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This GitHub repository contains the code we developed for our final project at Centrale Marseille.

Author: Cléa Han, Yanis Labeyrie, Adrien Zabban

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This project focuses on the analysis of blood traces within the scope of the work of forensic expert Philippe Esperança. The objective is to develop an artificial intelligence to assist and streamline the analysis of crime scenes involving blood.

Philippe Esperança's work on blood trace analysis has resulted in the classification of these traces into 18 distinct classes, listed in the table below. Our goal is to create a deep learning model capable of predicting the class for an image of a blood trace.

Classes	Classes
Passive Traces Models	Droplet Models
Contact Transfer Model	Sliding Transfer Model
Contact Alteration Model	Sliding Alteration Model
Accumulation Model	Flow Model
Volume Fall Model	Propelled Blood Model
Ejection Model	Impacted Volume Model
Impregnation Model	Interruption Zone Model
Impact Model	Impact Focal Point Model
Gravitational Trace Model	Expired Blood Model

Installation

To run the code you need python (We use python 3.10.11) and packages that is indicate in requirements.txt. You can run the following code to install all packages in the correct versions:

pip install -r requirements.txt

Inference

To perform an inference, execute the code run_infer.py with the following arguments:

Command	Description	Default Value
-d	Path to the folder containing the images to predict
-m	Path to the model to use	logs/retrain_resnet_allw_img256_2
-o	Path where the results will be saved (creating this folder if necessary)	Subfolder "inference_results" in the data directory
-s	Option to generate saliency map (true or false)	true

You can use python run_infer.py -h for this documentation. Example of code execution:

python run_infer.py -d data/images_to_infer -m logs/retrain_resnet_allw_img256_2 -o data/output -s false

Use Streamlit

You can also perform an inference with streamlit by using run_app.bat if you use Windows, or run this commende line:

streamlit run streamlit/streamlit_app.py

Training (validate and testing) new models

Config

In the file config.yaml, you can specify all details about your model such as data augmentation, model architecture, hyperparameters, batch size, learning rate, etc. Once your training is completed, a copy of this configuration will be saved in the logs directory along with the model weights, its performance metrics, and learning curves.

Data

For the code to function properly, the data for training, validation, or testing must be organized into subfolders named respectively train_256, val_256, and test_256, where 256 represents the image size (change the names of the subfolders accordingly). These subfolders should be grouped within a single directory, which you will specify in the file config.yaml under data.path.

For example:

data:                                 # data parameters
  path: data/data_labo                # path to the data
  real_data_path: data/data_real      # path to the real data
  image_size: 256                     # size of the images

In this example, the subfolders train_256, val_256, and test_256 containing laboratory images and real data respectively are within the directory data/data_labo and data/data_real.

Run the code

To perform training, validation, testing, or even launch random search, you should use the file main.py. You can run python main.py -h for more information.

Here are examples of launching training with the default configuration from config:

python main.py --mode train

And an example of testing the resnet_allw_img256_2 model without measuring the saliency map metrics:

python main.py --mode test --path logs/resnet_allw_img256_2 --run_saliency_metrics false

Random search et Grid search

To conduct a random search or a grid search to find the best hyperparameters, you need to create a file named search.yaml in the config folder with the parameters you want to test. For example, you can test finding the best learning rates and the alpha parameter for the adversarial model. Copy the following example into search.yaml:

learning:
  learning_rate: [0.01, 0.005, 0.001, 0.005, 0.0001]
  adv:
    learning_rate_adversary: [0.01, 0.005, 0.001, 0.005, 0.0001]
    alpha: [0.001, 0.1, 0.5, 1, 2, 5, 10, 100]

Then, to launch 20 training runs:

python main.py --mode random_search --num_run 20

If you want to test all combinations, use grid search instead of random search. This will create a directory in logs containing all your experiments. You will also have a summary table of the performance for each experiment in a CSV file.

Our strategy

Our strategy is described in the report (which is in French) that we invite you to read to better understand what we have done. Unfortunately, we had to remove the images from the report because they are confidential. You can use the table below, which provides the correspondence between the names given in this report and the names given in this repository.

Models name in the report	Model name in this repositoy
LP ResNet	resnet_img256_0
FT LP ResNet	retrain_resnet_img256_0
AWL ResNet	resnet_allw_img256_2
FT AWL ResNet	retrain_resnet_allw_img256_2
Adversarial	adv_img256_1