UppuluriKalyani/ML-Nexus

Request for Rock Segmentation Model Using Transfer Learning(VGG16) & U-Net architecture

Closed this issue · 2 comments

Kaibalya27 commented

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:

  1. Load the dataset from Kaggle: !kaggle datasets download -d romainpessia/artificial-lunar-rocky-landscape-dataset.
  2. Use a custom generator to load and preprocess images from the render/ and ground/ directories.
  3. Implement a U-Net architecture with VGG16 as the encoder, followed by transposed convolution layers for decoding.
  4. Compile and train the model using binary cross-entropy loss and the Adam optimizer.
  5. 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.

github-actions commented

Thanks for creating the issue in ML-Nexus!🎉
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github-actions commented

Hello @Kaibalya27! Your issue #531 has been closed. Thank you for your contribution!