I thought animals lived in Zoos? Selecting computer vision models from model zoos

In my last blog, I discussed some basics concepts of computer vision and how to create a facial recognition filter using OpenCV. But what if you want to detect in an image something other than faces? There are two possible ways forward:

1. Train your own model
2. Use a pre-existing openly available model

#computer-vision #object-detection #data-science #pytorch #tensorflow

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I thought animals lived in Zoos? Selecting computer vision models from model zoos

In my last blog, I discussed some basics concepts of computer vision and how to create a facial recognition filter using OpenCV. But what if you want to detect in an image something other than faces? There are two possible ways forward:

1. Train your own model
2. Use a pre-existing openly available model

#computer-vision #object-detection #data-science #pytorch #tensorflow

How to Predict Housing Prices with Linear Regression?

The final objective is to estimate the cost of a certain house in a Boston suburb. In 1970, the Boston Standard Metropolitan Statistical Area provided the information. To examine and modify the data, we will use several techniques such as data pre-processing and feature engineering. After that, we'll apply a statistical model like regression model to anticipate and monitor the real estate market.

Project Outline:

• EDA
• Feature Engineering
• Pick and Train a Model
• Interpret
• Conclusion

EDA

Before using a statistical model, the EDA is a good step to go through in order to:

• Recognize the data set
• Check to see if any information is missing.
• Find some outliers.
• To get more out of the data, add, alter, or eliminate some features.

Importing the Libraries

• Recognize the data set
• Check to see if any information is missing.
• Find some outliers.
• To get more out of the data, add, alter, or eliminate some features.

# Import the libraries #Dataframe/Numerical libraries import pandas as pd import numpy as np #Data visualization import plotly.express as px import matplotlib import matplotlib.pyplot as plt import seaborn as sns #Machine learning model from sklearn.linear_model import LinearRegression

Reading the Dataset with Pandas

#Reading the data path='./housing.csv' housing_df=pd.read_csv(path,header=None,delim_whitespace=True)

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
0	0.00632	18.0	2.31	0	0.538	6.575	65.2	4.0900	1	296.0	15.3	396.90	4.98	24.0
1	0.02731	0.0	7.07	0	0.469	6.421	78.9	4.9671	2	242.0	17.8	396.90	9.14	21.6
2	0.02729	0.0	7.07	0	0.469	7.185	61.1	4.9671	2	242.0	17.8	392.83	4.03	34.7
3	0.03237	0.0	2.18	0	0.458	6.998	45.8	6.0622	3	222.0	18.7	394.63	2.94	33.4
4	0.06905	0.0	2.18	0	0.458	7.147	54.2	6.0622	3	222.0	18.7	396.90	5.33	36.2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	0.06263	0.0	11.93	0	0.573	6.593	69.1	2.4786	1	273.0	21.0	391.99	9.67	22.4
502	0.04527	0.0	11.93	0	0.573	6.120	76.7	2.2875	1	273.0	21.0	396.90	9.08	20.6
503	0.06076	0.0	11.93	0	0.573	6.976	91.0	2.1675	1	273.0	21.0	396.90	5.64	23.9
504	0.10959	0.0	11.93	0	0.573	6.794	89.3	2.3889	1	273.0	21.0	393.45	6.48	22.0
505	0.04741	0.0	11.93	0	0.573	6.030	80.8	2.5050	1	273.0	21.0	396.90	7.88	11.9

Have a Look at the Columns

Crime: It refers to a town's per capita crime rate.

ZN: It is the percentage of residential land allocated for 25,000 square feet.

Indus: The amount of non-retail business lands per town is referred to as the indus.

CHAS: CHAS denotes whether or not the land is surrounded by a river.

NOX: The NOX stands for nitric oxide content (part per 10m)

RM: The average number of rooms per home is referred to as RM.

AGE: The percentage of owner-occupied housing built before 1940 is referred to as AGE.

DIS: Weighted distance to five Boston employment centers are referred to as dis.

