Set of models for classifcation of 3D volumes

Overview

Classification models 3D Zoo - Keras and TF.Keras

This repository contains 3D variants of popular CNN models for classification like ResNets, DenseNets, VGG, etc. It also contains weights obtained by converting ImageNet weights from the same 2D models.

This repository is based on great classification_models repo by @qubvel

Architectures:

Installation

pip install classification-models-3D

Examples

Loading model with imagenet weights:
# for keras
from classification_models_3D.keras import Classifiers

# for tensorflow.keras
# from classification_models_3D.tfkeras import Classifiers

ResNet18, preprocess_input = Classifiers.get('resnet18')
model = ResNet18(input_shape=(128, 128, 128, 3), weights='imagenet')

All possible nets for Classifiers.get() method: 'resnet18, 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'seresnet18', 'seresnet34', 'seresnet50', 'seresnet101', 'seresnet152', 'seresnext50', 'seresnext101', 'senet154', 'resnext50', 'resnext101', 'vgg16', 'vgg19', 'densenet121', 'densenet169', 'densenet201', 'inceptionresnetv2', 'inceptionv3', 'mobilenet', 'mobilenetv2'

Convert imagenet weights (2D -> 3D)

Code to convert 2D imagenet weights to 3D variant is available here: convert_imagenet_weights_to_3D_models.py. Weights were obtained with TF2, but works OK with Keras + TF1 as well.

How to choose input shape

If initial 2D model had shape (512, 512, 3) then you can use shape (D, H, W, 3) where D * H * W ~= 512*512, so something like (64, 64, 64, 3) will be ok.

Training with single NVIDIA 1080Ti (11 GB) worked with:

  • DenseNet121, DenseNet169 and ResNet50 with shape (96, 128, 128, 3) and batch size 6
  • DenseNet201 with shape (96, 128, 128, 3) and batch size 5
  • ResNet18 with shape (128, 160, 160, 3) and batch size 6

Related repositories

Unresolved problems

  • There is no DepthwiseConv3D layer in keras, so repo used custom layer from this repo by @alexandrosstergiou which can be slower than native implementation.
  • There is no imagenet weights for 'inceptionresnetv2' and 'inceptionv3'.

Description

This code was used to get 1st place in DrivenData: Advance Alzheimer’s Research with Stall Catchers competition.

More details on ArXiv: https://arxiv.org/abs/2104.01687

Citation

If you find this code useful, please cite it as:

@InProceedings{RSolovyev_2021_stalled,
  author = {Solovyev, Roman and Kalinin, Alexandr A. and Gabruseva, Tatiana},
  title = {3D Convolutional Neural Networks for Stalled Brain Capillary Detection},
  booktitle = {Arxiv: 2104.01687},
  month = {April},
  year = {2021}
}
Comments
  • Update __init__.py

    Update __init__.py

    Using keras 2.9.0, import keras_applications as ka gives the following error:- ModuleNotFoundError: No module named 'keras_applications'

    Instead using from keras import applications as ka works!

    opened by msmuskan 0
  • Pushing current version to PyPI

    Pushing current version to PyPI

    Hello @ZFTurbo,

    if you have time, please push the current updated status (with ConvNeXt) of this repo to PyPI. :)

    Thanks again for the great work and your time!

    Cheers, Dominik

    opened by muellerdo 0
  • Grad cam issue

    Grad cam issue

    Hello ,

    base_model, preprocess_input = Classifiers.get('seresnext50') model = base_model(input_shape=(512, 512, 20, 1 ), weights=None , include_top = False ) x = Flatten()(model.output) x = Dense(1024, activation= 'sigmoid')(x) x = Dense(2, activation= 'sigmoid')(x)

    Trying to train a model , the accuracy is everything resides upto expectation, but the gradcam are quite off from the region of the focus - how the accuracy is good but the grad cam is off the focus of targeted area .

    Using the layer - 'activation-161' as output ref - https://github.com/fitushar/3D-Grad-CAM/blob/master/3DGrad-CAM.ipynb for the gradcam generation code , the results are always at the border of the image.

    opened by ntirupathirao18 0
  • ImportError: cannot import name 'VersionAwareLayers' from 'keras.layers'

    ImportError: cannot import name 'VersionAwareLayers' from 'keras.layers'

    Thank you for the great work.

    I am experiencing the following error over and over, even though I created a brand new tensorflow environment and installed all the necessary libraries in it. Could you please have a look on it and guide me how do I solve this problem? Thank you.

    ImportError: Unable to import 'VersionAwareLayers' from 'keras.layers' (/home/ubuntu/anaconda3/envs/cm_3d/lib/python3.7/site-packages/keras/layers/init.py)

    opened by nasir3843 2
  • 3D DenseNet

    3D DenseNet

    Hello and sorry to bother you beforehand,

    I am currently conducting my master thesis project and I am trying to implement a 3D DenseNet-121 with knee MRIs as input data. While I was searching on how to implement a 3D version of the DenseNet I came across your repository and tried to change it for my application.

    I have some issues regarding my try and I didn't know where else to ask about it and again I am sorry if I am completely of topic asking them here.

    Firstly, my input shapes are (250,320,18,1) and when I give them as input to the 3D DenseNet I developed with stride_size=1 for my Conv_block and pooling_size=(2,2,2) and strides=(2,2,1) for my AveragePooling3D layer in the transition block, the model is constructed properly with the specific input_size, while when I am trying to load a DenseNet121 from classification_models_3d.tfkeras classifiers I am unable to construct it with input_shape(250,320,18,1), stride_size=1 and kernel_size=2. It gives as an error "Negative dimension size... for node pool4_pool/AvgPool3D". Is there a way to specifically define the strides for AvgPool3D layer in the transition block?

