Update README.md

README.md

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 ---
+inference: false
+co2_eq_emissions:
+      emissions: 7540
+      source: MLCo2 Machine Learning Impact calculator
+      geographical_location: East USA
+      hardware_used: Tesla V100-SXM2 GPU
+tags:
+- text-to-image
 license: gpl-3.0
+
+language: en
+model-index: 
+  - name: SpecLab
+    results: []
 ---
+
+# SpecLab Model Card
+
+This model card focuses on the model associated with the DALL·E mini 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 generate images based on text prompts. As the model developers wrote in the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) about DALL·E mini, “OpenAI had the first impressive model for generating images with [DALL·E](https://openai.com/blog/dall-e/). DALL·E mini is an attempt at reproducing those results with an open-source model.”
+* **Resources for more information:** See OpenAI’s website for more information about [DALL·E](https://openai.com/blog/dall-e/), including the [DALL·E model card](https://github.com/openai/DALL-E/blob/master/model_card.md). See the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
+* **Cite as:** 
+```bib text
+@misc{Haoli_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
+
+### DALL·E Mini 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-east1 (provided by model developers)
+* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 7.54 kg CO2 eq.
+
+## Citation
+
+```bibtext
+@misc{Dayma_DALL·E_Mini_2021,
+      author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
+      doi = {10.5281/zenodo.5146400},
+      month = {7},
+      title = {DALL·E Mini},
+      url = {https://github.com/borisdayma/dalle-mini},
+      year = {2021}
+}
+```
+
+*This model card was written by: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*