This repository attempts to replicate the SqueezeNet architecture and implement the same on an image classification task.

Overview

SqueezeNet-Implementation

This repository attempts to replicate the SqueezeNet architecture using TensorFlow discussed in the research paper: "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size".

The paper can be read here.

The official implementation of this paper can be found here.

Requirements

1. tensorflow-gpu==1.13.1 ( Does not work with Tensorflow 2.x)
2. sklearn
3. opencv-python
4. numpy
5. Python 3.x ( Specifically not python 3.8, anything else works)

Architecture Implemented

  1. Fire Module

  1. SqueezeNet Module

Working

The data used for this implementation was picked up from the Kaggle Dataset - Soil Types

  • Step 1: Clone the repository
git clone https://github.com/RohanMathur17/SqueezeNet-Implementation.git
  • Step 2: Install necessary libraries as discussed in Requirements section
  • Step 3: Within train.py, change your path for data at line 31
Change this line 
base_dir = '/content/gdrive/MyDrive/SqueezeNet/data/'
  • Step 4: In your command prompt, run the train.py file to train the model
python train.py

Additional Information

  • This repository attempts to replicate the architecture only. Performance may vary based on parameters implemented. Can change the same and experiment using the train.py module.
  • A sample usage of this can be found in the Notebook here.
Owner
Rohan Mathur
4th Year Undergrad | Data Science Enthusiast
Rohan Mathur
Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks"

HKD Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks" cifia-100 result The implementation of compared methods are ba

Wang Yucheng 30 Dec 18, 2022
Compute execution plan: A DAG representation of work that you want to get done. Individual nodes of the DAG could be simple python or shell tasks or complex deeply nested parallel branches or embedded DAGs themselves.

Hello from magnus Magnus provides four capabilities for data teams: Compute execution plan: A DAG representation of work that you want to get done. In

12 Feb 08, 2022
an Evolutionary Algorithm assisted GAN

EvoGAN an Evolutionary Algorithm assisted GAN ckpts

3 Oct 09, 2022
Deep Markov Factor Analysis (NeurIPS2021)

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Sarah Ostadabbas 2 Dec 16, 2022
Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

SA-Net: Shuffle Attention for Deep Convolutional Neural Networks (paper) By Qing-Long Zhang and Yu-Bin Yang [State Key Laboratory for Novel Software T

Qing-Long Zhang 199 Jan 08, 2023
Implementation of ConvMixer in TensorFlow and Keras

ConvMixer ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it operates directly on

Sayan Nath 8 Oct 03, 2022
A state of the art of new lightweight YOLO model implemented by TensorFlow 2.

CSL-YOLO: A New Lightweight Object Detection System for Edge Computing This project provides a SOTA level lightweight YOLO called "Cross-Stage Lightwe

Miles Zhang 54 Dec 21, 2022
Source code related to the article submitted to the International Conference on Computational Science ICCS 2022 in London

POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for COVID-19 Detection Source code related to the article submitted to the Internati

Tomasz Szczepański 1 Apr 29, 2022
Weakly Supervised Learning of Rigid 3D Scene Flow

Weakly Supervised Learning of Rigid 3D Scene Flow This repository provides code and data to train and evaluate a weakly supervised method for rigid 3D

Zan Gojcic 124 Dec 27, 2022
(EI 2022) Controllable Confidence-Based Image Denoising

Image Denoising with Control over Deep Network Hallucination Paper and arXiv preprint -- Our frequency-domain insights derive from SFM and the concept

Images and Visual Representation Laboratory (IVRL) at EPFL 5 Dec 18, 2022
Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.

Trading Gym Trading Gym is an open-source project for the development of reinforcement learning algorithms in the context of trading. It is currently

Dimitry Foures 535 Nov 15, 2022
We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Overview This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which

NEU-StatsML-Research 21 Sep 08, 2022
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

Configurations Change HOME_PATH in CONFIG.py as the current path Data Prepare CENSINCOME Download data Put census-income.data and census-income.test i

2 Aug 14, 2022
MBPO (paper: When to trust your model: Model-based policy optimization) in offline RL settings

offline-MBPO This repository contains the code of a version of model-based RL algorithm MBPO, which is modified to perform in offline RL settings Pape

LxzGordon 1 Oct 24, 2021
Lightweight mmm - Lightweight (Bayesian) Media Mix Model

Lightweight (Bayesian) Media Mix Model This is not an official Google product. L

Google 342 Jan 03, 2023
Data and codes for ACL 2021 paper: Towards Emotional Support Dialog Systems

Emotional-Support-Conversation Copyright © 2021 CoAI Group, Tsinghua University. All rights reserved. Data and codes are for academic research use onl

126 Dec 21, 2022
EfficientMPC - Efficient Model Predictive Control Implementation

efficientMPC Efficient Model Predictive Control Implementation The original algo

Vin 8 Dec 04, 2022
A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Improved Adversarial Systems for 3D Object Generation and Reconstruction: This is a repository for the paper "Improved Adversarial Systems for 3D Obje

Edward Smith 188 Dec 25, 2022
Time Delayed NN implemented in pytorch

Pytorch Time Delayed NN Time Delayed NN implemented in PyTorch. Usage kernels = [(1, 25), (2, 50), (3, 75), (4, 100), (5, 125), (6, 150)] tdnn = TDNN

Daniil Gavrilov 79 Aug 04, 2022
3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos This repository contains the source code and dataset for the pa

54 Oct 09, 2022