RAD: Accessibility to radial highways index

TAX: The TAX columns denote the rate of full-value property taxes per $10,000 dollars.

B: B=1000(Bk — 0.63)2 is the outcome of the equation, where Bk is the proportion of blacks in each town.

PTRATIO: It refers to the student-to-teacher ratio in each community.

LSTAT: It refers to the population's lower socioeconomic status.

MEDV: It refers to the 1000-dollar median value of owner-occupied residences.

Data Preprocessing

# Check if there is any missing values. housing_df.isna().sum() CRIM       0 ZN         0 INDUS      0 CHAS       0 NOX        0 RM         0 AGE        0 DIS        0 RAD        0 TAX        0 PTRATIO    0 B          0 LSTAT      0 MEDV       0 dtype: int64

No missing values are found

We examine our data's mean, standard deviation, and percentiles.

housing_df.describe()

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
count	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000
mean	3.613524	11.363636	11.136779	0.069170	0.554695	6.284634	68.574901	3.795043	9.549407	408.237154	18.455534	356.674032	12.653063	22.532806
std	8.601545	23.322453	6.860353	0.253994	0.115878	0.702617	28.148861	2.105710	8.707259	168.537116	2.164946	91.294864	7.141062	9.197104
min	0.006320	0.000000	0.460000	0.000000	0.385000	3.561000	2.900000	1.129600	1.000000	187.000000	12.600000	0.320000	1.730000	5.000000
25%	0.082045	0.000000	5.190000	0.000000	0.449000	5.885500	45.025000	2.100175	4.000000	279.000000	17.400000	375.377500	6.950000	17.025000
50%	0.256510	0.000000	9.690000	0.000000	0.538000	6.208500	77.500000	3.207450	5.000000	330.000000	19.050000	391.440000	11.360000	21.200000
75%	3.677083	12.500000	18.100000	0.000000	0.624000	6.623500	94.075000	5.188425	24.000000	666.000000	20.200000	396.225000	16.955000	25.000000
max	88.976200	100.000000	27.740000	1.000000	0.871000	8.780000	100.000000	12.126500	24.000000	711.000000	22.000000	396.900000	37.970000	50.000000

The crime, area, sector, nitric oxides, 'B' appear to have multiple outliers at first look because the minimum and maximum values are so far apart. In the Age columns, the mean and the Q2(50 percentile) do not match.

We might double-check it by examining the distribution of each column.

Inferences

1. The rate of crime is rather low. The majority of values are in the range of 0 to 25. With a huge value and a value of zero.
2. The majority of residential land is zoned for less than 25,000 square feet. Land zones larger than 25,000 square feet represent a small portion of the dataset.
3. The percentage of non-retial commercial acres is mostly split between two ranges: 0-13 and 13-23.
4. The majority of the properties are bordered by the river, although a tiny portion of the data is not.
5. The content of nitrite dioxide has been trending lower from.3 to.7, with a little bump towards.8. It is permissible to leave a value in the range of 0.1–1.
6. The number of rooms tends to cluster around the average.
7. With time, the proportion of owner-occupied units rises.
8. As the number of weights grows, the weight distance between 5 employment centers reduces. It could indicate that individuals choose to live in new high-employment areas.
9. People choose to live in places with limited access to roadways (0-10). We have a 30th percentile outlier.
10. The majority of dwelling taxes are in the range of $200-450, with large outliers around $700,000.
11. The percentage of people with lower status tends to cluster around the median. The majority of persons are of lower social standing.

Because the model is overly generic, removing all outliers will underfit it. Keeping all outliers causes the model to overfit and become excessively accurate. The data's noise will be learned.

The approach is to establish a happy medium that prevents the model from becoming overly precise. When faced with a new set of data, however, they generalise well.

We'll keep numbers below 600 because there's a huge anomaly in the TAX column around 600.

new_df=housing_df[housing_df['TAX']<600]

Looking at the Distribution

The overall distribution, particularly the TAX, PTRATIO, and RAD, has improved slightly.

