FGVC | Flow-edge Guided Video Completion | Code Inspection library

 by   vt-vl-lab Python Version: Current License: Non-SPDX

kandi X-RAY | FGVC Summary

kandi X-RAY | FGVC Summary

FGVC is a Python library typically used in Code Quality, Code Inspection applications. FGVC has no bugs, it has no vulnerabilities and it has medium support. However FGVC build file is not available and it has a Non-SPDX License. You can download it from GitHub.

[ECCV 2020] Flow-edge Guided Video Completion

            kandi-support Support

              FGVC has a medium active ecosystem.
              It has 1481 star(s) with 244 fork(s). There are 68 watchers for this library.
              It had no major release in the last 6 months.
              There are 12 open issues and 50 have been closed. On average issues are closed in 94 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of FGVC is current.

            kandi-Quality Quality

              FGVC has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              FGVC has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              FGVC releases are not available. You will need to build from source code and install.
              FGVC 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.
              FGVC saves you 2280 person hours of effort in developing the same functionality from scratch.
              It has 5120 lines of code, 263 functions and 37 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed FGVC and discovered the below as its top functions. This is intended to give you an instant insight into FGVC implemented functionality, and help decide if they suit your requirements.
            • Calculate flowNN gradient
            • Get the flowNN of the image .
            • Calculate video completion .
            • Train the model .
            • Runs the flow completion .
            • Updates the metrics .
            • Solve Poisson polynomial problem .
            • Calculate flow .
            • Calculates the gradient of the flow source frame .
            • Constructs an equation for an equation .
            Get all kandi verified functions for this library.

            FGVC Key Features

            No Key Features are available at this moment for FGVC.

            FGVC Examples and Code Snippets

            copy iconCopy
            # Stage 1: training with CUB dataset
            python3.6 pretrain_cub.py --dataset cub --exp_name pretrain_cub  --gpu_id 0 --config ./config/pretrain_cub.json 
            # Stage 2: training with Birds-to-Words and NABirds dataset alternately
            python3.6 pretrain.py --da  
            fast-bird-part-localization,Training a new head detector,Testing
            Jupyter Notebookdot img2Lines of Code : 12dot img2License : Permissive (MIT)
            copy iconCopy
            import sys
            from fast_bird_part_localization import settings
            import caffe
            from fast_bird_part_localization import detector
            fbp = detector('/path/to/project/models/he  
            PyTorch FGVC Dataset,Usage
            Pythondot img3Lines of Code : 2dot img3License : Permissive (MIT)
            copy iconCopy
            train_dataset = Cub2011('./cub2011', train=True, download=False)
            test_dataset = Cub2011('./cub2011', train=False, download=False)

            Community Discussions


            Why does Anaconda install pytorch cpuonly when I install cuda?
            Asked 2022-Mar-23 at 20:46

            I have created a Python 3.7 conda virtual environment and installed the following packages using this command:

            conda install pytorch torchvision torchaudio cudatoolkit=11.3 matplotlib scipy opencv -c pytorch

            They install fine, but then when I come to run my program I get the following error which suggests that a CUDA enabled device is not found:



            Answered 2022-Feb-18 at 14:52

            I beleive I had the following things wrong that prevented me from using Cuda. Despite having cuda installed the nvcc --version command indicated that Cuda was not installed and so what I did was add it to the path using this answer.

            Despite doing that and deleting my original conda environment and using the conda install pytorch torchvision torchaudio cudatoolkit=11.3 matplotlib scipy opencv -c pytorch command again I still got False when evaluating torch.cuda.is_available().

            I then used this command conda install pytorch torchvision torchaudio cudatoolkit=10.2 matplotlib scipy opencv -c pytorch changing cudatoolkit from verison 11.3 to version 10.2 and then it worked!

            Now torch.cuda.is_available() evaluates to True

            Unfortunately, Cuda version 10.2 was incompatible with my RTX 3060 gpu (and I'm assuming it is not compatible with all RTX 3000 cards). Cuda version 11.0 was giving me errors and Cuda version 11.3 only installs the CPU only versions for some reason. Cuda version 11.1 worked perfectly though!

            This is the command I used to get it to work in the end: pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

            Source https://stackoverflow.com/questions/71162459

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


            No vulnerabilities reported

            Install FGVC

            You can remove the --seamless flag for a faster processing time.
            Object removal:
            FOV extrapolation:


            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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