v2.1 [16/02/2022]

Meteor classification using Neural Networks

Author: Ibrahim Oulad Amar

Final degree project which consists of creating a meteor classification system using Deep Learning techniques.

The classifier uses 256x256 images as input. The images shall be the MAXPIXEL ones in the FTP format. In this case
I used the data provided by the University of Western Ontario. I am thankful to researcher Denis Vida for his help
in obtaining these data.

The data was split in two sets, training (85%) and validation (15%). The model is a CNN + MaxPool + BatchNormalization
(total 12 layers) along with 3 fully connected layers. The total number of parameters is 49,449, of which 512 are
not-trainable. The model performance metrics are:

• Model Precision: 0.931 (93.1%)
• Model Recall: 0.941 (94.1%)
• Model F1 Score: 0.936

The model is the one defined in the file final_model_weights.h5 in the folder
meteor_sort/results/weights/.

If you have any suggestions or questions don't hesitate to reach me at my email: ibrahim.ouladamar@gmail.com

v2.0 [06/02/2022]

Meteor classification using Neural Networks

Author: Ibrahim Oulad Amar

Final degree project which consists of creating a meteor classification system using Deep Learning techniques.

The classifier uses 256x256 images as input. The images shall be the MAXPIXEL ones in the FTP format. In this case
I used the data provided by the University of Western Ontario. I am thankful to researcher Denis Vida for his help
in obtaining these data.

The data was split in two sets, training (85%) and validation (15%). The model is a CNN + MaxPool + BatchNormalization
(total 12 layers) along with 3 fully connected layers. The total number of parameters is 49,449, of which 512 are
not-trainable. The model performance metrics are:

• Model Precision: 0.931 (93.1%)
• Model Recall: 0.941 (94.1%)
• Model F1 Score: 0.936

The model is the one defined in the file final_model_weights.h5 in the folder
meteor_sort/results/weights/.

If you have any suggestions or questions don't hesitate to reach me at my email: ibrahim.ouladamar@gmail.com

Version 1.1

Meteor Classification using Neural Networks

v1.1 [07/06/2021]

Author: Ibrahim Oulad Amar

Meteor classifier using 256x256 images as input. The images shall be the MAXPIXEL ones in the FTP format. In this case I used the data provided by the University of Western Ontario. I am grateful to researcher Denis Vida for his help in obtaining these data.

The data was splitted in two sets, training (85%) and validation (15%). The model is a CNN (3 layers) along with 3 fully connected layers. The total number of parameters is 49,449, of which 512 are not-trainable. The model performance metrics are:

Model Precision: 0.931 (93.1%)
Model Recall: 0.941 (94.1%)
Model F1 Score: 0.936

The model is the one defined in the results_2_21_code.py in the folder meteor_classification/results/results_2/model_2_21. The weights file is available in the ModelWeights.zip file on the same folder.

If you have any suggestions or questions don't hesitate to reach me at my personal email:
ibraoa98@gmail.com

Version 1.0

Meteor Classification using Neural Networks

v1.0 [25/04/2021]

Author: Ibrahim Oulad Amar

Meteor classifier using 256x256 images as input. The images shall be the MAXPIXEL ones in the FTP format. In this case I used the data provided by the University of Western Ontario. I am grateful to researcher Denis Vida for his help in obtaining these data.

The data was splitted in two sets, training (85%) and validation (15%). The model is a CNN (3 layers) along with 3 fully connected layers. The total number of parameters is 49,449, of which 512 are not-trainable. The model performance metrics are:

Model Precision: 0.931 (93.1%)
Model Recall: 0.941 (94.1%)
Model F1 Score: 0.936

The model is the one defined in the results_2_21_code.py in the folder meteor_classification/results/results_2/model_2_21. The weights file is available in the ModelWeights.zip file on the same folder.

If you have any suggestions or questions don't hesitate to reach me at my personal email:
ibraoa98@gmail.com