@@ -52,7 +52,7 @@ After data preparation, one can start the training step by:
python train.py \
--model_save_dir=output/ \
--pretrained_model=${path_to_pretrain_model}
--pretrain=${path_to_pretrain_model}
--data_dir=${path_to_data}
- Set ```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7``` to specifiy 8 GPU to train.
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@@ -62,10 +62,10 @@ After data preparation, one can start the training step by:
**download the pre-trained model:** This sample provides Resnet-50 pre-trained model which is converted from Caffe. The model fuses the parameters in batch normalization layer. One can download pre-trained model as:
sh ./weights/download_pretrain_weights.sh
sh ./weights/download.sh
Set `pretrained_model` to load pre-trained model. In addition, this parameter is used to load trained model when finetuning as well.
Please make sure that pretrained_model is downloaded and loaded correctly, otherwise, the loss may be NAN during training.
Set `pretrain` to load pre-trained model. In addition, this parameter is used to load trained model when finetuning as well.
Please make sure that pre-trained model is downloaded and loaded correctly, otherwise, the loss may be NAN during training.
**Install the [cocoapi](https://github.com/cocodataset/cocoapi):**
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@@ -111,25 +111,25 @@ Evaluation is to evaluate the performance of a trained model. This sample provid
python eval.py \
--dataset=coco2017 \
--pretrained_model=${path_to_pretrain_model} \
--weights=${path_to_weights} \
- Set ```export CUDA_VISIBLE_DEVICES=0``` to specifiy one GPU to eval.
Evalutaion result is shown as below:
```text
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.370
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.581
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.401
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.236
