LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

Overview

Deep-Leafsnap

Convolutional Neural Networks have become largely popular in image tasks such as image classification recently largely due to to Krizhevsky, et al. in their famous paper ImageNet Classification with Deep Convolutional Neural Networks. Famous models such as AlexNet, VGG-16, ResNet-50, etc. have scored state of the art results on image classfication datasets such as ImageNet and CIFAR-10.

We present an application of CNN's to the task of classifying trees by images of their leaves; specifically all 185 types of trees in the United States. This task proves to be difficult for traditional computer vision methods due to the high number of classes, inconsistency in images, and large visual similarity between leaves.

Kumar, et al. developed a automatic visual recognition algorithm in their 2012 paper Leafsnap: A Computer Vision System for Automatic Plant Species Identification to attempt to solve this problem.

Our model is based off VGG-16 except modified to work with 64x64 size inputs. We achieved state of the art results at the time. Our deep learning approach to this problem further improves the accuracy from 70.8% to 86.2% for the top-1 prediction accuracy and from 96.8% to 98.4% for top-5 prediction accuracy.

Top-1 Accuracy Top-5 Accuracy
Leafsnap 70.8% 96.8%
Deep-Leafsnap 86.2% 98.4%

We noticed that our model failed to recognize specific classes of trees constantly causing our overall accuracy to derease. This is primarily due to the fact that those trees had very small leaves which were hard to preprocess and crop. Our training images were also resized to 64x64 due to limited computational resources. We plan on further improving our data preprocessing and increasing our image size to 224x224 in order to exceed 90% for our top-1 prediction acurracy.

The following goes over the code and how to set it up on your own machine.

Files

  • model.py trains a convolutional neural network on the dataset.
  • vgg.py PyTorch model code for VGG-16.
  • densenet.py PyTorch model code for DenseNet-121.
  • resnet.py PyTorch model code for ResNet.
  • dataset.py creates a new train/test dataset by cropping the leaf and augmenting the data.
  • utils.py helps do some of the hardcore image processing in dataset.py.
  • averagemeter.py helper class which keeps track of a bunch of averages when training.
  • leafsnap-dataset-images.csv is the CSV file corresponding to the dataset.
  • requirements.txt contains the pip requirements to run the code.

Installation

To run the models and code make sure you Python installed.

Install PyTorch by following the directions here.

Clone the repo onto your local machine and cd into the directory.

git clone https://github.com/sujithv28/Deep-Leafsnap.git
cd Deep-Leafsnap

Install all the python dependencies:

pip install -r requirements.txt

Make sure sklearn is updated to the latest version.

pip install --upgrade sklearn

Also make sure you have OpenCV installed either through pip or homebrew. You can check if this works by running and making sure nothing complains:

python
import cv2

Download Leafsnap's image data and extract it to the main directory by running in the directory. Original data can be found here.

wget https://www.dropbox.com/s/dp3sk8wpiu9yszg/data.zip?dl=0
unzip -a data.zip?dl=0
rm data.zip?dl=0

Create the Training and Testing Data

To create the dataset, run

python dataset.py

This cleans the dataset by cropping only neccesary portions of the images containing the leaves and also resizes them to 64x64. If you want to change the image size go to utils.py and change img = misc.imresize(img, (64,64))to any size you want.

Training Model

To train the model, run

python model.py
Owner
Sujith Vishwajith
Computer Science & Math @ University of Maryland
Sujith Vishwajith
A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)

A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper Gated G

Ching-Yao Chuang 427 Dec 13, 2022
An implementation of the efficient attention module.

Efficient Attention An implementation of the efficient attention module. Description Efficient attention is an attention mechanism that substantially

Shen Zhuoran 194 Dec 15, 2022
A Nim frontend for pytorch, aiming to be mostly auto-generated and internally using ATen.

Master Release Pytorch - Py + Nim A Nim frontend for pytorch, aiming to be mostly auto-generated and internally using ATen. Because Nim compiles to C+

Giovanni Petrantoni 425 Dec 22, 2022
This game was designed to encourage young people not to gamble on lotteries, as the probablity of correctly guessing the number is infinitesimal!

Lottery Simulator 2022 for Web Launch Application Developed by John Seong in Ontario. This game was designed to encourage young people not to gamble o

John Seong 2 Sep 02, 2022
SBINN: Systems-biology informed neural network

SBINN: Systems-biology informed neural network The source code for the paper M. Daneker, Z. Zhang, G. E. Karniadakis, & L. Lu. Systems biology: Identi

Lu Group 15 Nov 19, 2022
KinectFusion implemented in Python with PyTorch

KinectFusion implemented in Python with PyTorch This is a lightweight Python implementation of KinectFusion. All the core functions (TSDF volume, fram

Jingwen Wang 80 Jan 03, 2023
Invariant Causal Prediction for Block MDPs

MISA Abstract Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challeng

Meta Research 41 Sep 17, 2022
The implementation of ICASSP 2020 paper "Pixel-level self-paced learning for super-resolution"

Pixel-level Self-Paced Learning for Super-Resolution This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resoluti

Elon Lin 41 Dec 15, 2022
code for "Self-supervised edge features for improved Graph Neural Network training",

Self-supervised edge features for improved Graph Neural Network training Data availability: Here is a link to the raw data for the organoids dataset.

Neal Ravindra 23 Dec 02, 2022
PyTorch package for the discrete VAE used for DALL·E.

Overview [Blog] [Paper] [Model Card] [Usage] This is the official PyTorch package for the discrete VAE used for DALL·E. Installation Before running th

OpenAI 9.5k Jan 05, 2023
A Partition Filter Network for Joint Entity and Relation Extraction EMNLP 2021

EMNLP 2021 - A Partition Filter Network for Joint Entity and Relation Extraction

zhy 127 Jan 04, 2023
Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

Geotrend 79 Dec 28, 2022
Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts

DataSelection-NMT Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts Quick update: The paper got accepted o

Javad Pourmostafa 6 Jan 07, 2023
Example-custom-ml-block-keras - Custom Keras ML block example for Edge Impulse

Custom Keras ML block example for Edge Impulse This repository is an example on

Edge Impulse 8 Nov 02, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
Certifiable Outlier-Robust Geometric Perception

Certifiable Outlier-Robust Geometric Perception About This repository holds the implementation for certifiably solving outlier-robust geometric percep

83 Dec 31, 2022
ISBI 2022: Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image.

Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Introduction This repository contains the PyTorch implem

25 Nov 09, 2022
Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness

Orthogonalizing Convolutional Layers with the Cayley Transform This repository contains implementations and source code to reproduce experiments for t

CMU Locus Lab 36 Dec 30, 2022
Python package for dynamic system estimation of time series

PyDSE Toolset for Dynamic System Estimation for time series inspired by DSE. It is in a beta state and only includes ARMA models right now. Documentat

Blue Yonder GmbH 40 Oct 07, 2022
Code for the paper "Graph Attention Tracking". (CVPR2021)

SiamGAT 1. Environment setup This code has been tested on Ubuntu 16.04, Python 3.5, Pytorch 1.2.0, CUDA 9.0. Please install related libraries before r

122 Dec 24, 2022