DarkNet-pt

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Implementation of DarkNet19, DarkNet53, CSPDarkNet53 in PyTorch

Contents:

1. DarkNet19 - used as a feature extractor in YOLO900.
2. DarkNet53 - used as a feature extractor in YOLOv3.
3. CSPDarkNet53 - Implementation of Cross Stage Partial Networks in DarkNet53.
4. DarkNet53-Elastic - Implementation of ELASTIC with DarkNet53.
5. CSPDarkNet53-Elastic - Implementation of CSP and ELASTIC in DarkNet53.??

Architecture of DarkNet19 and DarkNet53:

Description:

Results:

Weights of DarkNet53 (105th epoch), DarkNet19 (50th epoch), CSPDarkNet53 (80th epoch) and DarkNet53 ELASTIC (57th epoch) are available on here.

Trained on ImageNet

• GPU: Tesla V100
• Input size: 3x224x224

Dataset structure:

├── IMAGENET 
    ├── train
         ├── [class_id1]/xxx.{jpg,png,jpeg}
         ├── [class_id2]/xxy.{jpg,png,jpeg}
         ├── [class_id3]/xxz.{jpg,png,jpeg}
          ....
    ├── val
         ├── [class_id1]/xxx1.{jpg,png,jpeg}
         ├── [class_id2]/xxy2.{jpg,png,jpeg}
         ├── [class_id3]/xxz3.{jpg,png,jpeg}

Train:

 git clone https://github.com/yakhyo/DarkNet.git
 cd DarkNet2
 python main.py ../IMAGENET --batch-size 512 --workers 8

Note

Modify this line to choose the network to start the training:

from nets.nn import darknet19, darknet53, darknet53e, cspdarknet53

# darknet19
model = darknet19(num_classes=1000, init_weight=True)

# darknet53
model = darknet53(num_classes=1000, init_weight=True)

# darknet53 elastic
model = darknet53e(num_classes=1000, init_weight=True)

# cspdarknet53
model = cspdarknet53(num_classes=1000, init_weight=True)

Continue the training:

python main.py ../../Dataset/IMAGENET --batch-size 512 --workers 8 --resume darknet53.pth.tar