Update README.md

README.md

@@ -17,7 +17,7 @@ model-index:
 
 # 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). 
+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
 
@@ -25,8 +25,7 @@ This model card focuses on the model associated with the DALL·E mini space on H
 * **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).
+* **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{Haoli_SpecLab_2022,
@@ -60,8 +59,6 @@ The performance of the model may degrade when applied on non-biological tissue i
 
 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.
@@ -98,27 +95,27 @@ The simplified training procedure for SpecLab is as follows:
 
 ## Environmental Impact
 
-### DALL·E Mini Estimated Emissions
+### 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-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.
+* **Compute Region:** us-south1 
+* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 7.146 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}
+@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: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*
+*This model card was written by: Haoli Yin*