hi!
I have solved this issue by means of using the `yolo3_darknet53_custom` model
from the model zoo. This one expects a list of the new classes that will be
used to train your model. Here are the modifications I did to the file (I am
currently training locally and plan to move this to BYOM scripting mode):
# classes
classes=train_dataset.classes
# network
if args.dataset in ['voc','coco']:
net_name = '_'.join(('yolo3', args.network, args.dataset))
else:
net_name = '_'.join(('yolo3', args.network, 'custom'))
args.save_prefix += net_name
# use sync bn if specified
if args.syncbn and len(ctx) > 1:
print('Requested sync batch normalization')
net = get_model(net_name, pretrained_base=True,
norm_layer=gluon.contrib.nn.SyncBatchNorm,
norm_kwargs={'num_devices': len(ctx)})
net.reset_class(classes)
if args.pretrained:
print("Will load pretrained weights of COCO")
async_net = get_model(net_name,
pretrained_base=False,pretrained=True,ctx=ctx,classes=classes)
else:
print("Will load non pretrained weights")
async_net = get_model(net_name,
pretrained_base=False,pretrained=False,ctx=ctx,classes=classes) # used by cpu
worker
else:
print('No sync batch normalization will be performed')
if args.pretrained:
print("Will load pretrained weights of COCO")
net = get_model(net_name,
pretrained_base=True,pretrained=True,ctx=ctx,classes=classes)
else:
print("Will load non pretrained weights")
net = get_model(net_name,
pretrained_base=True,pretrained=False,ctx=ctx,classes=classes)
Hope this helps! Happy training!
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