Request for Rock Segmentation Model Using Transfer Learning(VGG16) & U-Net architecture
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Is your feature request related to a problem? Please describe.
I am working on segmenting rocks in lunar and outdoor images, but there isn't a specific model for this task in the repository. A specialized rock segmentation model is needed to extract features and accurately segment rocks from image data.
Describe the solution you'd like
I'd like to implement a U-Net-based architecture that uses a pre-trained VGG16 model to segment rocks in images. The model should:
- Load images from the given directory (
/content/images/
), - Use VGG16 for feature extraction and a U-Net decoder for segmentation,
- Segment images with rocks using convolutional layers, deconvolutional layers, and concatenation for accurate rock segmentation.
The model should be saved at intervals for validation and fine-tuning.
Approach to be followed (optional)
Here is a proposed implementation:
- Load the dataset from Kaggle:
!kaggle datasets download -d romainpessia/artificial-lunar-rocky-landscape-dataset
. - Use a custom generator to load and preprocess images from the
render/
andground/
directories. - Implement a U-Net architecture with VGG16 as the encoder, followed by transposed convolution layers for decoding.
- Compile and train the model using binary cross-entropy loss and the Adam optimizer.
- Save the model at intervals using ModelCheckpoint for further training and evaluation.
This model offers state-of-the-art segmentation performance, with improved accuracy and efficiency in segmenting complex rock formations, which can be particularly beneficial for users handling high-resolution geological data.
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Hello @Kaibalya27! Your issue #531 has been closed. Thank you for your contribution!