This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick."


A central challenge for the task of semantic segmentation is the prohibitive cost of obtaining dense pixel-level annotations to supervise model training. In this work, we show that in order to achieve a good level of segmentation performance, all you need are a few well-chosen pixel labels. We make the following contributions: (i) We investigate the novel semantic segmentation setting in which labels are supplied only at sparse pixel locations, and show that deep neural networks can use a handful of such labels to good effect; (ii) We demonstrate how to exploit this phenomena within an active learning framework, termed PixelPick, to radically reduce labelling cost, and propose an efficient “mouse-free” annotation strategy to implement our approach; (iii) We conduct extensive experiments to study the influence of annotation diversity under a fixed budget, model pretraining, model capacity and the sampling mechanism for picking pixels in this low annotation regime; (iv) We provide comparisons to the existing state of the art in semantic segmentation with active learning, and demonstrate comparable performance with up to two orders of magnitude fewer pixel annotations on the CamVid, Cityscapes and PASCAL VOC 2012 benchmarks; (v) Finally, we evaluate the efficiency of our annotation pipeline and its sensitivity to annotator error to demonstrate its practicality. Our code, models and annotation tool will be made publicly available.



Our code is based on Python 3.8 and uses the following Python packages.

Clone this repository
git clone https://github.com/NoelShin/PixelPick.git
cd PixelPick
Download dataset

Follow one of the instructions below to download a dataset you are interest in. Then, set the dir_dataset variable in args.py to the directory path which contains the downloaded dataset.

  • For CamVid, you need to download SegNet-Tutorial codebase as a zip file and use CamVid directory which contains images/annotations for training and test after unzipping it. You don't need to change the directory structure. [CamVid]

  • For Cityscapes, first visit the link and login to download. Once downloaded, you need to unzip it. You don't need to change the directory structure. It is worth noting that, if you set downsample variable in args.py (4 by default), it will first downsample train and val images of Cityscapes and store them within {dir_dataset}_d{downsample} folder which will be located in the same directory of dir_dataset. This is to enable a faster dataloading during training. [Cityscapes]

  • For PASCAL VOC 2012, the dataset will be automatically downloaded via torchvision.datasets.VOCSegmentation. You just need to specify which directory you want to download it with dir_dataset variable. If the automatic download fails, you can manually download through the following page (you don't need to untar VOCtrainval_11-May-2012.tar file which will be downloaded). [PASCAL VOC 2012 segmentation]

For more details about the data we used to train/validate our model, please visit datasets directory and find {camvid, cityscapes, voc}_{train, val}.txt file.

Train and validate

By default, the current code validates the model every epoch while training. To train a MobileNetv2-based DeepLabv3+ network, follow the below lines. (The pretrained MobileNetv2 will be loaded automatically.)

cd scripts
sh pixelpick-dl-cv.sh

Benchmark results

For CamVid and Cityscapes, we report the average of 5 different runs and 3 different runs for PASCAL VOC 2012. Please refer to our paper for details. ± one std of mean IoU is denoted.

model backbone (encoder) # labelled pixels per img (% annotation) mean IoU (%)
PixelPick MobileNetv2 20 (0.012) 50.8 ± 0.2
PixelPick MobileNetv2 40 (0.023) 53.9 ± 0.7
PixelPick MobileNetv2 60 (0.035) 55.3 ± 0.5
PixelPick MobileNetv2 80 (0.046) 55.2 ± 0.7
PixelPick MobileNetv2 100 (0.058) 55.9 ± 0.1
Fully-supervised MobileNetv2 360x480 (100) 58.2 ± 0.6
PixelPick ResNet50 20 (0.012) 59.7 ± 0.9
PixelPick ResNet50 40 (0.023) 62.3 ± 0.5
PixelPick ResNet50 60 (0.035) 64.0 ± 0.3
PixelPick ResNet50 80 (0.046) 64.4 ± 0.6
PixelPick ResNet50 100 (0.058) 65.1 ± 0.3
Fully-supervised ResNet50 360x480 (100) 67.8 ± 0.3

Note that to make training time manageable, we train on the quarter resolution (256x512) of the original Cityscapes images (1024x2048).

