-[Inference and Visualization](#inference-and-visualization)
-[Appendix](#appendix)
## Installation
...
...
@@ -45,34 +44,35 @@ Train the model on [MS-COCO dataset](http://cocodataset.org/#download), download
cd dataset/coco
./download.sh
The data catalog structure is as follows:
```
data/coco/
├── annotations
│ ├── instances_train2014.json
│ ├── instances_train2017.json
│ ├── instances_val2014.json
│ ├── instances_val2017.json
| ...
├── train2017
│ ├── 000000000009.jpg
│ ├── 000000580008.jpg
| ...
├── val2017
│ ├── 000000000139.jpg
│ ├── 000000000285.jpg
| ...
```
## Training
After data preparation, one can start the training step by:
python train.py \
--model_save_dir=output/ \
--pretrain=${path_to_pretrain_model}
--data_dir=${path_to_data}
- Set ```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7``` to specifiy 8 GPU to train.
- For more help on arguments:
python train.py --help
**download the pre-trained model:** This sample provides Resnet-50 pre-trained model which is converted from Caffe. The model fuses the parameters in batch normalization layer. One can download pre-trained model as:
sh ./weights/download.sh
Set `pretrain` to load pre-trained model. In addition, this parameter is used to load trained model when finetuning as well.
Please make sure that pre-trained model is downloaded and loaded correctly, otherwise, the loss may be NAN during training.
**Install the [cocoapi](https://github.com/cocodataset/cocoapi):**
To train the model, [cocoapi](https://github.com/cocodataset/cocoapi) is needed. Install the cocoapi:
@@ -81,6 +81,28 @@ To train the model, [cocoapi](https://github.com/cocodataset/cocoapi) is needed.
# not to install the COCO API into global site-packages
python2 setup.py install --user
**download the pre-trained model:** This sample provides Resnet-50 pre-trained model which is converted from Caffe. The model fuses the parameters in batch normalization layer. One can download pre-trained model as:
sh ./weights/download.sh
Set `pretrain` to load pre-trained model. In addition, this parameter is used to load trained model when finetuning as well.
Please make sure that pre-trained model is downloaded and loaded correctly, otherwise, the loss may be NAN during training.
**training:** After data preparation, one can start the training step by:
python train.py \
--model_save_dir=output/ \
--pretrain=${path_to_pretrain_model}
--data_dir=${path_to_data}
- Set ```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7``` to specifiy 8 GPU to train.
