Merge branch 'main' into docs

Author: damaggu

README.md

@@ -33,7 +33,7 @@ In order to pull the docker image you would first need to install `docker` and `
 After installing `docker` and `nvidia-docker2` you can download the SIPEC image by executing:
 
 ```
-docker pull chadhat/sipec:tf2
+docker pull sipec/sipec:tf2_v1
 ```
 
 **Note:** In order to run docker without `sudo` you would need to create a docker group and add your user to it. Please follow the instructions on: [https://docs.docker.com/engine/install/linux-postinstall/](https://docs.docker.com/engine/install/linux-postinstall/) 
@@ -92,20 +92,20 @@ If your system has multiple GPUs, the `--gpu` flag allows you to run a script on
 
 Here are some example command line usages of the pipeline
 <pre><code>
-docker container run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec:main_tf2 
+docker run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec/sipec:tf2_v1 
+                      segmentation.py --cv_folds 0 --gpu 0 --frames /home/user/data/mouse_segmentation_4plex_merged/frames --annotations /home/user/data/mouse_segmentation_4plex_merged/merged.json
+
+docker run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec/sipec:tf2_v1 
                      classification_comparison.py --gpu 0 --config_name behavior_config_final --random_seed 1 --output_path=/home/user/results
 
-docker container run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec:main_tf2 
-                      poseestimation.py --gpu 0 --operation train_mouse --output_path=/home/user/results/
+docker run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec/sipec:tf2_v1 
+                      poseestimation.py --gpu 0 --results_sink /home/user/results --dlc_path /home/user/data/mouse_pose/OFT/labeled-data/ --segnet_path /home/user/data/pretrained_networks/mask_rcnn_mouse_0095.h5 --config poseestimation_config_test
 
-docker container run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec:main_tf2 
-                      behavior.py --gpu 0 --annotations /home/user/data/20180124T113800-20180124T115800_0.csv --video /home/user/data/fullvids_20180124T113800-20180124T115800_%T1_0.mp4 --output_path /home/user/results
+docker run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec/sipec:tf2_v1 
+                      full_inference.py --gpu 0 --species mouse --video /home/user/data/full_inference_posenet_25_June/animal1234_day1.avi --posenet_path /home/user/data/pretrained_networks/posenet_mouse.h5  --segnet_path /home/user/data/pretrained_networks/mask_rcnn_mouse_0095.h5 --max_ids 4 --results_sink /home/user/results/full_inference     
 
-docker container run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec:main_tf2 
-                      full_inference.py --gpu 0 --species mouse --video /home/user/data/full_inference_and_vis_data/animal5678_day2.avi --segnet_path "/home/user/data/full_inference_and_vis_data/mask_rcnn_mouse_0095.h5" --max_ids 4 --results_sink /home/user/results/full_inference
+<b>Coming soon</b>: behavior.py
 
-docker container run -v "<b>RESULTS_PATH</b>:/home/user/results" --runtime=nvidia --rm sipec:main_tf2 
-                      segmentation.py --cv_folds 0 --gpu 0 --frames /home/user/data/mouse_segmentation_single/annotated_frames --annotations /home/user/data/mouse_segmentation_single/mouse_top_segmentation.json
 </pre></code>
 
 Where, **RESULTS_PATH** is the path on your machine where you would like to write the results.