Image classification for projects and researches

Overview

Python 3.7 Python 3.8 MIT License Coverage

KERAS CLASSIFY

Image classification for projects and researches

About The Project

Image classification is a commonly used problem in the experimental part of scientific papers and also frequently appears as part of the projects. With the desire to reduce time and effort, Keras Classify was created.

Getting Started

Installation

  1. Clone the repo: https://github.com/nguyentruonglau/keras-classify.git

  2. Install packages

    > python -m venv 
         
          
    > activate.bat (in scripts folder)
    > pip install -r requirements.txt
    
         

Todo List:

  • Cosine learning rate scheduler
  • Gradient-based Localization
  • Sota models
  • Synthetic data
  • Smart Resize
  • Support Python 3.X and Tf 2.X
  • Use imagaug for augmentation data
  • Use prefetching and multiprocessing to training.
  • Analysis Of Input Shape
  • Compiled using XLA, auto-clustering on GPU
  • Receiver operating characteristic

Quick Start

Analysis Of Input Shape

If your data has random input_shape, you don't know which input_shape to choose, the analysis program is the right choice for you. The algorithm is applied to analyze: Kernel Density Estimation.

Convert Data

From tensorflow 2.3.x already support auto fit_generator, however moving the data to npy file will make it easier to manage. The algorithm is applied to shuffle data: Random Permutation. Read more here.

Run: python convert/convert_npy.py

Training Model.

Design your model at model/models.py, we have made EfficientNetB0 the default. Adjust the appropriate hyperparameters and run: python train.py

Evaluate Model.

  • Statistics number of images per class after suffle on test data.

  • Provide model evalution indicators such as: Accuracy, Precesion, Recall, F1-Score and AUC (Area Under the Curve).

  • Plot training history of Accuracy, Loss, Receiver Operating Characteristic curve and Confusion Matrix.

Explainable AI.

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. "We propose a technique for producing 'visual explanations' for decisions from a large class of CNN-based models, making them more transparent" Ramprasaath R. Selvaraju ... Read more here.

Example Code

Use for projects

from keras.preprocessing.image import load_img, img_to_array
from keras.preprocessing.image import smart_resize
from tensorflow.keras.models import load_model
import tensorflow as tf
import numpy as np

#load pretrained model
model_path = 'data/output/model/val_accuracy_max.h5'
model = load_model(model_path)

#load data
img_path = 'images/images.jpg'
img = load_img(img_path)
img = img_to_array(img)
img = smart_resize(img, (72,72)) #resize to HxW
img = np.expand_dims(img, axis=0)

#prediction
y_pred = model.predict(img)
y_pred = np.argmax(y_pred, axis=1)

#see convert/output/label_decode.json
print(y_pred)

Smart resize (tf < 2.4.1)

from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image load_img
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import image_ops
import numpy as np

def smart_resize(img, new_size, interpolation='bilinear'):
    """Resize images to a target size without aspect ratio distortion.

    Arguments:
      img (3D array): image data
      new_size (tuple): HxW

    Returns:
      [3D array]: image after resize
    """
    # Get infor of the image
    height, width, _ = img.shape
    target_height, target_width = new_size

    crop_height = (width * target_height) // target_width
    crop_width = (height * target_width) // target_height

    # Set back to input height / width if crop_height / crop_width is not smaller.
    crop_height = np.min([height, crop_height])
    crop_width = np.min([width, crop_width])

    crop_box_hstart = (height - crop_height) // 2
    crop_box_wstart = (width - crop_width) // 2

    # Infor to resize image
    crop_box_start = array_ops.stack([crop_box_hstart, crop_box_wstart, 0])
    crop_box_size = array_ops.stack([crop_height, crop_width, -1])

    img = array_ops.slice(img, crop_box_start, crop_box_size)
    img = image_ops.resize_images_v2(
        images=img,
        size=new_size,
        method=interpolation)
    return img.numpy()

Contributor

  1. BS Nguyen Truong Lau ([email protected])
  2. PhD Thai Trung Hieu ([email protected])

License

Distributed under the MIT License. See LICENSE for more information.

You might also like...
An end-to-end PyTorch framework for image and video classification
An end-to-end PyTorch framework for image and video classification

What's New: March 2021: Added RegNetZ models November 2020: Vision Transformers now available, with training recipes! 2020-11-20: Classy Vision v0.5 R

Scripts for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation and a convolutional neural network (CNN) for image classification
Scripts for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation and a convolutional neural network (CNN) for image classification

About subwAI subwAI - a project for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation

PyTorch implementation of our method for adversarial attacks and defenses in hyperspectral image classification.
PyTorch implementation of our method for adversarial attacks and defenses in hyperspectral image classification.

Self-Attention Context Network for Hyperspectral Image Classification PyTorch implementation of our method for adversarial attacks and defenses in hyp

Code image classification of MNIST dataset using different architectures: simple linear NN, autoencoder, and highway network

Deep Learning for image classification pip install -r http://webia.lip6.fr/~baskiotisn/requirements-amal.txt Train an autoencoder python3 train_auto

A PyTorch Image-Classification With AlexNet And ResNet50.

