add ML pipeline instructions

README.md

@@ -1,4 +1,4 @@
-# Traffic-Sign Recognition and Classification
+# Objects Recognition and Classification
 ## ME499 - Independent Project, Winter 2021
 Yael Ben Shalom, Northwestern University.
 

traffic_signs_detection/traffic_signs_classification/README.md

@@ -1,7 +1,7 @@
 # Traffic-Sign Classification
 ## ME499 - Independent Project, Winter 2021
 Yael Ben Shalom, Northwestern University.<br>
-This module is a part of a [Traffic-Sign Recognition and Classification](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification) project.
+This module is a part of a [Objects Recognition and Classification](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification) project.
 
 
 ## Module Overview
@@ -14,7 +14,7 @@ I based my program on the German Traffic Sign Recognition Benchmark ([GTSRB](htt
 
 1. Clone the repository, using the following commands:
     ```
-    git clone https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/tree/master/traffic_signs_detection/traffic_signs_classification
+    git clone https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/tree/master/traffic_signs_detection/traffic_signs_classification
     ```
 
 2. Download the dataset and extract it into `./data` directory. The dataset can be found on the [INI Benchmark website](https://benchmark.ini.rub.de/?section=gtsrb&subsection=news), or downloaded directly through [here](https://s3-us-west-1.amazonaws.com/udacity-selfdrivingcar/traffic-signs-data.zip).
@@ -45,15 +45,15 @@ Run the classification program:
 <br>The program output when running it on the example image:
 
 The loss plot:<br>
-    ![Loss Graph](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/blob/master/traffic_signs_detection/traffic_signs_classification/images/Losses%20(100%20Epochs).png)
+    ![Loss Graph](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/traffic_signs_detection/traffic_signs_classification/images/Losses%20(100%20Epochs).png)
 
 
 The accuracy plot:<br>
-    ![Accuracy Graph](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/blob/master/traffic_signs_detection/traffic_signs_classification/images/Accuracy%20(100%20Epochs).png)
+    ![Accuracy Graph](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/traffic_signs_detection/traffic_signs_classification/images/Accuracy%20(100%20Epochs).png)
 
 
 The output image (with the correct prediction):<br>
-    ![Accuracy Graph](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/blob/master/traffic_signs_detection/traffic_signs_classification/images/Image_Classification.png)
+    ![Accuracy Graph](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/traffic_signs_detection/traffic_signs_classification/images/Image_Classification.png)
 
 
 ## Dataset

traffic_signs_detection/traffic_signs_recognition/README.md

@@ -1,23 +1,23 @@
 # Traffic-Sign Recognition
 ## ME499 - Independent Project, Winter 2021
 Yael Ben Shalom, Northwestern University.<br>
-This module is a part of a [Traffic-Sign Recognition and Classification](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification) project.
+This module is a part of a [Objects Recognition and Classification](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification) project.
 
 
 ## Module Overview
 In this module, I trained a neural network to detect and classify different traffic signs using PyTorch, YoloV5 and OpenCV.<br>
 I based my program on the German Traffic Sign Detection Benchmark ([GTSBB](https://benchmark.ini.rub.de/gtsdb_news.html)) dataset.
 
 An example of output video:<br>
-    ![Traffic-Sign Recognition](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/blob/master/traffic_signs_detection/traffic_signs_recognition/images/traffic-sign.gif?raw=true)
+    ![Traffic-Sign Recognition](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/traffic_signs_detection/traffic_signs_recognition/images/traffic-sign.gif?raw=true)
 
 
 ## User Guide
 ### Program Installation
 
 1. Clone the repository, using the following commands:
     ```
-    git clone https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/tree/master/traffic_signs_detection/traffic_signs_recognition
+    git clone https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/tree/master/traffic_signs_detection/traffic_signs_recognition
     ```
 
