File size: 4,713 Bytes
f2ede71 ca10562 49e0d95 f2ede71 ca10562 f2ede71 ca10562 a5c3ede ca10562 a5c3ede ca10562 dd73e5e ca10562 a5c3ede ca10562 40bdf3a ca10562 a5c3ede 40bdf3a ca10562 a5c3ede ca10562 a5c3ede |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
---
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* |