faster-rcnn.pytorch | A faster pytorch implementation of faster r-cnn | Machine Learning library
kandi X-RAY | faster-rcnn.pytorch Summary
kandi X-RAY | faster-rcnn.pytorch Summary
faster-rcnn.pytorch is a Python library typically used in Artificial Intelligence, Machine Learning applications. faster-rcnn.pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
A faster pytorch implementation of faster r-cnn (a fork)
A faster pytorch implementation of faster r-cnn (a fork)
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faster-rcnn.pytorch has a low active ecosystem.
It has 1 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
faster-rcnn.pytorch has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of faster-rcnn.pytorch is current.
Quality
faster-rcnn.pytorch has 0 bugs and 0 code smells.
Security
faster-rcnn.pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
faster-rcnn.pytorch code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
faster-rcnn.pytorch is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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faster-rcnn.pytorch releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
It has 5455 lines of code, 292 functions and 81 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed faster-rcnn.pytorch and discovered the below as its top functions. This is intended to give you an instant insight into faster-rcnn.pytorch implemented functionality, and help decide if they suit your requirements.
- Get an item from the group .
- Evaluate a set of images .
- Evaluate Gt objects for a given class
- Calculate evaluation results .
- Sample the rois using pytorch .
- Finds the bounding box overlaps between the given anchors .
- Load an annotation file .
- Load image index .
- Load COC annotation .
- Parse command line arguments
Get all kandi verified functions for this library.
faster-rcnn.pytorch Key Features
No Key Features are available at this moment for faster-rcnn.pytorch.
faster-rcnn.pytorch Examples and Code Snippets
No Code Snippets are available at this moment for faster-rcnn.pytorch.
Community Discussions
Trending Discussions on faster-rcnn.pytorch
QUESTION
Pytorch Faster R-CNN size mismatch errors in testing
Asked 2020-Jun-08 at 03:36
there!
When running test_net.py in pytorch1.0 Faster R-CNN and demo.py on coco dataset with faster_rcnn_1_10_9771.pth(the pretrained resnet101 model on coco dataset provided by jwyang), I encounter the same errors below :
...ANSWER
Answered 2020-Jun-08 at 03:36It says your model doesn't fit the pre-trained parameters you want to load.
Maybe check the model you're using and the .pth file and find out if they match or what.
Or post the code of your model and let's see what's going wrong.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install faster-rcnn.pytorch
You can download it from GitHub.
You can use faster-rcnn.pytorch 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.
You can use faster-rcnn.pytorch 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.
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This project is a faster pytorch implementation of faster R-CNN, aimed to accelerating the training of faster R-CNN object detection models. Recently, there are a number of good implementations:.
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