From 4ba5f42e8af0100d65d62a5a059269ad108eb0f8 Mon Sep 17 00:00:00 2001 From: Brandon Lee Date: Wed, 10 Aug 2022 12:01:06 -0400 Subject: [PATCH] enable using stitched dataset --- README.md | 2 +- train_model.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b9ef6802..d0f238b7 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ For detailed explanation, please refer to [How to generate a dataset for custom ### Training and Running a Model 1. Source the relevant environment variables for training the `res8` model: `source envs/res8.env`. -2. Train the model: `python -m training.run.train -i datasets/fire/positive datasets/fire/negative --model res8 --workspace workspaces/fire-res8`. +2. Train the model: `python -m training.run.train -i datasets/fire/positive datasets/fire/negative --model res8 --workspace workspaces/fire-res8`. It's recommended to also use `--use-stitched-datasets` if the training datasets are small. 3. For the CLI demo, run `python -m training.run.demo --model res8 --workspace workspaces/fire-res8`. `train_model.sh` is also available which encaspulates individual command into a single bash script diff --git a/train_model.sh b/train_model.sh index 3e6482ca..560666d7 100755 --- a/train_model.sh +++ b/train_model.sh @@ -23,4 +23,4 @@ done source ${ENV_FILE_PATH} echo ">>> training a model for ${VOCAB}; model will be stored at ${WORKSPACE_PATH}" -time python -m training.run.train --model ${MODEL_TYPE} --workspace "${WORKSPACE_PATH}" ${DATASET_ARGUMENT} +time python -m training.run.train --model ${MODEL_TYPE} --workspace "${WORKSPACE_PATH}" ${DATASET_ARGUMENT} --use-stitched-datasets