Correlation

Perfect correlation is denoted by the clear values. The medium correlation between the columns is represented by the reds, while the negative correlation is represented by the black.

With a value of 0.89, we can see that 'MEDV', which is the medium price we wish to anticipate, is substantially connected with the number of rooms 'RM'. The proportion of black people in area 'B' with a value of 0.19 is followed by the residential land 'ZN' with a value of 0.32 and the percentage of black people in area 'ZN' with a value of 0.32.

The metrics that are most connected with price will be plotted.

Feature Engineering

Feature Scaling

Gradient descent is aided by feature scaling, which ensures that all features are on the same scale. It makes locating the local optimum much easier.

Mean standardization is one strategy to employ. It substitutes (target-mean) for the target to ensure that the feature has a mean of nearly zero.

def standard(X):    '''Standard makes the feature 'X' have a zero mean'''    mu=np.mean(X) #mean    std=np.std(X) #standard deviation    sta=(X-mu)/std # mean normalization    return mu,std,sta     mu,std,sta=standard(X) X=sta X

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT
0	-0.609129	0.092792	-1.019125	-0.280976	0.258670	0.279135	0.162095	-0.167660	-2.105767	-0.235130	-1.136863	0.401318	-0.933659
1	-0.575698	-0.598153	-0.225291	-0.280976	-0.423795	0.049252	0.648266	0.250975	-1.496334	-1.032339	-0.004175	0.401318	-0.219350
2	-0.575730	-0.598153	-0.225291	-0.280976	-0.423795	1.189708	0.016599	0.250975	-1.496334	-1.032339	-0.004175	0.298315	-1.096782
3	-0.567639	-0.598153	-1.040806	-0.280976	-0.532594	0.910565	-0.526350	0.773661	-0.886900	-1.327601	0.403593	0.343869	-1.283945
4	-0.509220	-0.598153	-1.040806	-0.280976	-0.532594	1.132984	-0.228261	0.773661	-0.886900	-1.327601	0.403593	0.401318	-0.873561
...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	-0.519445	-0.598153	0.585220	-0.280976	0.604848	0.306004	0.300494	-0.936773	-2.105767	-0.574682	1.445666	0.277056	-0.128344
502	-0.547094	-0.598153	0.585220	-0.280976	0.604848	-0.400063	0.570195	-1.027984	-2.105767	-0.574682	1.445666	0.401318	-0.229652
503	-0.522423	-0.598153	0.585220	-0.280976	0.604848	0.877725	1.077657	-1.085260	-2.105767	-0.574682	1.445666	0.401318	-0.820331
504	-0.444652	-0.598153	0.585220	-0.280976	0.604848	0.606046	1.017329	-0.979587	-2.105767	-0.574682	1.445666	0.314006	-0.676095
505	-0.543685	-0.598153	0.585220	-0.280976	0.604848	-0.534410	0.715691	-0.924173	-2.105767	-0.574682	1.445666	0.401318	-0.435703

Choose and Train the Model

For the sake of the project, we'll apply linear regression.

Typically, we run numerous models and select the best one based on a particular criterion.

Linear regression is a sort of supervised learning model in which the response is continuous, as it relates to machine learning.

Form of Linear Regression

y= θX+θ1 or y= θ1+X1θ2 +X2θ3 + X3θ4

y is the target you will be predicting

0 is the coefficient

x is the input

We will Sklearn to develop and train the model

#Import the libraries to train the model from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression

Allow us to utilise the train/test method to learn a part of the data on one set and predict using another set using the train/test approach.

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) [7.22218258] 24.66379606613584

In this example, you will learn the model using below hypothesis:

Price= 24.85 + 7.18* Room

It is interpreted as:

For a decided price of a house:

A 7.18-unit increase in the price is connected with a growth in the number of rooms.

As a side note, this is an association, not a cause!

Interpretation

You will need a metric to determine whether our hypothesis was right. The RMSE approach will be used.

