Update README.md

README.md

@@ -15,6 +15,38 @@ datasets:
 ---
 
 
+# Model Card for cell-seg-sribd
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This repository provides the solution of team Sribd-med for NeurIPS-CellSeg Challenge. The details of our method are described in our paper [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images]. Some parts of the codes are from the baseline codes of the NeurIPS-CellSeg-Baseline repository,
+
+You can reproduce our method as follows step by step:
+
+
+### How to Get Started with the Model
+
+Install requirements by python -m pip install -r requirements.txt
+
+## Training Details
+
+### Training Data
+
+The competition training and tuning data can be downloaded from https://neurips22-cellseg.grand-challenge.org/dataset/ Besides, you can download three publiced data from the following link: Cellpose: https://www.cellpose.org/dataset  Omnipose: http://www.cellpose.org/dataset_omnipose Sartorius: https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation/overview 
+
+## Environments and Requirements:
+Install requirements by
+
+```shell
+python -m pip install -r requirements.txt
+```
+
+## Dataset
+The competition training and tuning data can be downloaded from https://neurips22-cellseg.grand-challenge.org/dataset/
+Besides, you can download three publiced data from the following link: 
+Cellpose: https://www.cellpose.org/dataset 
+Omnipose: http://www.cellpose.org/dataset_omnipose
+Sartorius: https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation/overview 
 
 
 
@@ -23,8 +55,9 @@ datasets:
 Here is how to use this model:
 
 ```python
-from transformers import cellseg_sribd
+
 from skimage import io, segmentation, morphology, measure, exposure
+from cell_sribd_model import MyModel
 import numpy as np
 import tifffile as tif
 import requests
@@ -56,9 +89,11 @@ for i in range(3):
         pre_img_data[:,:,i] = normalize_channel(img_channel_i, lower=1, upper=99)
 
 
+#config = ModelConfig()
+#print(config)
+my_model = MyModel.from_pretrained("Lewislou/cellseg_sribd")
 
-model = cellseg_sribd.from_pretrained("Lewislou/cellseg_sribd")
 with torch.no_grad():
-    output = model(pre_img_data)
+    output = my_model(pre_img_data)
 
 ```