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README.md

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@@ -131,7 +131,7 @@ git clone https://github.com/msracver/FCIS.git
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2. Two config files have been provided so far: FCIS@COCO with OHEM and FCIS@VOC without OHEM. We use 8 and 4 GPUs to train models on COCO and on VOC, respectively.
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3. To perform experiments, run the python scripts with the corresponding config file as input. For example, to train and test FCIS on COCO with ResNet-v1-101, use the following command
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```
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python experiments/fcis/fcis_end2end_train_test.py --cfg experiments/fcis/cfgs/resnet_v1_101_coco_trainval_fcis_end2end_ohem.yaml
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python experiments/fcis/fcis_end2end_train_test.py --cfg experiments/fcis/cfgs/resnet_v1_101_coco_fcis_end2end_ohem.yaml
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```
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A cache folder would be created automatically to save the model and the log under `output/fcis/coco/` or `output/fcis/voc/`.
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4. Please find more details in config files and in our code.

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