Root Means Square Error (RMSE) is defined as the square root of the mean of square error. The difference between the true and anticipated numbers called the error. It's popular because it can be expressed in y-units, which is the median price of a home in our scenario.

def rmse(predict,actual):    return np.sqrt(np.mean(np.square(predict - actual))) # Split the Data into train and test set X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) loss=rmse(predictions_test,y_test) print('loss: ',loss) print(model.score(X_test,y_test)) #accuracy [7.43327725] 24.912055881970886 loss: 3.9673165450580714 0.7552661033654667 Loss will be 3.96

This means that y-units refer to the median value of occupied homes with 1000 dollars.

This will be less by 3960 dollars.

While learning the model you will have a high variance when you divide the data. Coefficient and intercept will vary. It's because when we utilized the train/test approach, we choose a set of data at random to place in either the train or test set. As a result, our theory will change each time the dataset is divided.

This problem can be solved using a technique called cross-validation.

Improvisation in the Model

With 'Forward Selection,' we'll iterate through each parameter to assist us choose the numbers characteristics to include in our model.

Forward Selection

1. Choose the most appropriate variable (in our case based on high correlation)
2. Add the next best variable to the model
3. Some predetermined conditions must meet.

We'll use a random state of 1 so that each iteration yields the same outcome.

cols=[] los=[] los_train=[] scor=[] i=0 while i < len(high_corr_var):    cols.append(high_corr_var[i])        # Select inputs variables    X=new_df[cols]        #mean normalization    mu,std,sta=standard(X)    X=sta        # Split the data into training and testing    X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=1)        #fit the model to the training    lnreg=LinearRegression().fit(X_train,y_train)        #make prediction on the training test    prediction_train=lnreg.predict(X_train)        #make prediction on the testing test    prediction=lnreg.predict(X_test)        #compute the loss on train test    loss=rmse(prediction,y_test)    loss_train=rmse(prediction_train,y_train)    los_train.append(loss_train)    los.append(loss)        #compute the score    score=lnreg.score(X_test,y_test)    scor.append(score)        i+=1

We have a big 'loss' with a smaller collection of variables, yet our system will overgeneralize in this scenario. Although we have a reduced 'loss,' we have a large number of variables. However, if the model grows too precise, it may not generalize well to new data.

In order for our model to generalize well with another set of data, we might use 6 or 7 features. The characteristic chosen is descending based on how strong the price correlation is.

high_corr_var ['RM', 'ZN', 'B', 'CHAS', 'RAD', 'DIS', 'CRIM', 'NOX', 'AGE', 'TAX', 'INDUS', 'PTRATIO', 'LSTAT']

With 'RM' having a high price correlation and LSTAT having a negative price correlation.

# Create a list of features names feature_cols=['RM','ZN','B','CHAS','RAD','CRIM','DIS','NOX'] #Select inputs variables X=new_df[feature_cols] # Split the data into training and testing sets X_train,X_test,y_train,y_test= train_test_split(X,y, random_state=1) # feature engineering mu,std,sta=standard(X) X=sta # fit the model to the trainning data lnreg=LinearRegression().fit(X_train,y_train) # make prediction on the testing test prediction=lnreg.predict(X_test) # compute the loss loss=rmse(prediction,y_test) print('loss: ',loss) lnreg.score(X_test,y_test) loss: 3.212659865936143 0.8582338376696363

The test set yielded a loss of 3.21 and an accuracy of 85%.

Other factors, such as alpha, the learning rate at which our model learns, could still be tweaked to improve our model. Alternatively, return to the preprocessing section and working to increase the parameter distribution.

For more details regarding scraping real estate data you can contact Scraping Intelligence today

https://www.websitescraper.com/how-to-predict-housing-prices-with-linear-regression.php

Top Chat Software for Live Streaming & Broadcasting Web & Mobile Apps

Do you Increase your Website Engagment?

I analysed, ranked and reviewed best live video streaming chat APIs and SDKs for your web & mobile app based on client reviews and ratings. portfolio, usecases, cost, secure streaming, live chat features, cost, support, etc.

