FGVC | Fine-Grained Visual Classification for Plants & Flowers | Machine Learning library
kandi X-RAY | FGVC Summary
kandi X-RAY | FGVC Summary
Train ResNet to recognize over 998 categories (997 plants + 1 others) for Kaggle Competition. Deep Learning for Plants Disease Recognition for AI Challenger 2018 Competition.
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Top functions reviewed by kandi - BETA
- Run plant type validation
- Train a model using FFT
- Forward transform function
- Computes the phi of the phi
- Copy files from_target to to to_target_dir
- Creates a directory if it does not exist
- Infer predictions from an image file
- Load data
- Infer from an image
- Infer from input image
- Run FashionMNIST
- Concatenates an image using BIDS
- Convert CSV file to JSON format
- Get access token
- Runs a resnet
- Evaluate model inference
- Train a model
FGVC Key Features
FGVC Examples and Code Snippets
Community Discussions
Trending Discussions on FGVC
QUESTION
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:
...ANSWER
Answered 2022-Feb-18 at 14:52I 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
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install FGVC
You can use FGVC 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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