REARRANGE

Author: oflucas

.gitignore

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+data/
+models/
+*.record
+.ipynb_checkpoints/

README.md

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+
 [//]: # (Image References)
 [left0000]: ./examples/left0000.jpg
 [left0003]: ./examples/left0003.jpg
@@ -9,148 +10,51 @@
 [real0000]: ./examples/real0000.png
 [real0140]: ./examples/real0140.png
 [real0701]: ./examples/real0701.png
-[sim0003]: ./examples/sim0003.png
-[sim0011]: ./examples/sim0011.png
-[sim0027]: ./examples/sim0027.png
 
 # Traffic Light Detection and Classification with TensorFlow Object Detection API
----
 
-#### A brief introduction to the project is available [here](https://medium.com/@Vatsal410/traffic-light-detection-tensorflow-api-c75fdbadac62)
+The project is forked from https://github.com/coldKnight/TrafficLight_Detection-TensorFlowAPI.git
 
----
+A brief introduction to the project is available [here](https://medium.com/@Vatsal410/traffic-light-detection-tensorflow-api-c75fdbadac62)
 
-AWS AMI with all the software dependencies like TensorFlow and Anaconda (in the community AMIs) - `udacity-carnd-advanced-deep-learning`
 
 ### Get the dataset
 
 [Drive location](https://drive.google.com/file/d/0B-Eiyn-CUQtxdUZWMkFfQzdObUE/view?usp=sharing)
 
-### Get the models
-
-Do `git clone https://github.com/tensorflow/models.git` inside the tensorflow directory
 
-Follow the instructions at [this page](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) for installing some simple dependencies.
+### Get the tensorflow models lib
 
-**All the files have to be kept inside the `tensorflow/models/research/` directory - data/, config/, data_conversion python files, .record files and utilitites/ ,etc.**
+Do `git clone https://github.com/tensorflow/models.git` and update directory in .sh files
 
+Follow the instructions at [this page](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) for installing some simple dependencies.
 
-### Location of pre-trained models:
+Location of pre-trained models:
 [pre-trained models zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md)
 
-Download the required model tar.gz files and untar them into `/tensorflow/models/research/` directory with `tar -xvzf name_of_tar_file`.
-
-### Creating TFRecord files:
-
-`python data_conversion_udacity_sim.py --output_path sim_data.record`
-
-`python data_conversion_udacity_real.py --output_path real_data.record`
-
----
-
-## Commands for training the models and saving the weights for inference.
-
-## Using Faster-RCNN model
-
-### For Simulator Data
-
-#### Training
-
-`python object_detection/train.py --pipeline_config_path=config/faster_rcnn-traffic-udacity_sim.config --train_dir=data/sim_training_data/sim_data_capture`
-
-#### Saving for Inference
-
-`python object_detection/export_inference_graph.py --pipeline_config_path=config/faster_rcnn-traffic-udacity_sim.config --trained_checkpoint_prefix=data/sim_training_data/sim_data_capture/model.ckpt-5000 --output_directory=frozen_sim/`
-
-
-### For Real Data
-
-#### Training
-
-`python object_detection/train.py --pipeline_config_path=config/faster_rcnn-traffic_udacity_real.config --train_dir=data/real_training_data`
-
-#### Saving for Inference
-
-`python object_detection/export_inference_graph.py --pipeline_config_path=config/faster_rcnn-traffic_udacity_real.config --trained_checkpoint_prefix=data/real_training_data/model.ckpt-10000 --output_directory=frozen_real/`
-
----
-
-## Using Inception SSD v2
+Download the required model tar.gz files and untar them into `models/` directory with `tar -xvzf name_of_tar_file`.
 
-### For Simulator Data
 
-#### Training
-
-`python object_detection/train.py --pipeline_config_path=config/ssd_inception-traffic-udacity_sim.config --train_dir=data/sim_training_data/sim_data_capture`
-
-#### Saving for Inference
+### Creating TFRecord files:
 
-`python object_detection/export_inference_graph.py --pipeline_config_path=config/ssd_inception-traffic-udacity_sim.config --trained_checkpoint_prefix=data/sim_training_data/sim_data_capture/model.ckpt-5000 --output_directory=frozen_models/frozen_sim_inception/`
+`python data_conversion.py --input_yaml data/training_data/annotations_train.yaml --output_path data/train.record`
+`python data_conversion.py --input_yaml data/training_data/annotations_eval.yaml --output_path data/eval.record`
 
 
-### For Real Data
+## Using Faster-RCNN / Inception SSD v2 / MobileNet SSD v1 model
 
 #### Training
 
-`python object_detection/train.py --pipeline_config_path=config/ssd_inception-traffic_udacity_real.config --train_dir=data/real_training_data`
+`sh train.sh <faster_rcnn | ssd_inception | ssd_mobilene>`
 
-#### Saving for Inference
-
-`python object_detection/export_inference_graph.py --pipeline_config_path=config/ssd_inception-traffic_udacity_real.config --trained_checkpoint_prefix=data/real_training_data/model.ckpt-10000 --output_directory=frozen_models/frozen_real_inception/`
+#### Saving Weights for Inference
 
+`sh freeze.sh <faster_rcnn | ssd_inception | ssd_mobilene> <model checkpoint version num>`
 ---
 
-## Using MobileNet SSD v1
-(Due to some unknown reasons the model gets trained but does not save for inference. Ignoring this for now.)
-
-### For Simulator Data
-
-#### Training
-
-`python object_detection/train.py --pipeline_config_path=config/ssd_mobilenet-traffic-udacity_sim.config --train_dir=data/sim_training_data/sim_data_capture`
-
-#### Saving for Inference
-
-`python object_detection/export_inference_graph.py --pipeline_config_path=config/ssd_mobilenet-traffic-udacity_sim.config --trained_checkpoint_prefix=data/sim_training_data/sim_data_capture/model.ckpt-5000 --output_directory=frozen_models/frozen_sim_mobile/`
-
-
-### For Real Data
-
-#### Training
-
-`python object_detection/train.py --pipeline_config_path=config/ssd_mobilenet-traffic_udacity_real.config --train_dir=data/real_training_data`
-
-#### Saving for Inference
-
-`python object_detection/export_inference_graph.py --pipeline_config_path=config/ssd_mobilenet-traffic_udacity_real.config --trained_checkpoint_prefix=data/real_training_data/model.ckpt-10000 --output_directory=frozen_models/frozen_real_mobile/`
-
----
 
 **Inference results can be viewed using the TrafficLightDetection-Inference.ipynb or .html files.**
 
 ### Camera Image and Model's Detections      
 ![alt-text][left0000]
 ![alt-text][real0000]
-
-![alt-text][left0140]
-![alt-text][real0140]
-
-![alt-text][left0701]
-![alt-text][real0701]
-
-![alt-text][left0003]
-![alt-text][sim0003]
-
-![alt-text][left0011]
-![alt-text][sim0011]
-
-![alt-text][left0027]
-![alt-text][sim0027]
-
----
-
-#### Some useful links
-
-- [Uploading/Downloading files between AWS and GoogleDrive](http://olivermarshall.net/how-to-upload-a-file-to-google-drive-from-the-command-line/)
-
-- [Using Jupyter notebooks with AWS](https://medium.com/towards-data-science/setting-up-and-using-jupyter-notebooks-on-aws-61a9648db6c5)