Turn your viewers into participatients with Live Streaming Chat Solutions. There are lot of Real-time chat apis & SDks Providers have in online market now. You can easily integrte and customize real time chat solutions into your new or existing live video streaming web and iOS & android applications. Below have mentioned best real time chat api & SDk Proivders.

Here are The Most Popular Live Video Streaming Chat APIs & SDKs to be Considered for your Mobile App

1. CONTUS Fly - Real-time Messaging Platform for Live Streaming Apps & Webs

CONTUS Fly is one of the leading real time messaging software providers in the market for a decade. Their messaging platforms are completely customizable since they provide Chat APIs and SDKs to integrate real time chat feasibility on your live streaming applications irrespective of audience base. Engage your audience like a live concert, stadium like experience through digitally. Create channels for every live streaming event, sports or anything that would create buzz. Enable audience to interact with each other over voice, video chats and real-time text chats with engaging emojis. CONTUS Fly enables users to add emojis and stickers to captivate each audience and create fun.

Highlight Features of CONTUS Fly Live Video Streaming Platform Includes:

1. Chat for Live Video Streaming
2. Video & Audio Recording
3. Video Calling
4. Drawing whitebord
5. Screen Sharing
6. End to End Encryption

2. Apphitect -Instant chat for Live Streaming Platforms

To make every live streaming and broadcasting videos more engaging and entertaining, Apphitect’s instant messaging comes with exciting Instant messaging chat APIs to add chat into streaming applications. Apphitect is built with multiple real time communication features like video chat, voice chat and real-time chat to your streaming apps. Their solution surprisingly has a wide range of features to communicate, engage and increase subscription benefits.

Highlight Features of Apphitect Live Insterative Broadcasting Software Includes:

1. Live Video Streaming Chat
2. Cross Platform Support
3. Audio & Video Recording
4. Live Video Calling
5. Emoji & Stickers

3. MirrorFly - Enterprise Real Time Chat for Streaming Websites

One of the enterprise-grade real-time chat solutions built to create virtual chat experience for live streaming events and websites for big brands and startups. Irrespective of audience base, category, MirrorFly provides customizable real time chat APIs to add virtual communication mediums on live streaming and broadcasting applications. Their solution comes with absolute moderation tools and open channels to talk and listen with your audience. MirrorFly’s server infrastructure has the potential to handle concurrent messages and users and to achieve maximum sales conversion.

Highlight Features of MirrorFly Live Streaming Chat API Includes:

1. Face to Face Video Calling
2. Live Interactive Broadcasting
3. Call Recording
4. Digital Whiteboard
5. Group Video Calling

4. Applozic - Real-time Chat Plugin for Live Broadcasting & Video Streaming apps

When it comes to building a live streaming chat app software that covers the entire platforms and demand All-in-One package (features, Customization to any extent) with a one-time payment for lifetime performance, then undoubtedly Contus Fly makes the right choice to partner with. The company offers live broadcasting SDK for Android/iOS and chat APIs for customization.

Highlight Features of Applozic Chat Live Streaming Platform Includes:

1. Real time Communication
2. Cross Platform Support
3. Live Audio Broadcasting
4. Push Notifications
5. Secure Image Sharing

5. Sendbird - Top Real time Chat for Live Video Streams

Being a leading real time chat platform provider in the market, Sendbird has its own hallmark of communication features to the world’s most prominent live streaming applications. Their real time chat solution enables broadcasting and streaming platform’ owners to create a physical equivalent digital chat experience for the audience during any live event streaming to interact, collaborate and cheer together within the same streaming screen. By creating open channels and groups, you can enable the audience to interact with each other during any streaming, engage them with polls, stickers, multiple communication channels and more.

Highlight Features of Sendbird Live Streaming Chat API Includes:

1. Chat for Streaming website
2. Messaging Data
3. Multi Platforms
4. Push Notifications
5. End to End Encryption

6. Agora - Interactive Live Chat for Live Video Streaming

Agora, a deep integratable API available in the market to deliver live interactive streaming experience for workplace, enterprises, gaming, retail, telehealth and social live streaming websites. With easy-to-embed SDKs, Agora empowers businesses to add HD and low latency video and voice chat features into any streaming platforms and channels. Their easy-to-embed real time chat features encourage higher levels of user engagement and opportunity to drive more audience.

