Update README.md

README.md

@@ -6,10 +6,8 @@ pipeline_tag: image-classification
 
 ### Model Description
 
-This model is the best model released from the CHAMMI-75:pre-training multi-channel models with heterogeneous microscopy images paper currently under review at a conference. 
-This model was pre-trained on the entire CHAMMI-75 dataset using the bag of channels method. It has acheived state of the art in 7/8 self-supervised learning benchmarks.
-The utility of this model is to be used in single-cell analysis of microscopic imaging.
-
+Morphem is a self supervised learning framework trained with the DINO Bag of Channels recipe on the entire CHAMMI-75 dataset. 
+It serves as a benchmark for performance for self-supervised models.
 
 - **Developed by:** Vidit Agrawal, John Peters, Juan Caicedo
 - **Shared by:** [Caicedo Lab]
@@ -53,20 +51,30 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+MorphEm was pre-trained on the entire CHAMMI-75 pre-training data. 
+The CHAMMI-75 dataset consists of 75 heterogenous studies and 2.8 million multi-channel images. 
 
 ### Training Procedure
 
-We have utilized the DINO model. 
+We have utilized the self-supervised learning framework called DINO. We pre-trained a model which inputs a single channel one at a time. For evaluation, you would concatenate each channel specifically.
 
 #### Preprocessing
 
-[More Information Needed]
+We used three transforms mainly for preprocessing: SaturationNoiseInjector(), SelfImageNormalize(), Resize(224,224)
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
-specify all 6 benchmarks, and point to them. Point to the paper?
+We have evaluated this model on 6 different benchmarks. The model is highly competitive in most of them. The benchmarks are listed below:
+
+1. CHAMMI
+2. HPAv23
+3. Jump-CP
+4. IDR0017
+5. CELLPHIE
+6. RBC-MC
+
+More details can be found in the paper: 
 
 #### Summary
 
@@ -74,20 +82,21 @@ specify all 6 benchmarks, and point to them. Point to the paper?
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
 - **Hardware Type:** Nvidia RTX A6000
-- **Hours used:** 2500
+- **Hours used:** 2352
 - **Cloud Provider:** Private Infrastructure
 - **Compute Region:** Private Infrastructure
-- **Carbon Emitted:** 324 kg CO2
+- **Carbon Emitted:** 304 kg CO2
 
 ## Technical Specifications
 
 
 The model is a ViT Small trained on 2500 Nvidia A6000 GPU hours. The model was trained on a multi-node system with 2 nodes, each containing 7 GPUs.
 
-<!-- ## Citation -->
+## Citation
+
+Can be cited as the following:
+
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->