Train image classification on ImageNet
Use as many default commands as possible:
python3 classify.py train <data_folder> -a dla34
With more data settings:
python3 classify.py train <data_folder> -a dla34 --data-name imagenet \
--classes 1000 -j 4 --epochs 120 --start-epoch 0 --batch-size 256 \
--crop-size 224 --scale-size 256
If you want to train on a dataset that is not already defined in dataset.py, please specify a new data name and put info.json in the data folder. info.json contains a dictionary with required values mean and std, which are the mean and standard deviation of the images in the new dataset. A full set of options can be found in dataset.py. The other useful fields are eigval and eigvec, which are the eigen values and vectors for the image pixel variations in the dataset. A minimal info.json looks like:
{
"mean": [0.485, 0.456, 0.406],
"std": [0.229, 0.224, 0.225]
}
If the new dataset contains 2 classes, the command can start with:
python3 classify.py train <data_folder> -a dla34 --data-name new_data \
--classes 2
If you want to start your training with models pretrained on ImageNet and fine tune the model with learning rate 0.01, you can do
python3 classify.py train <data_folder> -a dla34 --data-name new_data \
--classes 2 --pretrained imagenet --lr 0.01
Segmentation and boundary prediction data format is the same as DRN.
To use --bn-sync, please include lib in PYTHONPATH.
Cityscapes
python3 segment.py train -d <data_folder> -c 19 -s 832 --arch dla102up \
--scale 0 --batch-size 16 --lr 0.01 --momentum 0.9 --lr-mode poly \
--epochs 500 --bn-sync --random-scale 2 --random-rotate 10 \
--random-color --pretrained-base imagenet
bn-sync is not necessary for CamVid and boundaries with 12GB GPU memory.
CamVid
python3 segment.py train -d <data_folder> -c 11 -s 448 --arch dla102up \
--scale 0 --batch-size 16 --epochs 1200 --lr 0.01 --momentum 0.9 \
--step 800 --pretrained-base imagenet --random-scale 2 --random-rotate 10 \
--random-color --save-feq 50
BSDS
python3 segment.py train -d <data_folder> -c 2 -s 416 --arch dla102up \
--scale 0 --batch-size 16 --epochs 1200 --lr 0.01 --momentum 0.9 \
--step 800 --pretrained-base imagenet --random-rotate 180 --random-color \
--save-freq 50 --edge-weight 10 --bn-sync
PASCAL Boundary
python3 segment.py train -d <data_folder> -c 2 -s 480 --arch dla102up \
--scale 0 --batch-size 32 --epochs 400 --lr 0.01 --momentum 0.9 \
--step 200 --pretrained-base imagenet --random-rotate 10 --random-color \
--save-freq 25 --edge-weight 10
How many GPUs does the program require for training?
We tested all the training on GPUs with at least 12 GB memory. We usually tried to use fewest GPUs for the batch sizes. So the actually number of required GPUs is different between models, depending on the model sizes. Some model training may require 8 GPUs, such as training dla102up on Cityscapes dataset.
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