faster-rcnn-resnet | ResNet Implementation for Faster-rcnn | Computer Vision library

 by   Eniac-Xie Python Version: Current License: MIT

X-RayKey FeaturesCode SnippetsCommunity Discussions(1)VulnerabilitiesInstallSupport

kandi X-RAY | faster-rcnn-resnet Summary

kandi X-RAY | faster-rcnn-resnet Summary

faster-rcnn-resnet is a Python library typically used in Artificial Intelligence, Computer Vision applications. faster-rcnn-resnet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However faster-rcnn-resnet build file is not available. You can download it from GitHub.

ResNet Implementation for Faster-rcnn
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            kandi-support Support

              faster-rcnn-resnet has a low active ecosystem.
              It has 208 star(s) with 118 fork(s). There are 11 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 17 open issues and 7 have been closed. On average issues are closed in 5 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of faster-rcnn-resnet is current.

            kandi-Quality Quality

              faster-rcnn-resnet has 0 bugs and 0 code smells.

            kandi-Security Security

              faster-rcnn-resnet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              faster-rcnn-resnet code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              faster-rcnn-resnet is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              faster-rcnn-resnet releases are not available. You will need to build from source code and install.
              faster-rcnn-resnet has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              faster-rcnn-resnet saves you 1845 person hours of effort in developing the same functionality from scratch.
              It has 4072 lines of code, 227 functions and 47 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-resnet and discovered the below as its top functions. This is intended to give you an instant insight into faster-rcnn-resnet implemented functionality, and help decide if they suit your requirements.
            • List of roidb
            • Return roidb handler
            • Setup the image
            • Reshape the object
            • Append flipped images
            • Return the widths of each image
            • Override build extensions
            • Overrides the cuda compiler
            • Locate CUDA
            • Find a file in a search path
            • Load configuration from a file
            • Recursively merge b into b
            • Turn on competition mode
            • Add a path to sys path
            • Parse command line arguments
            • Get an imdb dataset
            • Forward pixels from bottom to bottom
            • Create a config dictionary from a list
            • Set the proposal method
            Get all kandi verified functions for this library.

            faster-rcnn-resnet Key Features

            No Key Features are available at this moment for faster-rcnn-resnet.

            faster-rcnn-resnet Examples and Code Snippets

            No Code Snippets are available at this moment for faster-rcnn-resnet.

            Community Discussions

            Trending Discussions on faster-rcnn-resnet

            Unable to load pre-trained model checkpoint with TensorFlow Object Detection API

            QUESTION

            Unable to load pre-trained model checkpoint with TensorFlow Object Detection API
            Asked 2021-Apr-17 at 10:33

            Similar to this question:

            Where can I find model.ckpt in faster_rcnn_resnet50_coco model? (this solution doesn't work for me)

            I have downloaded the ssd_resnet152_v1_fpn_1024x1024_coco17_tpu-8 with the intention of using it as a starting point. I am using the sample model configuration associated with that model in the TF model zoo.

            I am only changing the num classes and paths for tuning, training and eval.

            With:

            ...

            ANSWER

            Answered 2021-Apr-17 at 10:33

            Try changing the fine_tune_checkpoint path in the config file to something like path_to_folder/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0

            And in your training command, set the model_dir flag to just point to the model directory, don't include training, kind of like --model_dir=/ssd_resnet152_v1_fpn_1024x1024_coco17_tpu-8

            Source

            Just change the backslashes to forward-slashes, since you're on windows

            Source https://stackoverflow.com/questions/67118189

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

            Vulnerabilities

            No vulnerabilities reported

            Install faster-rcnn-resnet

            The usage is similar to py-faster-rcnn. We'll call the directory that you cloned faster-rcnn-resnet ROOT.
            Clone this repository
            Clone the modified caffe-fast-rcnn
            Build Cython module
            Build Caffe

            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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