PyTorch 图像分类 依赖库的下载与安装 在终端中执行 pip install -r -requirements.txt 完成项目依赖库的安装 使用方式 数据集的准备 STL10 数据集 下载:STL-10 Dataset 存储位置:将下载后的数据集中 train_X.bin,train_y.b

CNN Based Meta-Learning for Noisy Image Classification and Template Matching

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

Code of Classification Saliency-Based Rule for Visible and Infrared Image Fusion

CSF Code of Classification Saliency-Based Rule for Visible and Infrared Image Fusion Tips: For testing: CUDA_VISIBLE_DEVICES=0 python main.py For trai

A python-image-classification web application project, written in Python and served through the Flask Microframework
A python-image-classification web application project, written in Python and served through the Flask Microframework

A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.

All the essential resources and template code needed to understand and practice data structures and algorithms in python with few small projects to demonstrate their practical application.

Data Structures and Algorithms Python INDEX 1. Resources - Books Data Structures - Reema Thareja competitiveCoding Big-O Cheat Sheet DAA Syllabus Inte

Releases(v1.0.0)
Owner
Nguyễn Trường Lâu
AI Researcher at FPT Software
Nguyễn Trường Lâu
Code for our ACL 2021 paper "One2Set: Generating Diverse Keyphrases as a Set"

One2Set This repository contains the code for our ACL 2021 paper “One2Set: Generating Diverse Keyphrases as a Set”. Our implementation is built on the

Jiacheng Ye 63 Jan 05, 2023
AsymmetricGAN - Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

AsymmetricGAN for Image-to-Image Translation AsymmetricGAN Framework for Multi-Domain Image-to-Image Translation AsymmetricGAN Framework for Hand Gest

Hao Tang 42 Jan 15, 2022
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

Junction Tree Variational Autoencoder for Molecular Graph Generation Official implementation of our Junction Tree Variational Autoencoder https://arxi

Wengong Jin 418 Jan 07, 2023
Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks.

The Lottery Ticket Hypothesis for Pre-trained BERT Networks Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks. [NeurIPS

VITA 122 Dec 14, 2022
HyperCube: Implicit Field Representations of Voxelized 3D Models

HyperCube: Implicit Field Representations of Voxelized 3D Models Authors: Magdalena Proszewska, Marcin Mazur, Tomasz Trzcinski, Przemysław Spurek [Pap

Magdalena Proszewska 3 Mar 09, 2022
PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation This is the PyTorch implemention of ICCV'21 paper SGPA: Structure

Chen Kai 24 Dec 05, 2022
The official PyTorch code for NeurIPS 2021 ML4AD Paper, "Does Thermal data make the detection systems more reliable?"

MultiModal-Collaborative (MMC) Learning Framework for integrating RGB and Thermal spectral modalities This is the official code for NeurIPS 2021 Machi

NeurAI 12 Nov 02, 2022
Python wrappers to the C++ library SymEngine, a fast C++ symbolic manipulation library.

SymEngine Python Wrappers Python wrappers to the C++ library SymEngine, a fast C++ symbolic manipulation library. Installation Pip See License section

136 Dec 28, 2022
A developer interface for creating Chat AIs for the Chai app.

ChaiPy A developer interface for creating Chat AIs for the Chai app. Usage Local development A quick start guide is available here, with a minimal exa

Chai 28 Dec 28, 2022
Robust Self-augmentation for NER with Meta-reweighting

Robust Self-augmentation for NER with Meta-reweighting

Lam chi 17 Nov 22, 2022
Web service for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation based on OpenFace 2.0

OpenGaze: Web Service for OpenFace Facial Behaviour Analysis Toolkit Overview OpenFace is a fantastic tool intended for computer vision and machine le

Sayom Shakib 4 Nov 03, 2022
Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions

Aquarius Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions NOTE: We are currently going through the open-source process requir

Zhiyuan YAO 0 Jun 02, 2022
Virtual Dance Reality Stage: a feature that offers you to share a stage with another user virtually

Portrait Segmentation using Tensorflow This script removes the background from an input image. You can read more about segmentation here Setup The scr

291 Dec 24, 2022
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

Attention Gated Networks (Image Classification & Segmentation) Pytorch implementation of attention gates used in U-Net and VGG-16 models. The framewor

Ozan Oktay 1.6k Dec 30, 2022
Software Platform for solving and manipulating multiparametric programs in Python

PPOPT Python Parametric OPtimization Toolbox (PPOPT) is a software platform for solving and manipulating multiparametric programs in Python. This pack

10 Sep 13, 2022
Official Repository for Machine Learning class - Physics Without Frontiers 2021

PWF 2021 Física Sin Fronteras es un proyecto del Centro Internacional de Física Teórica (ICTP) en Trieste Italia. El ICTP es un centro dedicado a fome

36 Aug 06, 2022
Oscar and VinVL

Oscar: Object-Semantics Aligned Pre-training for Vision-and-Language Tasks VinVL: Revisiting Visual Representations in Vision-Language Models Updates

Microsoft 938 Dec 26, 2022
Open-source Monocular Python HawkEye for Tennis

Tennis Tracking 🎾 Objectives Track the ball Detect court lines Detect the players To track the ball we used TrackNet - deep learning network for trac

ArtLabs 188 Jan 08, 2023
This project demonstrates the use of neural networks and computer vision to create a classifier that interprets the Brazilian Sign Language.

LIBRAS-Image-Classifier This project demonstrates the use of neural networks and computer vision to create a classifier that interprets the Brazilian

Aryclenio Xavier Barros 26 Oct 14, 2022
Sequential model-based optimization with a `scipy.optimize` interface

Scikit-Optimize Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements

Scikit-Optimize 2.5k Jan 04, 2023