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https://github.com/akash1070/computer-vision

Building & Deploying Computer Vision Models
https://github.com/akash1070/computer-vision

alexnet cnn-architecture cnn-keras covnets dropout-keras googlenet-inception-network lenet-5 lenet-architecture mnist-dataset mnist-fashion object-detection opencv-python pooling pycharm-ide transfer-learning vgg16 yolov5

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Building & Deploying Computer Vision Models

Host: GitHub
URL: https://github.com/akash1070/computer-vision
Owner: Akash1070
Created: 2022-09-23T03:03:22.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2022-09-23T03:43:59.000Z (about 2 years ago)
Last Synced: 2024-10-11T02:41:23.950Z (about 1 month ago)
Topics: alexnet, cnn-architecture, cnn-keras, covnets, dropout-keras, googlenet-inception-network, lenet-5, lenet-architecture, mnist-dataset, mnist-fashion, object-detection, opencv-python, pooling, pycharm-ide, transfer-learning, vgg16, yolov5
Language: Jupyter Notebook
Homepage:
Size: 1.62 MB
Stars: 1
Watchers: 2
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        # **Building and Deploying Models of Computer Vision**

![Python 3.6](https://img.shields.io/badge/Python-3.6-brightgreen.svg)

## Authors

- [@Akash Kumar Jha](https://github.com/Akash1070)

## Deployment

    1. Data Extraction

    2. Analyze the Data

    3. Data Preprocessing.

    4. Building CNN Architecture

    5. Train the Model

    6. Model Evaluation on the Test Set

    7. Adding Dropout into the Network

    8. Final loss and accuracy

    

## Installation

To install the libraries used in this project. Follow the 

below steps:

```bash

%tensorflow_version 2.x

import tensorflow

tensorflow.__version__

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import matplotlib.pyplot as plt # plotting library

%matplotlib inline

from keras.models import Sequential

import tensorflow as tf

from tensorflow.keras.optimizers import Adam # - Works ,RMSprop

from tensorflow.keras.utils import to_categorical, plot_model

from keras import  backend as K

from tensorflow.keras.datasets import mnist

from keras.layers import Dense

from keras.layers.convolutional import Conv2D, MaxPooling2D

# from keras.models import Sequential

from keras.layers import Activation, Flatten, Dropout

from tensorflow.keras.layers import BatchNormalization

from keras.layers.advanced_activations import LeakyReLU

from keras.datasets import fashion_mnist

import random

random.seed(0)

# Ignore the warnings

import warnings

warnings.filterwarnings("ignore")

```

    

## Running Flask Api

To run tests, run the following command

```bash

  python app.py

```

## 🚀 About Me

Data Scientist Enthusiast | Petroleum Engineer Graduate | Solving Problems Using Data 

# Hi, I'm Akash! 👋

## 🔗 Links

[![github](https://img.shields.io/badge/github-000?style=for-the-badge&logo=ko-fi&logoColor=white)](https://github.com/Akash1070)

[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/akashkumar107/)

## Tech Stack

![Logo](https://businesstoys.in/assets/programs/full-stack-data-science-professional-program/tools.png)

## Other Me

👩‍💻 I’m interested in Petroleum Engineering

🧠 I’m currently learning Data Scientist | Data Analytics | Business Analytics

👯‍♀️ I’m looking to collaborate on Ideas & Data

## 🛠 Skills

1. Data Scientist

2. Data Analyst

3. Business Analyst

4. Machine Learning 

## Future Plans 

⚡️ Looking forward to help drive innovations into your company as a Data Scientist

⚡️ Looking forward to offer more than I take and leave the place better than i found