From 20b97d01963fd2e23d74dd2f5f1cebaff438c831 Mon Sep 17 00:00:00 2001 From: UchihaUdai Date: Tue, 30 Jul 2019 15:17:53 +0530 Subject: [PATCH] Update Documentation. --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 97f621b..2516312 100755 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Please do cite it if you find this project useful. :) - Cleaner and understandable code. - Replaced all manual editing with command line arguments. - Removed Bugs due to changes in names of the `operations` in the inception model. -- Code Tested on a dummy dataset of three classes on google colab. +- Code Tested on a **dummy dataset** of three classes on **google colab.** ## DataSet Used * [Argentinian Sign Language Gestures](http://facundoq.github.io/unlp/lsa64/). The dataset is made available strictly for academic purposes by the owners. Please read the license terms carefully and cite their paper if you plan to use the dataset. @@ -87,7 +87,7 @@ train_videos -h, --help show the help message and exit ``` -The code involves some hand segmentation (based on the data we used) for each frame. (You can remove that code if you are working on some other data set) +The code involves some **hand segmentation** (based on the data we used) for each frame. (You can remove that code if you are working on some other data set) #### Extracting frames form training videos @@ -157,7 +157,7 @@ For more information about the above command refer [here](https://codelabs.devel #### Approach 1 -- Each Video is represented by a sequence of `n` dimensional vectors (probability distribution or output of softmax) one for each frame. Here `n` is the number of classes. +- Each Video is represented by a sequence of `n` dimensional vectors **(probability distribution or output of softmax)** one for each frame. Here `n` is the number of classes. **On Training Data** @@ -177,7 +177,7 @@ For more information about the above command refer [here](https://codelabs.devel #### Approach 2 -- Each Video represented by a sequence of 2048 dimensional vectors (output of last Pool Layer) one for each frame +- Each Video represented by a sequence of 2048 dimensional vectors **(output of last Pool Layer)** one for each frame **On Training Data** @@ -198,7 +198,7 @@ For more information about the above command refer [here](https://codelabs.devel --test ``` - This will create a file `predicted-frames-GlobalPool-test.pkl` that will be used by RNN. + This will create a file `predicted-frames-GlobalPool-test.pkl` that will be used by **RNN**. ### 5. Train the RNN. @@ -235,7 +235,7 @@ For more information about the above command refer [here](https://codelabs.devel python3 rnn_train.py predicted-frames-final_result-train.pkl non_pool.model ``` -This will train the RNN model on the **softmax based representation** of gestures for 10 epochs and save the model with name `non_pool.model` in a folder named checkpoints. +This will train the RNN model on the **softmax based representation** of gestures for **10 epochs** and save the model with name `non_pool.model` in a folder named checkpoints. #### Approach 2 @@ -243,7 +243,7 @@ This will train the RNN model on the **softmax based representation** of gesture python3 rnn_train.py predicted-frames-GlobalPool-train.pkl pool.model ``` -This will train the RNN model on the **pool layer based representation** of gestures for 10 epochs and save the model with name `pool.model` in a folder named checkpoints. +This will train the RNN model on the **pool layer based representation** of gestures for **10 epochs** and save the model with name `pool.model` in a folder named checkpoints. ### 6. Test the RNN Model @@ -293,4 +293,4 @@ This will use the `pool.model` to predict the labels of the **pool layer based r Predictions and corresponding gold labels for each test video will be dumped in to **results.txt** -Happy Coding :) +Happy Coding <3