places365 | The Places365-CNNs for Scene Classification | Machine Learning library

by CSAILVision Python Version: Current License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | places365 Summary

places365 is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Machine Learning, Pytorch applications. places365 has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However places365 build file is not available. You can download it from GitHub.

We release various convolutional neural networks (CNNs) trained on Places365 to the public. Places365 is the latest subset of Places2 Database. There are two versions of Places365: Places365-Standard and Places365-Challenge. The train set of Places365-Standard has ~1.8 million images from 365 scene categories, where there are at most 5000 images per category. We have trained various baseline CNNs on the Places365-Standard and released them as below. Meanwhile, the train set of Places365-Challenge has extra 6.2 million images along with all the images of Places365-Standard (so totally ~8 million images), where there are at most 40,000 images per category. Places365-Challenge will be used for the Places2 Challenge 2016 to be held in conjunction with the ILSVRC and COCO joint workshop at ECCV 2016. The data Places365-Standard and Places365-Challenge are released at Places2 website.

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Quality

Security

License

Reuse

Support

places365 has a medium active ecosystem.

It has 1753 star(s) with 532 fork(s). There are 57 watchers for this library.

It had no major release in the last 6 months.

There are 32 open issues and 57 have been closed. On average issues are closed in 85 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of places365 is current.

Quality

places365 has 0 bugs and 0 code smells.

Security

places365 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

places365 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

places365 is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

places365 releases are not available. You will need to build from source code and install.

places365 has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

places365 saves you 316 person hours of effort in developing the same functionality from scratch.

It has 760 lines of code, 35 functions and 8 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed places365 and discovered the below as its top functions. This is intended to give you an instant insight into places365 implemented functionality, and help decide if they suit your requirements.

Loads a wide model
Construct a ResNet - 18 model
Recursively call recursion on each module
Load the scene txt
Rotate image
Compute the CAM of the layer
Returns the image transformer

Get all kandi verified functions for this library.

places365 Key Features

No Key Features are available at this moment for places365.

places365 Examples and Code Snippets

Dataset Structure

Python

Lines of Code : 97

License : Permissive (MIT)

Copy

data_256
├── concept_train
│   ├── airplane
│   │   ├── airplane
│   ├── bed
│   │   ├── bed
│   ├── desk
│   │   ├── desk
│   ├── fridge
│

Swin Transformer V2: Scaling Up Capacity and Resolution,Usage

Python

Lines of Code : 13

License : Permissive (MIT)

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from swin_transformer_v2 import SwinTransformerV2

from swin_transformer_v2 import swin_transformer_v2_t, swin_transformer_v2_s, swin_transformer_v2_b, \
    swin_transformer_v2_l, swin_transformer_v2_h, swin_transformer_v2_g

# SwinV2-T
swin_transfo

Data Preparation

Python

Lines of Code : 9

License : Permissive (MIT)

Copy

    ├── data_v2
    │   └── BBoxS
    │       └── reverie_obj_feats_v2.pkl
    │   └── BBoxes_v2
    ├── REVERIE_train.json
    ├── REVERIE_val_seen.json
    ├── REVERIE_val_unseen.json
    ├── REVERIE_test.json
    └── objpos.json

Community Discussions

Trending Discussions on places365

My uploading to Activeloop Hub is slow. How to make Hub dataset uploading faster?

QUESTION

My uploading to Activeloop Hub is slow. How to make Hub dataset uploading faster?

Asked 2022-Mar-24 at 00:56

I am working with the places365(resized) dataset. It is a classification dataset with around 2.7 million images and it is 131GB.

I am trying to upload this dataset to Hub—the dataset format for AI—and the dataset was uploading at around 5MB/s. After doing so I was able to load the dataset and around 2.4 million images were there.

Is it possible to make the uploading process faster?

I used the following code to try and upload the dataset:

...

ANSWER

Answered 2022-Mar-24 at 00:56

The speeds you are experiencing are consistent with expectations. Generally, Hub can upload datasets at ~10-15 MB/s single-threaded. It seems like you're running at ~5MB/s, which is roughly in the same ballpark. If you want to run multi-threaded, you can check out the methodology in the upload_parallel function in the script that is found on the Places305 GitHub example page. It uses multiprocessing to speed things up.

Btw, you are also able to visualize Places365 on Activeloop Platform.

Source https://stackoverflow.com/questions/71550416

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install places365

You can download it from GitHub.
You can use places365 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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