| --- |
| 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. Temporarily, please [contact me](https://haoliyin.me) for the demo. |
|
|
| ## 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* |
