Installation.

1. python 3.9
2. you can install libraries through requirements.txt pip install -r requirements.txt

data organiztion.

""" 
[Auto Generate Test Dataset]
DATASET Folder
    |   
    |----category1 -- img1.jpg
    |              -- img2.jpg 
    |              -- etc      
    |   
    |----category2 -- img1.jpg
    |              -- etc
    |         
    |----etc
"""

Generating data.

python generate_dataset_auto_create_testset.py --cfg config.yaml <data_folder>

Example: python generate_dataset_auto_create_testset.py --cfg config.yaml ./game_data

Result: train/val/test: 0.5/0.27/0.23

Method of testing/ prediction

We use 2 methods:

1. test.py output results with confusion matrixes and wrong match log (it's saved in check_wrong_match & wrong_match_images, the folders need to be generated mannualy).

python test.py --cfg config.yaml --model <models_path>

example: python test.py --cfg config.yaml --model ./experiments/legenddata/2023-04-11/mobilenetv2_035 /2023-04-11_22-09-52_default/train/model_best_2023-04-11_22-09-52_default.pth.tar

2. predict.py outputs results with your requirements.

python predict.py --cfg config.yaml --model <models_path>

python predict.py --cfg ./config.yaml --model ./experiments/legenddata/2023-04-11/mobilenetv2_035/ 2023-04-11_22-09-52_default/train/model_best_2023-04-11_22-09-52_default.pth.tar

You can check my outputs in results with trained models (efficientnet_b1_pruned/ mobilenetv2_035/ resnet18) that include the results of test.py (check_wrong_match) and predict.py (output.txt)

Link for data and experiment result:

https://drive.google.com/drive/folders/1lYmy7-diSegug3Yi--bETYJZyC3OYGni?usp=sharing