 2. Download the dataset and extract it into `./data` directory. The dataset can be found on the [INI Benchmark website](https://benchmark.ini.rub.de/?section=gtsrb&subsection=news), or downloaded directly through [here](https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html).
@@ -30,7 +30,7 @@ An example of output video:<br>
 Run the recognition program:
 1. To train the program on the dataset and test it on a specific video, copy the video into `./input_video` directory and run the following command from the root directory:
     ```
-    python code/recognition.py --video <video-name>
+    python code/recognition_videos.py --video <video-name>
     ```
     Where `<video-name>` is the name of the video (including video type).
 

trash_detection/trash_classification/README.md

@@ -1,7 +1,7 @@
 # Trash Classification
 ## ME499 - Independent Project, Winter 2021
 Yael Ben Shalom, Northwestern University.<br>
-This module is a part of a [Traffic-Sign Recognition and Classification](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification) project.
+This module is a part of a [Objects Recognition and Classification](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification) project.
 
 
 ## Module Overview
@@ -14,7 +14,7 @@ I based my program on the [Garbage Classification Dataset](https://www.kaggle.co
 
 1. Clone the repository, using the following commands:
     ```
-    git clone https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/tree/master/trash_recognition/trash_classification
+    git clone https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/tree/master/trash_recognition/trash_classification
     ```
 
 2. Extract the dataset located in the `./data` directory.
@@ -45,15 +45,15 @@ Run the classification program:
 <br>The program output when running it on the example image:
 
 The loss plot:<br>
-    ![Loss Graph](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/blob/master/trash_recognition/trash_classification/images/Losses%20(100%20Epochs).png)
+    ![Loss Graph](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/trash_detection/trash_classification/images/Losses%20(100%20Epochs).png)
 
 
 The accuracy plot:<br>
-    ![Accuracy Graph](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/blob/master/trash_recognition/trash_classification/images/Accuracy%20(100%20Epochs).png)
+    ![Accuracy Graph](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/trash_detection/trash_classification/images/Accuracy%20(100%20Epochs).png)
 
 
 The output image (with the correct prediction):<br>
-    ![Accuracy Graph](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/blob/master/trash_recognition/trash_classification/images/Image_Classification.png)
+    ![Accuracy Graph](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/trash_detection/trash_classification/images/Image_Classification.png)
 
 
 ## Dataset

trash_detection/trash_recognition/README.md

@@ -1,21 +1,24 @@
 # Trash Recognition
 ## ME499 - Independent Project, Winter 2021
 Yael Ben Shalom, Northwestern University.<br>
-This module is a part of a [Traffic-Sign Recognition and Classification](https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification) project.
+This module is a part of a [Objects Recognition and Classification](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification) project.
 
 
 ## Module Overview
 In this module I trained a neural network to detect and classify different recycable objects using PyTorch, YoloV5 and OpenCV.<br>
 I based my program on the Trash Annotations in Context ([TACO](http://tacodataset.org/)) dataset.<br>
 The TACO dataset contains ~60 different classes, but in this project I only detect 10 different objects.
 
+An example of output video:<br>
+    ![Trash Recognition](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/trash_detection/trash_recognition/images/real-time%20detection2.gif)
+
 
 ## User Guide
 ### Program Installation
 
 1. Clone the repository, using the following commands:
     ```
-    git clone https://github.com/YaelBenShalom/Traffic-Sign-Recognition-and-Classification/tree/master/trash_recognition/trash_recognition
+    git clone https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/tree/master/trash_recognition/trash_recognition
     ```
 
 2. Download the dataset and extract it into `./data` directory. The dataset can be found on the [TACO dataset website](http://tacodataset.org/), or downloaded directly through [here](https://www.kaggle.com/kneroma/tacotrashdataset/download).
@@ -70,6 +73,9 @@ After training the model, run the recognition program:
     Where `<video-name>` is the name of the video (including video type).
 
 
+    ![Trash Recognition](https://github.com/YaelBenShalom/Objects-Recognition-and-Classification/blob/master/trash_detection/trash_recognition/images/detecting_baxter.gif)
+
+
 ## Dataset
 TACO is a growing image dataset of waste in the wild. It contains images of litter taken under diverse environments: woods, roads and beaches. These images are manually labeled and segmented according to a hierarchical taxonomy to train and evaluate object detection algorithms.<br>
 The dataset currently contain 60 different classes.<br>