model backbone (encoder) # labelled pixels per img (% annotation) mean IoU (%)
PixelPick MobileNetv2 20 (0.015) 52.0 ± 0.6
PixelPick MobileNetv2 40 (0.031) 54.7 ± 0.4
PixelPick MobileNetv2 60 (0.046) 55.5 ± 0.6
PixelPick MobileNetv2 80 (0.061) 56.1 ± 0.3
PixelPick MobileNetv2 100 (0.076) 56.5 ± 0.3
Fully-supervised MobileNetv2 256x512 (100) 61.4 ± 0.5
PixelPick ResNet50 20 (0.015) 56.1 ± 0.4
PixelPick ResNet50 40 (0.031) 60.0 ± 0.3
PixelPick ResNet50 60 (0.046) 61.6 ± 0.4
PixelPick ResNet50 80 (0.061) 62.3 ± 0.4
PixelPick ResNet50 100 (0.076) 62.8 ± 0.4
Fully-supervised ResNet50 256x512 (100) 68.5 ± 0.3
model backbone (encoder) # labelled pixels per img (% annotation) mean IoU (%)
PixelPick MobileNetv2 10 (0.009) 51.7 ± 0.2
PixelPick MobileNetv2 20 (0.017) 53.9 ± 0.8
PixelPick MobileNetv2 30 (0.026) 56.7 ± 0.3
PixelPick MobileNetv2 40 (0.034) 56.9 ± 0.7
PixelPick MobileNetv2 50 (0.043) 57.2 ± 0.3
Fully-supervised MobileNetv2 N/A (100) 57.9 ± 0.5
PixelPick ResNet50 10 (0.009) 59.7 ± 0.8
PixelPick ResNet50 20 (0.017) 65.6 ± 0.5
PixelPick ResNet50 30 (0.026) 66.4 ± 0.2
PixelPick ResNet50 40 (0.034) 67.2 ± 0.1
PixelPick ResNet50 50 (0.043) 67.4 ± 0.5
Fully-supervised ResNet50 N/A (100) 69.4 ± 0.3


model dataset backbone (encoder) # labelled pixels per img (% annotation) mean IoU (%) Download
PixelPick CamVid MobileNetv2 100 (0.058) 56.1 Link
PixelPick CamVid ResNet50 100 (0.058) TBU TBU
PixelPick Cityscapes MobileNetv2 100 (0.076) 56.8 Link
PixelPick Cityscapes ResNet50 100 (0.076) 63.3 Link
PixelPick VOC 2012 MobileNetv2 50 (0.043) 57.4 Link
PixelPick VOC 2012 ResNet50 50 (0.043) 68.0 Link

PixelPick mouse-free annotation tool


We are currently working on integrating PixelPick annotation tool into VGG Image Annotator (VIA) which offers much better GUI (and degree of freedom in terms of file formats) than our current python-based version. However, for those who are interested in trying the current version, we leave a sample script for annotating the CamVid training images.

To implement the script, only two things are required to do:

(1) Go to annotation_tool/launch_gui.py file. You should be able to see

dataset_to_paths = {
    "camvid": {
        "dir_imgs": "{PATH_TO_CAMVID_DIR}/train",  # Directory containing the images
        "dir_gts": "{PATH_TO_CAMVID_DIR}/trainannot",  # Directory containing the groundtruth labels
        "path_query": "../query.npy"  # Path to the query file

Then, you need to replace {PATH_TO_CAMVID_DIR} with your directory which contains CamVid dataset. An example query.npy file can be found in the annotation_tool directory (you don't need to move this file).

(2) Then move to annotation_tool/scripts and launch the GUI by

cd annotation_tool/scripts
sh cv-train.sh

Then, the annotation tool will be launched.

It is worth noting that

  1. By default, the number of images that will be annotated is set to 10. However, you can change this value by setting a different value (-1 for all images).

  2. Your annotation will be stored in annotation_tool/logs/camvid_* directory as txt files.


To be updated.


We borrowed code for the MobileNetv2-based DeepLabv3+ network from https://github.com/Shuai-Xie/DEAL.

If you have any questions, please contact us at {gyungin, weidi, samuel}@robots.ox.ac.uk.