This is implementation of Darknet53 network discussed in [1] used for feature extractor of YOLOv3.
This new network is more efficient than ResNet-101 or ResNet-152.
Here are some ImageNet results:
- Framework: Darknet [2]
- GPU: Titan X
- Input Shape(CWH): 3 x 256 x 256
Darknet-53 is better than ResNet-101 and 1.5× faster.
Darknet-53 has similar performance to ResNet-152 and is 2× faster [1] .
But when I trained and tested this model with 224x224 input image, I could not get the good results like the above table.
I got 75.xx% accracy on Imagenet validation dataset.
Pretrained model_weight_download
- Download the ImageNet dataset and move validation images to labeled subfolders
- To do this, you can use the following script
- imagenet data is processed as described here
python train.py --data [imagenet-folder with train and val folders] --gpu 0 -b 64
- Framework: PyTorch
- GPU: GTX 1080 Ti 11GB
- CPU: i7 6550 3.4 GHZ
- RAM: 16 GB
- Batch Size: 1
- Input Shape(CWH): 3 x 224 x 224
On GPU
resnet101 : 0.034906 sec
resnet152 : 0.055852 sec
densenet121 : 0.041888 sec
darknet53 : 0.017952 sec
On CPU
resnet101 : 0.675194 sec
resnet152 : 0.949459 sec
densenet121 : 0.649266 sec
darknet53 : 0.405916 sec