update readme

README.md

@@ -1,2 +1,43 @@
 # About
-This repositroy is used for my Master level degree project.
+This this the code repository for my final project of MSc Data Science and Machine Learning degree at UCL. It's better to check out this repo with the thesis.
+
+# Introduction
+In this project, we explore training strategies for deep architectures of Computer Vision. Especially, we focus on "ImageNet pre-training", and "multi-task learning" and "regularization" are also applied.
+
+# Requirements
+* Python 3
+* PyTorch >= 0.4.0
+* Numpy 
+
+# Data
+In this project, we use several datasets for semantic segmentation and human part segmentation. See and modify their paths in config.json for your convenience.
+
+* [Pascal Voc 2012](http://host.robots.ox.ac.uk/pascal/VOC/)
+* [Semantic Boundary Dataset](http://home.bharathh.info/pubs/codes/SBD/download.html)
+* [Look Into Person Dataset](http://sysu-hcp.net/lip/)
+
+# Usage
+```bash
+python train_imagenet.py [--arch ARCH] [--epochs N]
+                         [--dataset D] [--data_portion DP]
+                         [--batch_size N] [--lr LR] [--momentum M]
+                         [--momentum_bn M_BN] [--weight-decay W]
+                         [--pretrained] [--dprob DPB]
+                         [--n_classes C] [--optim O]
+                         [--manual_seed MANUALSEED]
+
+--arch, -a            model architecture: sunet64 | fcn32resnet18 |
+                      fcn32resnet 34 | fcn32resnet50 | fcn32resnet101 |   
+                      fcn32resnet152 | fcn32vgg
+--dataset             dataset: sbd | pascal | pascalpart | lip
+--epochs              number of epochs to train
+--batch-size          mini-batch size (default: 10)
+--lr                  initial learning rate
+--momentum            momentum
+--momentum_bn         momentum of batch normalization
+--wd                  weight decay (default: 1e-4)
+--pretrained          use pre-trained model
+--manual_seed         manual seed 
+--n_classes           number of classes of ground truth annotation
+--dprob               dropout probability
+```