Comprehensive image classification library
Why image classification?
Image classification is one of the fundamental computer vision tasks that serves as the backbone for solving different computer vision problems.
How does this repository help?
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Distributed data_parallel training (Pytorch Distributed Data Parallel)
- Run multiple processes on different machines and various GPUs
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Three different initialization are supported.
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Hyperparamters are given as a
Yamlfile for each scenario. -
Creating folders named with the time of execution for the followings outputs:
- Training and test logs as text
- Initial and final checkpoints
- Training and test statistics as Numpy variables
- Tensorboard logs
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Four different learning rate schedulers are supported.
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Hyperparamters are given as a
Yamlfile for each scenario. -
All training and validation metrics are save for further analysis.
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Tensorboard events are logged in a separate folder categorized by the dataset and architecture.
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Every run file is given as bash file.
The primitive case
An example for training a fully connected network on MNIST dataset is the following:
python -m torch.distributed.launch --nproc_per_node=1 --master_port=$RANDOM main.py \
-a fc --dataname mnist --config mnist_fc_train --logterminal
The advanced case
An example for training a ResNet50 network on ImageNet1K dataset is the following:
python -m torch.distributed.launch --nproc_per_node=5 --master_port=$RANDOM main.py
-a resnet50 --dataname imagenet --config imagenet_resnet50_train