J.A.R.V.I.S.

This is a repo for the Computer Vision project, which is a part of the DSBA 2020-2022. Below the results for- and instructions to recreate the algorithms we built and trained.

Index

- Darknet_Jarvis:

Contains the necessary setup files and code for the evaluation of Darknet YoloV4. For the specifics on how to recreate our results refer to the dedicate section "Darknet (Yolov4) results"

- duplicate_images:

Scripts to find duplicate images in the dataset

- yolov5:

Contains the necessary setup files and code for the evaluation of Pytorch YoloV5. For the specifics on how to recreate our results refer to the dedicate section "Pytorch (Yolov5) results"

- descriptive_stats.ipynb:

Notebook with all the descriptive statistics on our datasets

- merging_workspaces.py:

Script to merge multiple Roboflow workspace into a single directory

Darknet (Yolov4) results

Label	Avg. IOU
Adidas	0.802068
Apple Inc-	0.701490
Coca-Cola	0.783254
Emirates	0.728129
Hard Rock Cafe	0.727069
Mercedes-Benz	0.757376
NFL	0.772498
Nike	0.661942
Pepsi	0.816151
Puma	0.737169
Starbucks	0.829903
The North Face	0.766542
Toyota	0.784606
Under Armour	0.668946

In order to reproduce the results from Darknet Yolov4, please:

1. Create a folder to test our results.
2. Download the trained weights yolo4_trained.weights from Google Drive.
3. Git clone darknet from https://github.com/AlexeyAB/darknet
4. Git clone our repo. Compile darknet using the Makefile provided by us at JARVIS/Darknet_Jarvis/setup/Makefile. (You will need to replace the original Makefile by AlexeyAB with ours.)
5. Go to Roboflow and download the re-annotated test set with the YOLO Darknet format into darknet/data. This will create a folder named test, which has the images and their labels, which we compare our predictions against.
6. From the darknet folder, run ./darknet detector test ../JARVIS/Darknet_Jarvis/setup/obj.data ../JARVIS/Darknet_Jarvis/setup/yolo-obj.cfg ../yolo4_trained.weights -dont_show -ext_output < ../JARVIS/Darknet_Jarvis/setup/test.txt > result.txt
7. In the Darknet_Jarvis folder, run python results_evaluation/pred_to_CSV.py "../../darknet/result.txt".
8. In the Darknet_Jarvis folder, run python results_evaluation/GT_to_csv.py "../../darknet/data/test/* .txt".
9. Follow the Jupyter notebook Darknet results to verify the results.

Pytorch (Yolov5) results

Version L

Label	Avg. IOU
Adidas	0.871891
Apple Inc-	0.840646
Coca-Cola	0.893000
Emirates	0.858639
Hard Rock Cafe	0.843841
Mercedes-Benz	0.841095
NFL	0.873054
Nike	0.827050
Pepsi	0.902139
Puma	0.861367
Starbucks	0.889383
The North Face	0.850859
Toyota	0.854315
Under Armour	0.808131

Version X

Label	Avg. IOU
Adidas	0.870254
Apple Inc-	0.845880
Coca-Cola	0.891550
Emirates	0.863402
Hard Rock Cafe	0.847951
Mercedes-Benz	0.832241
NFL	0.864726
Nike	0.820951
Pepsi	0.905823
Puma	0.859121
Starbucks	0.886776
The North Face	0.852864
Toyota	0.854814
Under Armour	0.820642

In order to reproduce the results from Pytorch Yolov5 L and X, please:

1. Create a conda environment in your machine (with python 3.8) conda create -n "name" python=3.8 conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

2. Clone the github repository of yolov5 and install requirements git clone https://github.com/ultralytics/yolov5 cd yolov5 pip install -r requirements.txt

3. Inside yolov5 folder download the pre-trained weights (choose version (l or x l is faster x is better) and adjust image size if different from roboflow) here:
   python detect.py --weights yolov5l.pt --img 640 (the weights present are in the google folder) python detect.py --weights yolov5x.pt --img 640

4. Insert your train, test, valid folder inside the yolov5 folder where each train, test, and valid contains subfolders of images and labels. Make sure that the subfolder of labels contains txt where the label is a number and not a string.

5. Create a new .yaml file with data specification

6. To train: python train.py --img 640 --batch 12 --epochs 20 --data data/data.yaml --weights yolov5x.pt

7. The trained weights are present here YOLO v5X, YOLO v5L

8. The trained weights are then used to test for new images with the following command : python detect.py --source test_folder/test/images --weights weights_folder/best.pt --img 640 --save-txt --save-conf

To see the analysis of performance, go to performance_analysis_visualizations.ipynb and the testing we did on the noise dataset, go to testing_on_noise.ipynb