This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset.

Overview

DeepLab-ResNet-TensorFlow

This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset.

Updates

29 Jan, 2017:

  • Fixed the implementation of the batch normalisation layer: it now supports both the training and inference steps. If the flag --is-training is provided, the running means and variances will be updated; otherwise, they will be kept intact. The .ckpt files have been updated accordingly - to download please refer to the new link provided below.
  • Image summaries during the training process can now be seen using TensorBoard.
  • Fixed the evaluation procedure: the 'void' label (255) is now correctly ignored. As a result, the performance score on the validation set has increased to 80.1%.

Model Description

The DeepLab-ResNet is built on a fully convolutional variant of ResNet-101 with atrous (dilated) convolutions, atrous spatial pyramid pooling, and multi-scale inputs (not implemented here).

The model is trained on a mini-batch of images and corresponding ground truth masks with the softmax classifier at the top. During training, the masks are downsampled to match the size of the output from the network; during inference, to acquire the output of the same size as the input, bilinear upsampling is applied. The final segmentation mask is computed using argmax over the logits. Optionally, a fully-connected probabilistic graphical model, namely, CRF, can be applied to refine the final predictions. On the test set of PASCAL VOC, the model achieves 79.7% of mean intersection-over-union.

For more details on the underlying model please refer to the following paper:

@article{CP2016Deeplab,
  title={DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs},
  author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L Yuille},
  journal={arXiv:1606.00915},
  year={2016}
}

Requirements

TensorFlow needs to be installed before running the scripts. TensorFlow 0.12 is supported; for TensorFlow 0.11 please refer to this branch.

To install the required python packages (except TensorFlow), run

pip install -r requirements.txt

or for a local installation

pip install -user -r requirements.txt

Caffe to TensorFlow conversion

To imitate the structure of the model, we have used .caffemodel files provided by the authors. The conversion has been performed using Caffe to TensorFlow with an additional configuration for atrous convolution and batch normalisation (since the batch normalisation provided by Caffe-tensorflow only supports inference). There is no need to perform the conversion yourself as you can download the already converted models - deeplab_resnet.ckpt (pre-trained) and deeplab_resnet_init.ckpt (the last layers are randomly initialised) - here.

Nevertheless, it is easy to perform the conversion manually, given that the appropriate .caffemodel file has been downloaded, and Caffe to TensorFlow dependencies have been installed. The Caffe model definition is provided in misc/deploy.prototxt. To extract weights from .caffemodel, run the following:

python convert.py /path/to/deploy/prototxt --caffemodel /path/to/caffemodel --data-output-path /where/to/save/numpy/weights

As a result of running the command above, the model weights will be stored in /where/to/save/numpy/weights. To convert them to the native TensorFlow format (.ckpt), simply execute:

python npy2ckpt.py /where/to/save/numpy/weights --save-dir=/where/to/save/ckpt/weights

Dataset and Training

To train the network, one can use the augmented PASCAL VOC 2012 dataset with 10582 images for training and 1449 images for validation.

The training script allows to monitor the progress in the optimisation process using TensorBoard's image summary. Besides that, one can also exploit random scaling of the inputs during training as a means for data augmentation. For example, to train the model from scratch with random scale turned on, simply run:

python train.py --random-scale

To see the documentation on each of the training settings run the following:

python train.py --help

An additional script, fine_tune.py, demonstrates how to train only the last layers of the network.

Evaluation

The single-scale model shows 80.1% mIoU on the Pascal VOC 2012 validation dataset. No post-processing step with CRF is applied.

The following command provides the description of each of the evaluation settings:

python evaluate.py --help

Inference

To perform inference over your own images, use the following command:

python inference.py /path/to/your/image /path/to/ckpt/file

This will run the forward pass and save the resulted mask with this colour map:

Missing features

At the moment, the post-processing step with CRF is not implemented. Besides that, multi-scale inputs are missing, as well. No weight regularisation is applied.

