---
inference: false
co2_eq_emissions:
      emissions: 7540
      source: MLCo2 Machine Learning Impact calculator
      geographical_location: East USA
      hardware_used: Tesla V100-SXM2 GPU
tags:
- segmentation
license: gpl-3.0

language: en
model-index: 
  - name: SpecLab
    results: []
---

# SpecLab Model Card

This model card focuses on the model associated with the SpecLab space on Hugging Face, available [here](https://huggingface.co/spaces/Nano1337/SpecLab). 

## Model Details

* **Developed by:** Haoli Yin
* **Model type:** Atrous Spatial Pyramid Pooling (ASPP) model for Specular Reflection Segmentation in Endoscopic Images
* **Language(s):** English
* **License:** GPL 3.0
* **Model Description:** This is a model that can be used to create dense pixel-wise segmentation masks of detected specular reflections from an endoscopy image. 
* **Cite as:** 
```bib text
@misc{Yin_SpecLab_2022,
      author = {Yin, Haoli},
      doi = {TBD},
      month = {8},
      title = {SpecLab},
      url = {https://github.com/Nano1337/SpecLab},
      year = {2022}
}
```

## Uses

### Direct Use

The model is intended to be used to generate dense pixel-wise segmentation maps of specular reflection regions found in endoscopy images. Intended uses exclude those described in the [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use) section.

### Downstream Use

The model could also be used for downstream use cases, including further research efforts, such as detecting specular reflection in other real-world scenarios. This application would require fine-tuning the model with domain-specific datasets.

## Limitations and Bias

### Limitations

The performance of the model may degrade when applied on non-biological tissue images. There may also be edge cases causing the model to fail to detect specular reflection, especially if the specular reflection present is a different color than white. 


### Bias 

The model is trained on endoscopy video data, so it has a bias towards detecting specular reflection better on biological tissue backgrounds. 

### Limitations and Bias Recommendations

* Users (both direct and downstream) should be made aware of the biases and limitations.
* Further work on this model should include methods for balanced representations of different types of specular reflections. 


## Training

### Training Data

The GLENDA "no pathology" dataset was used to train the model:
* [GLENDA Dataset](http://ftp.itec.aau.at/datasets/GLENDA/), which contains ~12k image frames. 
* Masks (to be released), were generated using the specular reflection detection pipeline found in this paper (to be released).
* Train/Val/Test was split randomly based on a 60/20/20 distribution. 

### Training and Evaluation Procedure & Results

You can view the training logs [here at Weights and Biases](https://wandb.ai/nano-1337/Predict/reports/SpecLab-Training-for-10-Epochs--VmlldzoyNDYyNDIz?accessToken=xfjtfgb5szvsk08luvmwinjl6y2kvp1vl1eax52kbxgwgbwjqv29yed9elzgbju1)

During training, input images pass through the system as follows:
* Images are transformed by albumentations with horizontal/vertical flips to augment the data, normalized to [0, 1], and converted to a tensor. 
* A forward pass is run through the model and the logits are output
* Loss is the "Binary Cross Entropy with Logits Loss" between the model prediction logits and the ground truth masks
* The logits are run through a sigmoid activation function and a threshold at 0.5 is set to binarize the output. 

The simplified training procedure for SpecLab is as follows: 

* **Hardware:** One 16GB NVIDIA Tesla V100-SXM2
* **Optimizer:** Adam
* **Batch:** 4 samples 
* **Learning rate:** initialized at 0.001 then CosineAnnealingLR with a T_max of 20.
* **Epochs:** 10 epochs
* **Steps:** 18k

## Environmental Impact

### SpecLab Estimated Emissions

Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.

* **Hardware Type:** Tesla V100-SXM2
* **Hours used:** 6
* **Cloud Provider:** Google Colab
* **Compute Region:** us-south1 
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 0.7146 kg CO2 eq.

## Citation

```bibtext
@misc{Yin_SpecLab_2022,
      author = {Yin, Haoli},
      doi = {TBD},
      month = {8},
      title = {SpecLab},
      url = {https://github.com/Nano1337/SpecLab},
      year = {2022}
}
```

*This model card was written by: Haoli Yin*