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--- |
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license: afl-3.0 |
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metrics: |
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- accuracy |
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pipeline_tag: image-segmentation |
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--- |
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# Model Card for Model ID |
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This U-Net model classifies each pixel in an rtMRI Video into air or tissue, and we get the Air-Tissue Boundaries. |
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### Model Description |
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he model uses a U-Net architecture with three decoder branches, each consisting of convolutional and upsampling layers. |
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The encoder consists of convolutional and downsampling layers, followed by a bottleneck layer. |
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The three decoder branches share the same encoder and bottleneck layers, but have different upsampling and convolutional layers. |
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Each decoder branch produces a mask for a different class, with the final output being a 3D tensor with shape (batch_size, height, width, n_labels). |
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- **Developed by:** Vinayaka Hegde , during my internship at Signal Processing Interpretation and Representation (SPIRE) |
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, Lab , at the Indian Institute of Science, Bengaluru |
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- **Model type:** U-Net |
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- **Language(s) (NLP):** N/A |
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- **License:** Apache 2.0 |
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- **Finetuned from model** N/A |
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### Model Sources [optional] |
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- **Repository:** vinster619/UNet_USC_TIMIT |
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- **Paper [optional]:** |
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- **Demo [optional]:** |
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## Uses |
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This pre-trained U-Net model was trained on a dataset comprising videos 342 and 391 from each speaker present in the 10-speaker USC-TIMIT Corpus (Total 20 Videos). |
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The model is designed to classify each pixel in an rtMRI video as either air or tissue. |
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Three distinct masks were used to train the model. |
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### Direct Use |
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3 Segmented binary masks , and their corresponding "contours" can be accurately segmented for any rtMRI video within the USC-TIMIT Corpus. |
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### Downstream Use [optional] |
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This model can be fine-tuned to work properly on other subjects of otehr rtMRI Datasets by finetuning using aprroximately 10-15 frames of any new subject the segmentation has to be performed on. |
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### Out-of-Scope Use |
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The model will accurately perform segmentation ONLY on videos from the USC-TIMIT Corpus. To accurately perform segmentation on videos if subjects from other rtMRI datasets, fine-tuning using frames from the new subject is required. |
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## How to Get Started with the Model |
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Please run the inference.py code , to acces the uploaded weights on this repository and obtain an output video file with the segmented Air-Tissue boundaries. |
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## Training Details |
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Data:USC-TIMIT Corpus (https://sail.usc.edu/span/usc-timit/) |
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Training set size: 2 Videos per subject from each of the 10 subjects present in the dataset |
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Validation set size: 1 Video per subject from each of the 10 subjects present in the dataset |
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Model Architecture: |
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Optimizer: Adam |
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Loss Function: Binary Crossentropy |
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Epochs: 30 , EarlyStopping used |
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Batch Size: 8 |
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Evaluation Metrics: Pixel Classification Accuracy, Dice Coefficient |
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Validation Split: Specify the proportion of the data used for validation (based on the split between train_matrix and val_matrix) |
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Hardware: NVIDIA GeForce RTX 4060 Laptop GPU |
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## Model Card Authors |
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Vinayaka Hegde |
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## Model Card Contact |
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vinayakahegde619@gmail.com |
