pytorch复现Fast-RCNN目标检测项目

# Fast R-CNN pytorch This is an implementation of Fast R-CNN using pytorch on the animal images of COCO dataset. Fast R-CNN uses ROIPooling to avoid repeated calculation in R-CNN and combines classification and location togerther using FC in neural networks. To prepare data, download and unzip in the COCO2017 folder. To install required packages, run ``` pip install -r requirements.txt ``` To create regions using selective search, run ``` python script/create_regions.py ``` This may take several hours to complete. Herein, vgg19_bn is used in Fast R-CNN to extract features. To train the model, run ``` python train.py --epochs num_epochs --cuda True/False ``` To test the model on specific images, run ``` python test.py --jpg_path jpg_path ``` test sample