from `STEGO\src` to generate the prerequisite KNN information for the custom dataset.
+
+You can now train on your custom dataset using:
+```shell script
+python train_segmentation.py
+```
+
+## Understanding STEGO
+
+#### Unsupervised semantic segmentation
+Real-world images can be cluttered with multiple objects making classification feel arbitrary. Furthermore, objects in the real world don't always fit in bounding boxes. Semantic segmentation methods aim to avoid these challenges by assigning each pixel of an image its own class label. Conventional semantic segmentation methods are notoriously difficult to train due to their dependence on densely labeled images, which can take 100x longer to create than bounding boxes or class annotations. This makes it hard to gather sizable and diverse datasets impossible in domains where humans don't know the structure a-priori. We sidestep these challenges by learning an ontology of objects with pixel-level semantic segmentation through only self-supervision.
+
+#### Deep features connect objects across images
+Self-supervised contrastive learning enables algorithms to learn intelligent representations for images without supervision. STEGO builds on this work by showing that representations from self-supervised visual transformers like  Caron et. al.’s  DINO are already aware of the relationships between objects. By computing the cosine similarity between image features, we can see that similar semantic regions such as grass, motorcycles, and sky are “linked” together by feature similarity.
+
+![Feature connection GIF](https://mhamilton.net/images/Picture3.gif)
+
+
+#### The STEGO architecture
+The STEGO unsupervised segmentation system learns by distilling correspondences between images into a set of class labels using a contrastive loss. In particular we aim to learn a segmentation that respects the induced correspondences between objects. To achieve this we train a shallow segmentation network on top of the DINO ViT backbone with three contrastive terms that distill connections between an image and itself, similar images, and random other images respectively. If two regions are strongly coupled by deep features we encourage them to share the same class.
+
+![Architecture](results/figures/stego.svg)
+
+#### Results
+
+We evaluate the STEGO algorithm on the CocoStuff, Cityscapes, and Potsdam semantic segmentation datasets. Because these methods see no labels, we use a Hungarian matching algorithm to find the best mapping between clusters and dataset classes. We find that STEGO is capable of segmenting complex and cluttered scenes with much higher spatial resolution and sensitivity than the prior art, [PiCIE](https://sites.google.com/view/picie-cvpr2021/home). This not only yields a substantial qualitative improvement, but also more than doubles the mean intersection over union (mIoU). For results on Cityscapes, and Potsdam see [our paper](https://arxiv.org/abs/2203.08414).
+
+![Cocostuff results](results/figures/cocostuff27_results.jpg)
+
 
 ## Citation
 
@@ -80,3 +176,7 @@ python train_segmentation.py
   year={2022}
 }
 ```
+
+## Contact
+
+For feedback, questions, or press inquiries please contact [Mark Hamilton](mailto:[email protected])

src/precompute_knns.py

@@ -37,10 +37,12 @@ def my_app(cfg: DictConfig) -> None:
     print(cfg.output_root)
 
     image_sets = ["val", "train"]
-    # dataset_names = ["cocostuff27", "cityscapes", "voc"]
-    dataset_names = ["directory"]
-    # crop_types = ["five", None]#, "random"]
-    crop_types = [None]
+    dataset_names = ["cocostuff27", "cityscapes", "potsdam"]
+    crop_types = ["five", None]
+
+    # Uncomment these lines to run on custom datasets
+    #dataset_names = ["directory"]
+    #crop_types = [None]
 
     res = 224
     n_batches = 16