    And secondly, I was thinking to load the 3D weights to my 3D DenseNet 121, is there a folder in your repository where I can find your pre-trained weights on imagenet??

    Again thank you for having this repository publicly available and sorry if I am completely of topic asking such things here.

    I look forward for you answer, Kind regards, Anastasis

    opened by alexopoulosanastasis 4
  • What are the limitations on Inceptionv3 input shape?

    What are the limitations on Inceptionv3 input shape?

    I seem to always get this error when I try to create InceptionV3 model no matter what input_shape. What are the limitations on input shape there?

    InvalidArgumentError: Negative dimension size caused by subtracting 3 from 2 for '{{node conv3d_314/Conv3D}} = 
    Conv3D[T=DT_FLOAT, data_format="NDHWC", dilations=[1, 1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 2, 1]](Placeholder, 
    conv3d_314/Conv3D/ReadVariableOp)' with input shapes: [?,2,17,17,192], [3,3,3,192,320].
    
    opened by mazatov 0
Releases(v1.0.4)
AWS provides a Python SDK, "Boto3" ,which can be used to access the AWS-account from the local.

Boto3 - The AWS SDK for Python Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for Python, which allows Python developers to wri

Shreyas Srivastava 1 Oct 25, 2021
Fight Recognition from Still Images in the Wild @ WACVW2022, Real-world Surveillance Workshop

Fight Detection from Still Images in the Wild Detecting fights from still images is an important task required to limit the distribution of social med

Şeymanur Aktı 10 Nov 09, 2022
Open source Python module for computer vision

About PCV PCV is a pure Python library for computer vision based on the book "Programming Computer Vision with Python" by Jan Erik Solem. More details

Jan Erik Solem 1.9k Jan 06, 2023
PyDeepFakeDet is an integrated and scalable tool for Deepfake detection.

PyDeepFakeDet An integrated and scalable library for Deepfake detection research. Introduction PyDeepFakeDet is an integrated and scalable Deepfake de

Junke, Wang 49 Dec 11, 2022
Implementation of various Vision Transformers I found interesting

Implementation of various Vision Transformers I found interesting

Kim Seonghyeon 78 Dec 06, 2022
This porject is intented to build the most accurate model for predicting the porbability of loan default

Estimating-Loan-Default-Probability IBA ML2 Mid-project / Kaggle Competition This porject is intented to build the most accurate model for predicting

Adil Gahramanov 1 Jan 24, 2022
An open source Python package for plasma science that is under development

PlasmaPy PlasmaPy is an open source, community-developed Python 3.7+ package for plasma science. PlasmaPy intends to be for plasma science what Astrop

PlasmaPy 444 Jan 07, 2023
Serverless proxy for Spark cluster

Hydrosphere Mist Hydrosphere Mist is a serverless proxy for Spark cluster. Mist provides a new functional programming framework and deployment model f

hydrosphere.io 317 Dec 01, 2022
Chunkmogrify: Real image inversion via Segments

Chunkmogrify: Real image inversion via Segments Teaser video with live editing sessions can be found here This code demonstrates the ideas discussed i

David Futschik 112 Jan 04, 2023
using yolox+deepsort for object-tracker

YOLOX_deepsort_tracker yolox+deepsort实现目标跟踪 最新的yolox尝尝鲜~~(yolox正处在频繁更新阶段,因此直接链接yolox仓库作为子模块) Install Clone the repository recursively: git clone --rec

245 Dec 26, 2022
A collection of SOTA Image Classification Models in PyTorch

A collection of SOTA Image Classification Models in PyTorch

sithu3 85 Dec 30, 2022
Source code for paper "Deep Diffusion Models for Robust Channel Estimation", TBA.

diffusion-channels Source code for paper "Deep Diffusion Models for Robust Channel Estimation". Generic flow: Use 'matlab/main.mat' to generate traini

The University of Texas Computational Sensing and Imaging Lab 15 Dec 22, 2022
an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 985 Jan 08, 2023
CARL provides highly configurable contextual extensions to several well-known RL environments.

CARL (context adaptive RL) provides highly configurable contextual extensions to several well-known RL environments.

AutoML-Freiburg-Hannover 51 Dec 28, 2022
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022
YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)

Introduction Yolov5-face is a real-time,high accuracy face detection. Performance Single Scale Inference on VGA resolution(max side is equal to 640 an

DeepCam Shenzhen 1.4k Jan 07, 2023
A collection of easy-to-use, ready-to-use, interesting deep neural network models

Interesting and reproducible research works should be conserved. This repository wraps a collection of deep neural network models into a simple and un

Aria Ghora Prabono 16 Jun 16, 2022
A Tensorflow implementation of the Text Conditioned Auxiliary Classifier Generative Adversarial Network for Generating Images from text descriptions

A Tensorflow implementation of the Text Conditioned Auxiliary Classifier Generative Adversarial Network for Generating Images from text descriptions

Ayushman Dash 93 Aug 04, 2022
A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

P-tuning A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''. How to use our code We have released the code

THUDM 562 Dec 27, 2022
InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy

InferPy: Deep Probabilistic Modeling Made Easy InferPy is a high-level API for probabilistic modeling written in Python and capable of running on top

PGM-Lab 141 Oct 13, 2022