https://github.com/srlozano/mame_dl
Museum Art Mediums (MAMe) image classification task solved by Convolutional Neural Networks at Universitat Politècnica de Catalunya · BarcelonaTech
https://github.com/srlozano/mame_dl
convolutional-neural-networks deep-learning keras python tensorflow
Last synced: about 2 months ago
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Museum Art Mediums (MAMe) image classification task solved by Convolutional Neural Networks at Universitat Politècnica de Catalunya · BarcelonaTech
- Host: GitHub
- URL: https://github.com/srlozano/mame_dl
- Owner: SrLozano
- Created: 2022-03-10T09:26:31.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-04-21T17:39:59.000Z (about 4 years ago)
- Last Synced: 2025-03-23T11:31:09.211Z (over 1 year ago)
- Topics: convolutional-neural-networks, deep-learning, keras, python, tensorflow
- Language: Python
- Homepage: https://arxiv.org/abs/2007.13693
- Size: 33 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MAMe dataset 🎨🤖
Museum Art Mediums image classification task solved by [Laia Borrell Araunabeña ](https://github.com/Laiaborrell) and [Mario Lozano Cortés](https://github.com/SrLozano), Artificial Intelligence master's students at _Universitat Politècnica de Catalunya · BarcelonaTech - UPC_. The resolution of this task is part of the Deep Learning subject.
## Goal of the project 💥💯
Get the highest possible accuracy on the test set.
## The dataset 🗂🖼
The MAMe dataset is a novel image classification task focused on museum art mediums. Data is gathered from three different museums:
- The Metropolitan Museum of Art of New York.
- The Los Angeles County Museum of Art.
- The Cleveland Museum of Art.
Data from all museums is aggregated by art experts into 29 classes of mediums (i.e., materials and techniques).
The dataset contains the following distribution:
The MAMe dataset is originally introduced in the paper [The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties](https://arxiv.org/abs/2007.13693). Although here in this project only 256x256 images version of the dataset is considered, the original MAMe dataset with high-resolution images of variable shape can be download from its oficial [website](https://storage.hpai.bsc.es/mame-dataset/MAMe_data.zip). Additionally, a set of baseline models and code used to train them is available in the oficial github [repository](https://github.com/HPAI-BSC/MAMe-baselines).
Data is expected at the dataset folder. In this folder two other folders are expected:
- data_256: All the images that are going to be used in the task. The _sort_dataset_folder_ is in charged of the sorting of this folder by considering the definitions stated in the metadata folder. The images can be found in the following [link](https://storage.hpai.bsc.es/mame-dataset/MAMe_data_256.zip).
- MAMe_metada: Labels and information of each on of the images. A copy of this folder can be found in the docs folder.
## Architecture proposed 🏛
- Learning rate = 0.001
- Optimizer = Adam
- Batch size = 128
```python
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same', kernel_initializer="he_normal", input_shape=(256, 256, 3)))
model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(GlobalMaxPooling2D())
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.Dense(1024, activation='relu'))
model.add(tf.keras.layers.Dense(512, activation='relu'))
model.add(tf.keras.layers.Dense(29, activation='softmax'))
```
## Run it 🚀
Run mame_cnn.py in order to train the selected model (specified in the exp variable) or run ready_to_go_model.py to load and use an already trained model.
## Results 📊
**Accuracy on test set: 0.737**
## Next updates 🔜
Overfitting limitation
## Issues 🤕
Overfitting
## Want to collaborate? 🙋🏻
Feel free to improve and optimize the existing code. To contribute to the project, read the previous points carefully and do the next steps with the project:
1. Fork it ()
2. Create your feature branch (`git checkout -b feature/MAMe`)
3. Commit your changes (`git commit -am 'Increased accuracy to ...'`)
4. Push to the branch (`git push origin feature/MAMe`)
5. Create a new Pull Request
## Need help ❓
Feel free to contact the developer if you have any questions or suggestions about the project or how you can help with it.