Other implementations

A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations

jaxdf - JAX-based Discretization Framework Overview | Example | Installation | Documentation ⚠️ This library is still in development. Breaking changes

UCL Biomedical Ultrasound Group 65 Dec 23, 2022
ML From Scratch

ML from Scratch MACHINE LEARNING TOPICS COVERED - FROM SCRATCH Linear Regression Logistic Regression K Means Clustering K Nearest Neighbours Decision

Tanishq Gautam 66 Nov 02, 2022
Contrastive Language-Image Pretraining

CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair

OpenAI 11.5k Jan 08, 2023
Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Deep Learning to Improve Breast

Li Shen 305 Jan 03, 2023
Source Code for AAAI 2022 paper "Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching"

Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching This repository is an official implementation of

HKUST-KnowComp 13 Sep 08, 2022
An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" in Pytorch.

GLOM An implementation of Geoffrey Hinton's paper "How to represent part-whole hierarchies in a neural network" for MNIST Dataset. To understand this

50 Oct 19, 2022
gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks.

gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks. It is built on top of the OpenAI G

Robin Henry 99 Dec 12, 2022
Minimal diffusion models - Minimal code and simple experiments to play with Denoising Diffusion Probabilistic Models (DDPMs)

Minimal code and simple experiments to play with Denoising Diffusion Probabilist

Rithesh Kumar 16 Oct 06, 2022
Lightwood is Legos for Machine Learning.

Lightwood is like Legos for Machine Learning. A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glu

MindsDB Inc 312 Jan 08, 2023
Cascaded Pyramid Network (CPN) based on Keras (Tensorflow backend)

ML2 Takehome Project Reimplementing the paper: Cascaded Pyramid Network for Multi-Person Pose Estimation Dataset The model uses the COCO dataset which

Vo Van Tu 1 Nov 22, 2021
This is the code for CVPR 2021 oral paper: Jigsaw Clustering for Unsupervised Visual Representation Learning

JigsawClustering Jigsaw Clustering for Unsupervised Visual Representation Learning Pengguang Chen, Shu Liu, Jiaya Jia Introduction This project provid

DV Lab 73 Sep 18, 2022
LAnguage Model Analysis

LAMA: LAnguage Model Analysis LAMA is a probe for analyzing the factual and commonsense knowledge contained in pretrained language models. The dataset

Meta Research 960 Jan 08, 2023
Multi-Modal Machine Learning toolkit based on PaddlePaddle.

简体中文 | English PaddleMM 简介 飞桨多模态学习工具包 PaddleMM 旨在于提供模态联合学习和跨模态学习算法模型库,为处理图片文本等多模态数据提供高效的解决方案,助力多模态学习应用落地。 近期更新 2022.1.5 发布 PaddleMM 初始版本 v1.0 特性 丰富的任务

njustkmg 520 Dec 28, 2022
Simulation environments for the CrazyFlie quadrotor: Used for Reinforcement Learning and Sim-to-Real Transfer

Phoenix-Drone-Simulation An OpenAI Gym environment based on PyBullet for learning to control the CrazyFlie quadrotor: Can be used for Reinforcement Le

Sven Gronauer 8 Dec 07, 2022
CATE: Computation-aware Neural Architecture Encoding with Transformers

CATE: Computation-aware Neural Architecture Encoding with Transformers Code for paper: CATE: Computation-aware Neural Architecture Encoding with Trans

16 Dec 27, 2022
Code for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"

Language Generation with Recurrent Generative Adversarial Networks without Pre-training Code for training and evaluation of the model from "Language G

Amir Bar 253 Sep 14, 2022
Awesome Transformers in Medical Imaging

This repo supplements our Survey on Transformers in Medical Imaging Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat,

Fahad Shamshad 666 Jan 06, 2023
Library for 8-bit optimizers and quantization routines.

bitsandbytes Bitsandbytes is a lightweight wrapper around CUDA custom functions, in particular 8-bit optimizers and quantization functions. Paper -- V

Facebook Research 687 Jan 04, 2023
A PyTorch implementation of DenseNet.

A PyTorch Implementation of DenseNet This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Conv

Brandon Amos 771 Dec 15, 2022
Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

MyTT Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! to Stock Market Financial Technical Analysis Python

dev 34 Dec 27, 2022