• Network Type (Tiny_Yolo_v4)
• TLT Version (nvidia/tao/tao-toolkit-tf:v3.21.11-tf1.15.5-py3)
Hi!
I want to train an object detector with my custom dataset, I’m following the instructions provided in the jupyter notebook, but the anchor shapes part is a little confusing.
It says:
If you use your own dataset, you will need to run the code below to generate the best anchor shape
The anchor shape generated by this script is sorted.Write the first 3 into small_anchor_shape in the config file.
Write middle 3 into mid_anchor_shape.
Write last 3 into big_anchor_shape.
!tao yolo_v4_tiny kmeans -l $DATA_DOWNLOAD_DIR/training/labels
-i $DATA_DOWNLOAD_DIR/training/images
-n 9
-x 1248
-y 384
- half of my dataset is 512*384 and the other half is 384*512, what values for x, y I should use?
- the above code produces 3 clusters (small_anchor_shape, mid_anchor_shape, big_anchor_shape) but the config file only has two clusters (mid_anchors_shape big_anchor_shape), how should I use them?!
- in the augmentation_config part in the config file there are output_width: 1248 and output_height: 384, are they related to x, y in the above command or image sizes in my dataset?
I tried some combinations randomly, training converges well but the predicted boxes are not very good, the width of predicted boxes are larger than the expected