fist commit

README.md

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-# RCNN
-This is a Implementation of R-CNN
+# Implementation of R-CNN
+
+This is a Implementation of R-CNN. (R-CNN: Regions with Convolutional Neural Network Features)
+
+## Prerequisites
+- Python 3.5
+- Pytorch 0.4.1
+- sklearn 0.18.1
+- cv2 3.4.0
+
+You can run the code in Windows/Linux with CPU/GPU. 
+
+## Dataset
+
+For simplicity, rather than use PASCAL VOC 2012 dataset, I choose a samll dataset 17flowers. It can be downloaded from my Google Drive: 
+
+https://drive.google.com/open?id=1qAkLZ-Wa8mca2xz-RZI3XW1Syjk2JfOB
+
+## Structure
+
+The project is structured as follows:
+
+```
+├── checkpoints/
+├── data/
+|   ├── dataset_factory.py    
+|   ├── dataset_factory.py    
+├── generate/
+├── loss/
+|   ├── losses.py  
+├── models/
+|   ├── model_factory.py    
+|   ├── models.py  
+├── networks/
+|   ├── network_factory.py    
+|   ├── networks.py 
+├── options/
+|   ├── base_options.py    
+|   ├── test_options.py 
+|   ├── train_options.py
+├── sample_dataset/
+|   ├── 2flowers   
+|   ├── 17flowers 
+│   ├── fine_tune_list.txt
+|   ├── train_list.txt
+├── utils/
+|   ├── selectivesearch.py    
+|   ├── util.py 
+├── evaluate.py
+├── train_step1.py
+├── train_step2.py
+├── train_step3.py
+├── train_step4.py
+```
+
+## Getting started
+
+### Supervised Train
+
+In this step, we use pre-trained AlexNet of Pytorch and train it using 17flowers dataset. 
+
+```
+$ python train_step1.py 
+```
+
+You can directly run it with default parameters.
+
+### Finetune
+
+In this step, we finetune the model on the 2flowers dataset.
+
+```
+$ python train_step2.py --batch_size 128 --load_alex_epoch 100 --options_dir finetune
+```
+
+Here we use the pre-trained AlexNet with 100 training epoch in the first step. "Options_dir" decides where to save parameters.
+
+### Train SVM
+
+In this step, we use features extracted from the last step to train SVMs. As stated in the paper, class-specific SVMs are trained in this step. Here there are two SVMs.
+
+```
+$ python train_step3.py --load_finetune_epoch 100 --options_dir svm
+```
+
+Here we adopt the finetuned AlexNet with 100 epoch in last step as feature extracter.
+
+### Box Regression
+
+In this step, we train a regression network to refine bounding boxes.
+
+```
+$ python train_step4.py --decay_rate 0.5 --options_dir regression --batch_size 512
+```
+
+### Evaluate
+
+```
+$ python evaluate.py --load_finetune_epoch 100 --load_reg_epoch 40 --img_path ./sample_dataset/2flowers/jpg/1/image_1281.jpg
+```
+
+![](https://github.com/bigbrother33/Deep-Learning/blob/master/photo/1.PNG)
+
+## References
+
+- Selective-search: https://github.com/AlpacaDB/selectivesearch
+- R-CNN with Tensorflow: https://github.com/bigbrother33/Deep-Learning
+
+Many codes related to image proposals are copied directly from "R-CNN with Tensorflow", and this project has shown more details about R-CNN in chinese.

data/dataset_factory.py

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+import os
+
+class DatasetFactory:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def get_by_name(dataset_name, opt):
+        if dataset_name == 'AlexnetDataset':
+            from data.datasets import AlexnetDataset
+            dataset = AlexnetDataset(opt)
+        elif dataset_name == 'FinetuneDataset':
+            from data.datasets import FinetuneDataset
+            dataset = FinetuneDataset(opt)
+        elif dataset_name == 'SVMDataset':
+            from data.datasets import SVMDataset
+            dataset = SVMDataset(opt)
+        elif dataset_name == 'RegDataset':
+            from data.datasets import RegDataset
+            dataset = RegDataset(opt)
+        else:
+            raise ValueError("Dataset [%s] not recognized." % dataset_name)
+
+        print('Dataset {} was created'.format(dataset.name))
+        return dataset
+
+class DatasetBase:
+    def __init__(self, opt):
+        self._name = 'BaseDataset'
+        self._root = opt.data_dir
+        self._opt = opt
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def path(self):
+        return self._root