Move doc pictures to imgs dir

docs/GETTING_STARTED.md

@@ -242,7 +242,7 @@ We provide lots of useful tools under `tools/` directory.
 
 You can plot loss/top-k acc curves given a training log file. Run `pip install seaborn` first to install the dependency.
 
-![acc_curve_image](./acc_curve.png)
+![acc_curve_image](imgs/acc_curve.png)
 
 ```shell
 python tools/analyze_logs.py plot_curve ${JSON_LOGS} [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]

docs/TECHNICAL_DETAILS.md

@@ -21,7 +21,7 @@ Each operation takes a dict as input and also output a dict for the next transfo
 
 A typical pipeline is shown in the following figure.
 With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange).
-![pipeline figure](./datapipeline.png)
+![pipeline figure](imgs/data_pipeline.png)
 
 The operations are categorized into data loading, pre-processing, formatting.
 

docs/tutorials/data_pipeline.md

@@ -13,9 +13,9 @@ The data preparation pipeline and the dataset is decomposed. Usually a dataset
 defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict.
 A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next operation.
 
-We present a typical pipeline in the following figure. The blue blocks are pipeline operations. 
+We present a typical pipeline in the following figure. The blue blocks are pipeline operations.
 With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange).
-![pipeline figure](data_pipeline.png)
+![pipeline figure](../imgs/data_pipeline.png)
 
 The operations are categorized into data loading, pre-processing and formatting.