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Firstly, let me say thanks and congratulations on this ambitious and impressive project.
I've been perusing the code-base and I don't understand it very well. I would like to fine-tune YOLO to my own dataset, using a checkpoint that was learnt on VOC/COCO data.
Just as a test-run, to see what would happen, I ran
...
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 13, 13, 1024)
Load | Yep! | conv 3x3p1_1 +bnorm leaky | (?, 13, 13, 1024)
Load | Yep! | conv 1x1p0_1 linear | (?, 13, 13, 425)
-------+--------+----------------------------------+---------------
Running entirely on CPU
Traceback (most recent call last):
File "./flow", line 42, in <module>
tfnet = TFNet(FLAGS)
File "/home/hal9000/Sources/darkflow/net/build.py", line 51, in __init__
self.setup_meta_ops()
File "/home/hal9000/Sources/darkflow/net/build.py", line 94, in setup_meta_ops
if self.FLAGS.train: self.build_train_op()
File "/home/hal9000/Sources/darkflow/net/help.py", line 15, in build_train_op
self.framework.loss(self.out)
File "/home/hal9000/Sources/darkflow/net/vanilla/train.py", line 9, in loss
'Loss type {} not implemented'.format(loss_type)
AssertionError: Loss type [region] not implemented
So I ask:
(1) Which options on the flow shell script do I need to run fine-tuning?
(2) How do I point to my dataset, and how should it be formatted?
Thanks again.
The text was updated successfully, but these errors were encountered:
(1) You did it correctly, the only reason you cannot fine-tune this config is because it is YOLO9000, for which I did not build the training part (as stated in README). If you want to fine-tune older version's configs, point to ./cfg/v1/ or ./cfg/v1.1/
(2) use option --dataset and --annotation, you can see the complete set of options by ./flow --h (again, as stated in README). The format should be identical to that of PASCAL VOC dataset.
Firstly, let me say thanks and congratulations on this ambitious and impressive project.
I've been perusing the code-base and I don't understand it very well. I would like to fine-tune YOLO to my own dataset, using a checkpoint that was learnt on VOC/COCO data.
Just as a test-run, to see what would happen, I ran
./flow --train --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights
which came back with the error:
So I ask:
(1) Which options on the flow shell script do I need to run fine-tuning?
(2) How do I point to my dataset, and how should it be formatted?
Thanks again.
The text was updated successfully, but these errors were encountered: