From 87ee8e1ef8f0be82da00c91bbbf82a4441b77a4c Mon Sep 17 00:00:00 2001 From: Joshua Zhang Date: Tue, 30 Jan 2018 09:45:40 -0600 Subject: [PATCH] update readme --- README.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a586c84..b0520b6 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # YOLO-v2: Real-Time Object Detection -Still under development. 71 mAP on VOC2007 achieved so far. +Still under development. 71 mAP(darknet) and 74mAP(resnet50) on VOC2007 achieved so far. This is a pre-released version. @@ -21,7 +21,7 @@ custom operators are not presented in official MXNet. [Instructions](http://mxne - Download the pretrained [model](https://github.com/zhreshold/mxnet-yolo/releases/download/0.1-alpha/yolo2_darknet19_416_pascalvoc0712_trainval.zip), and extract to `model/` directory. - Run ``` -# cd /paht/to/mxnet-yolo +# cd /path/to/mxnet-yolo python demo.py --cpu # available options python demo.py -h @@ -29,6 +29,7 @@ python demo.py -h ### Train the model - Grab a pretrained model, e.g. [`darknet19`](https://github.com/zhreshold/mxnet-yolo/releases/download/0.1-alpha/darknet19_416_ILSVRC2012.zip) +- (optional) Grab a pretrained resnet50 model, [`resnet-50-0000.params`](http://data.dmlc.ml/models/imagenet/resnet/50-layers/resnet-50-0000.params),[`resnet-50-symbol.json`](http://data.dmlc.ml/models/imagenet/resnet/50-layers/resnet-50-symbol.json), this will produce slightly better mAP than `darknet` in my experiments. - Download PASCAL VOC dataset. ``` cd /path/to/where_you_store_datasets/ @@ -52,4 +53,8 @@ python tools/prepare_dataset.py --dataset pascal --year 2007 --set test --target - Start training ``` python train.py --gpus 0,1,2,3 --epoch 0 +# choose different networks, such as resnet50_yolo +python train.py --gpus 0,1,2,3 --network resnet50_yolo --data-shape 416 --pretrained model/resnet-50 --epoch 0 +# see advanced arguments for training +python train.py -h ```