7. Enablex - A Redefined Communication APIs for In-app Chat

Their smart and secure chat APIs deliver real-time chat feasibility for live and on-demand video streaming websites. The real time chat features provides users to communicate and engage within the same streaming platform irrespective of interaction medium and audience count. Enablex offers platform-as-a-service communication solutions for real time messaging integration with APIs hosting possibility on public, private and cloud deployment. Their APIs are enriched with multiple communication features and engagement tools like live-polls, stickers and more.

8. Pubnub - In-app Chat Platforms for Live Event Streaming Websites

In order to increase user engagement with live and remote audiences, Pubnub offers real time messaging chat functionality with interactive features to drive event-based engagement with mass chat. Their in-app chat feature enhances live programs, event streaming and blogging content with live polling, multiple chats and more. It also enables live streaming websites to build community, channels and super groups during live streaming to bring the entire audience base to one place.

9. Vonage - Communication APIs for In-app Messagings

Vonage is a prime provider of communication APIs for major industrial sectors and enterprise workplaces. With its API, businesses such as live streaming applications can integrate in-app messaging features into any streaming platforms on Android, iOS and Web to empower user engagement. Their APIs are powered with scalable infrastructure and provide multiple communication mediums such as in-app voice, video and chat proactively engaging the audience.

10. Firekast - Live Chat Widget for Video Streaming Player

Firekast provides a customizable live chat widget with HTML code for streaming players to enable chat within any streaming or on-demand videos. The chat widget gives the ability for brands and content owners to make the audience to interact with each other for better engagement and proactivity during streaming. The Firekast Live chat comes with moderator tools that will allow administrators to delete or ban abusive content and users from the channel or groups. Firekast’s live chat comes with a private chat widget to create public or private chat rooms to make effective collaboration and discussions.
 

Conclusion
And this is all the real time chat providers in the market to implement chat functionality in any live streaming or broadcasting platforms. More than delivering entertaining live content, creating a massive engagement and buzz for every live event is the smarter way to turn every audience into a protiable subscriber. Picking up the right software provider is more important than just handling the integration process.

#live #live-streaming-solutions #live-streaming-chat-api #live-streaming-chat-sdk #chat-api-for-live-broadcasting

How To Deter Adversarial Attacks In Computer Vision Models

While computer vision has become one of the most used technologies across the globe, computer vision models are not immune to threats. One of the reasons for this threat is the underlying lack of robustness of the models. Indrajit Kar, who is the Principal Solution Architect at Accenture, took through a talk at CVDC 2020 on how to make AI more resilient to attack.

As Kar shared, AI has become the new target for attackers, and the instances of manipulation and adversaries have increased dramatically over the last few years. From companies such as Google and Tesla to startups are affected by adversarial attacks.

“While we celebrate advancements in AI, deep neural networks (DNNs)—the algorithms intrinsic to much of AI—have recently been proven to be at risk from attack through seemingly benign inputs. It is possible to fool DNNs by making subtle alterations to input data that often either remain undetected or are overlooked if presented to a human,” he said.

Type Of Adversarial Attacks

Alterations to images that are so small as to remain unnoticed by humans can cause DNNs to misinterpret the image content. As many AI systems take their input from external sources—voice recognition devices or social media upload, for example—this ability to be tricked by adversarial input opens a new, often intriguing, security threat. This has called for an increase in cybersecurity which is coming together to address the crevices in computer vision and machine learning.

#developers corner #adversarial attacks #computer vision #computer vision adversarial attack

Vision: Datasets, Transforms and Models Specific to Computer Vision

Torchvision

The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.

