Comments (6)
I can't either have a working Miniconda (2 and 3) since I had issue with conda calling from pip._vendor.requests
I'll probably try on a cleaner installation of Ubuntu 14.04, later.
Thank you.
from dextr-pytorch.
Hello,
Thanks for your comments!
We tested our code using Python 3.6 and not Python 2.7 so it would be better if you could test with the newer Python version.
Also, you describe that you have Keras and TF, but for the implementation in this repo you need Pytorch. The Keras with TF backend version is here.
Let us know if you have any other questions!
from dextr-pytorch.
Hello,
Thanks for your feedback.
My mistake about Keras, I tried both project but here is the results with PyTorch. I edited my first post.
I'll try tomorrow with Python 3.6 and I'll let you know.
from dextr-pytorch.
Hi,
I failed to build a working virtualenv with Python3.6 on Ubuntu14.04, so I'll stop playing with DEXTR for now. Thanks for your time.
from dextr-pytorch.
I am sorry to hear that. I would recommend you installing Miniconda as suggested in the Readme instead of the virtualenv if you want to give it a last shot.
from dextr-pytorch.
after annotating with four points, complete image is becoming black.
from dextr-pytorch.
Related Issues (20)
- MS Coco Training Code HOT 1
- UR CODE WAS COPIED FOR SELL NOW HOT 1
- PASCAL Context Background (Stuff) Classes dataloader
- Results of dextr_pascal and dextr_coco models HOT 2
- Training time of PASCAL VOC 2012 HOT 1
- What if generated mask is not correct? HOT 1
- I can't click the extreme points in the picture HOT 1
- IndexError: index 0 is out of bounds for axis 0 with size 0 HOT 3
- RuntimeError: ONNX export failed: Couldn't export operator aten::adaptive_avg_pool2d
- Issue with running the demo file HOT 2
- Results on background classes of PASCAL Context HOT 1
- OSError: [WinError 126] The specified module could not be found HOT 1
- Speed metrics ? HOT 2
- How to train this model on other dataset? HOT 1
- Performance of DEXTR with 2 or 3 points HOT 3
- process about clicks more than 4 points HOT 1
- demo.py blocks at line 49
- Question about Loss HOT 3
- List out of range HOT 2
- Train on Custom dataset in COCO style annotation
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