Traceback (most recent call last): File "detect.py", line 27, in import pybenchmark File "/home/a/anaconda3/lib/python3.6/site-packages/pybenchmark/init.py", line 1, in from .profile import profile, stats, kpystones, pystones File "/home/a/anaconda3/lib/python3.6/site-packages/pybenchmark/profile.py", line 3, in from test import pystone # native python-core "PYSTONE" Benchmark Program ImportError: cannot import name 'pystone'

When convert the darknet yolo-voc weight to torch model, error occurs like this:

Traceback (most recent call last):
  File "convert_darknet_torch.py", line 140, in <module>
    main()
  File "convert_darknet_torch.py", line 105, in main
    val = np.array(struct.unpack('%df' % cnt, f.read(cnt * 4)), np.float32)
struct.error: unpack requires a bytes object of length 4096

Because I only want to obtain a torch model and evaluate it by myself, I didn't run quick_start.sh to do dataset caching. The main steps are as following:

First, I downloaded weight file from website of darknet and put it in $ROOT, which is $HOME/model/darknet:

wget http://pjreddie.com/media/files/darknet19_448.conv.23

And the command I used to convert the model is (just copied from the quick_start.sh script):

python3 convert_darknet_torch.py ~/model/darknet/darknet19_448.conv.23 -m model/name=model_voc model/dnn=model.yolo2.Darknet -d --copy ~/model/darknet/darknet19_448.conv.23.pth

And could you tell me the difference of darknet19_448.conv.23 and yolo-voc.weights? I tried to convert yolo-voc.weights and it worked well.

INFO:root:load voc dataset Traceback (most recent call last): File "cache.py", line 70, in main() File "cache.py", line 50, in main module, func = dataset.rsplit('.', 1) ValueError: not enough values to unpack (expected 2, got 1)

when i ran quick_start.sh,it show some warning. WARNING - training config (pre_model/yolo2-pytorch/model_20/model.yolo2.Darknet.ini) not found And i can't found model.yolo2.Darknet.ini in project.

Hi there, sorry about that to post this here, i was trying to write yolo in torch but struggled the backward function for so many days, but the gradients are always exploding slowly, could you kindly shed a light on my codes? Thank you so much.

gradInput[{ {}, {}, 1, {}, {} }] = self.mse:backward(torch.cmul(self.x_buffer, x, coord_mask), tx)
gradInput[{ {}, {}, 2, {}, {} }] = self.mse:backward(torch.cmul(self.y_buffer, y, coord_mask), ty)
gradInput[{ {}, {}, 3, {}, {} }] = self.mse:backward(torch.cmul(self.w_buffer, w, coord_mask), tw)
gradInput[{ {}, {}, 4, {}, {} }] = self.mse:backward(torch.cmul(self.h_buffer, h, coord_mask), th)
gradInput[{ {}, {}, 5, {}, {} }] = self.mse:backward(torch.cmul(self.conf_buffer, conf, coord_mask), tconf)
gradInput[{ {}, {}, { 6, 5 + nC }, {}, {} }][self.cls_mask] = self.ce:backward(torch.cmul(self.cls_buffer, cls), tcls)

Hi , Q1) Anchor_Boxes_Generator file generate 10 values of anchors , i have question about these values , as we have 5 anchors and this generator generate 10 values, more likely a first two of 10 values related to first anchor box , right ? if so , what are means of these two values ? W , H for first anchors for aspect ratio and scale for that anchor? Q2) Is it possible to fine-tuning this with one GTX 1080 (8G)?

Great work on this setup.

We can export the weights to Caffe2, but then how do we utilize the deployed weights in a Caffe2 instance on something like an nVidia Jetson TX2? There's no real Yolo2 for Caffe2, right?

I'm confused as to why you're converting to Caffe2 and how that deployment would actually work.

When I convert pretrained darknet model with this command:

python3 convert_darknet_torch.py ~/model/darknet/darknet19_448.conv.23 -m model/name=model_voc model/dnn=model.yolo2.Darknet -d --copy ~/model/darknet/darknet19_448.conv.23.pth

error happened:

Traceback (most recent call last): File "convert_darknet_torch.py", line 142, in main() File "convert_darknet_torch.py", line 107, in main val = np.array(struct.unpack('%df' % cnt, f.read(cnt * 4)), np.float32) struct.error: unpack requires a bytes object of length 4096