Installation

We recommend Anaconda as Python package management system. Please refer to pytorch.org for the detail of PyTorch (torch) installation. The following is the corresponding torchvision versions and supported Python versions.

torch	torchvision	python
main / nightly	main / nightly	>=3.7, <=3.10
1.13.0	0.14.0	>=3.7, <=3.10
1.12.0	0.13.0	>=3.7, <=3.10
1.11.0	0.12.0	>=3.7, <=3.10
1.10.2	0.11.3	>=3.6, <=3.9
1.10.1	0.11.2	>=3.6, <=3.9
1.10.0	0.11.1	>=3.6, <=3.9
1.9.1	0.10.1	>=3.6, <=3.9
1.9.0	0.10.0	>=3.6, <=3.9
1.8.2	0.9.2	>=3.6, <=3.9
1.8.1	0.9.1	>=3.6, <=3.9
1.8.0	0.9.0	>=3.6, <=3.9
1.7.1	0.8.2	>=3.6, <=3.9
1.7.0	0.8.1	>=3.6, <=3.8
1.7.0	0.8.0	>=3.6, <=3.8
1.6.0	0.7.0	>=3.6, <=3.8
1.5.1	0.6.1	>=3.5, <=3.8
1.5.0	0.6.0	>=3.5, <=3.8
1.4.0	0.5.0	==2.7, >=3.5, <=3.8
1.3.1	0.4.2	==2.7, >=3.5, <=3.7
1.3.0	0.4.1	==2.7, >=3.5, <=3.7
1.2.0	0.4.0	==2.7, >=3.5, <=3.7
1.1.0	0.3.0	==2.7, >=3.5, <=3.7
<=1.0.1	0.2.2	==2.7, >=3.5, <=3.7

Anaconda:

conda install torchvision -c pytorch

pip:

pip install torchvision

From source:

python setup.py install
# or, for OSX
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install

We don't officially support building from source using pip, but if you do, you'll need to use the --no-build-isolation flag. In case building TorchVision from source fails, install the nightly version of PyTorch following the linked guide on the contributing page and retry the install.

By default, GPU support is built if CUDA is found and torch.cuda.is_available() is true. It's possible to force building GPU support by setting FORCE_CUDA=1 environment variable, which is useful when building a docker image.

Image Backend

Torchvision currently supports the following image backends:

• Pillow (default)
• Pillow-SIMD - a much faster drop-in replacement for Pillow with SIMD. If installed will be used as the default.
• accimage - if installed can be activated by calling torchvision.set_image_backend('accimage')
• libpng - can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions.
• libjpeg - can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. libjpeg-turbo can be used as well.

Notes: libpng and libjpeg must be available at compilation time in order to be available. Make sure that it is available on the standard library locations, otherwise, add the include and library paths in the environment variables TORCHVISION_INCLUDE and TORCHVISION_LIBRARY, respectively.

Video Backend

Torchvision currently supports the following video backends:

• pyav (default) - Pythonic binding for ffmpeg libraries.
• video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any conflicting version of ffmpeg installed. Currently, this is only supported on Linux.

conda install -c conda-forge ffmpeg
python setup.py install

Using the models on C++

TorchVision provides an example project for how to use the models on C++ using JIT Script.

Installation From source:

mkdir build
cd build
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install

Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:

find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)

The TorchVision package will also automatically look for the Torch package and add it as a dependency to my-target, so make sure that it is also available to cmake via the CMAKE_PREFIX_PATH.

For an example setup, take a look at examples/cpp/hello_world.

Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any Python dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link to Python. This can be done by passing -DUSE_PYTHON=on to CMake.

TorchVision Operators

In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that you #include <torchvision/vision.h> in your project.

Documentation

You can find the API documentation on the pytorch website: https://pytorch.org/vision/stable/index.html

Contributing

See the CONTRIBUTING file for how to help out.

Disclaimer on Datasets

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

Pre-trained Model License

The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.

More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See SWAG LICENSE for additional details.

Download Details:

Author: Pytorch
Source Code: https://github.com/pytorch/vision 
License: BSD-3-Clause license

#machinelearning #computer #vision #dataset