add the readme

README.md

@@ -1 +1,94 @@
 # CellSAM
+This is the official repository for CellSAM: Segment Anything in Microscopy Images of C. elegans.
+
+## Installation
+1. Create a virtual environment `conda create -n cellsam python=3.10 -y` and activate it `conda activate cellsam`
+2. Install [Pytorch 2.0](https://pytorch.org/get-started/locally/)
+3. `git clone https://github.com/DevoLearn/CellSAM.git`
+4. Enter the CellSAM folder `cd CellSAM` and run `pip install -e .`
+
+## Get Started
+Download the [model checkpoint](https://drive.google.com/drive/folders/1AF8AFw3dpppg_U79kGsXXl85sy4DNR3m?usp=share_link) and place it at e.g., `work_dir/CellSAM/cellsam_vit_b`
+
+We provide three ways to quickly test the model on your microscopy images:
+
+1. **Command line**
+
+```bash
+python CellSAM_Inference.py # segment the demo image
+```
+
+Segment other images with the following flags:
+```bash
+-i input_img
+-o output path
+--box bounding box of the segmentation target
+```
+
+2. **Jupyter-notebook**
+
+We provide a step-by-step tutorial on [CoLab](https://colab.research.google.com/drive/1HXntUbgstm8UFgamW71PMAf7hgIT-J3V?usp=sharing)
+
+You can also run it locally with `tutorial_quickstart.ipynb`.
+
+3. **GUI**
+
+Install `PyQt5` with [pip](https://pypi.org/project/PyQt5/): `pip install PyQt5` or [conda](https://anaconda.org/anaconda/pyqt): `conda install -c anaconda pyqt`
+
+```bash
+python gui.py
+```
+
+
+
+## Model Training
+
+We have trained only mask decoder by freezing the image encode ,prompt encoder .
+
+### Training on multiple GPUs (Recommend)
+
+The model was trained on one v100 gpu .
+
+```bash
+sbatch train_multi_gpus.sh
+```
+
+When the training process is done, please convert the checkpoint to SAM's format for convenient inference.
+
+```bash
+python utils/ckpt_convert.py # Please set the corresponding checkpoint path first
+```
+
+### Training on one GPU
+
+```bash
+python train_one_gpu.py
+```
+
+### Data preprocessing
+
+Download the demo [dataset](http://celltrackingchallenge.net/datasets/) and unzip it to `data/CellTrain/`.
+
+This dataset contains microscopy images of C. elegans. The names of the cell label are available at [CellTrackingCHALLENGE](http://celltrackingchallenge.net/annotations/).
+
+
+
+- Split dataset: 80% for training and 20% for testing
+- Max-min normalization
+- Resample image size to `1024x2014`
+- Save the pre-processed images and labels as `png` files
+
+One  can navigate through th folder for the `notebooks/explore_preprocess_cell_tracking_challeneg_dataset.ipynb` for converting the 3d tiff files to microscopy images to png images 
+
+
+
+## Acknowledgements
+- We highly appreciate all the Google summer of code organizers and dataset owners for providing the public dataset to the community.
+- Thanks for the bracly alicea, mayukh deb and mainak deb through entire GSOC period
+- We thank Meta AI for making the source code of [segment anything](https://github.com/facebookresearch/segment-anything) publicly available.
+- We also thank Alexandre Bonnet for sharing this insightful [blog](https://encord.com/blog/learn-how-to-fine-tune-the-segment-anything-model-sam/).
+- we also thanks for the code MedSAM Code owner for there code on Medical images [MedSAM](https://github.com/bowang-lab/MedSAM/tree/main)
+
+---
+
+This README provides an overview of the CellSAM model, its architecture, the dataset it's trained on, and how to get started with it. Adjustments can be made based on specific requirements or additional information.

train_multi_gpu.py

@@ -237,7 +237,7 @@ def __getitem__(self, index):
 parser.add_argument("-task_name", type=str, default="CellSAM-ViT-B")
 parser.add_argument("-model_type", type=str, default="vit_b")
 parser.add_argument(
-    "-checkpoint", type=str, default="work_dir/SAM/sam_vit_b_01ec64.pth"
+    "-checkpoint", type=str, default="work_dir/CellSAM/sam_vit_b_01ec64.pth"
 )
 # parser.add_argument('-device', type=str, default='cuda:0')
 parser.add_argument(

train_one_gpu.py

@@ -246,7 +246,7 @@ def __getitem__(self, index):
 parser.add_argument("-task_name", type=str, default="CellSAM-ViT-B")
 parser.add_argument("-model_type", type=str, default="vit_b")
 parser.add_argument(
-    "-checkpoint", type=str, default="work_dir/SAM/sam_vit_b_01ec64.pth"
+    "-checkpoint", type=str, default="work_dir/CellSAM/sam_vit_b_01ec64.pth"
 )
 # parser.add_argument('-device', type=str, default='cuda:0')
 parser.add_argument(