anirudh-chakravarthy / casenet Goto Github PK
View Code? Open in Web Editor NEWA Pytorch implementation of CASENet for the Cityscapes Dataset
A Pytorch implementation of CASENet for the Cityscapes Dataset
The paper mention that they use the maximum F-measure (MF) at optimal dataset scale (ODS), and average precision (AP) , do you have the code?
Thank you for sharing your implementation.
I am wondering would be useful to use the CASENet network for the binary edge detection for the custom dataset or adopting the CASENet network with Hausdorff distance loss (or even cross-entropy loss) instead of multi-label loss?
Here is an example of my ground truth:
Thank you in advance
Could you provide train_aug_label_binary_np.h
and test_label_binary_np.h5
files?
Thank you for your implement so much!
Can you please report some performance about your code on cityscapes dataset?
Thank you again!
Best wishes!
Is anyone working on this repo? I trained and tested this model on anothoer dataset, the side output pictures was like this:
You can see that those side outputs doesn't show any boundary, but the fused predictions looks ok. I noticed that in the author's essay, side activations shows boundaries of the input images like this:
I wonder if anyone is having the same problem.
First, thanks a lot for your excellent work, which has made convenient for everyone . Yet I have met a trouble that the Pytorch pretrained model on Google Drive is broken. The extract-caffe-parameters code needs to import caffe, which is so inconvenient in my situation. Could you please upload the Pytorch pretrained model again?
Hi, I've finished the pre-processing but there was an error occur after running "python utils/convert_bin_to_hdf5.py"
Traceback (most recent call last):
File "utils/convert_bin_to_hdf5.py", line 66, in
convert_num_to_bitfield(label_data, h, w, npz_name, root_folder, h5_file)
File "utils/convert_bin_to_hdf5.py", line 30, in convert_num_to_bitfield
padded_bit_tensor = torch.cat((torch.zeros(cls_num-actual_len).byte(), bit_tensor.byte()), dim=0)
RuntimeError: $ Torch: invalid memory size -- maybe an overflow? at ..\aten\src\TH\THGeneral.cpp:188
The main reason of the error is that 'cls_num' is smaller than 'actual_len', so 'cls_num-actual'_len value is under 0.
I don't understand what is 'actual_len'. What does the actual length mean?
the nyuv2 has 40 classes,if the code of 'cityscape_process' can process the nyuv2 dataset? cityscape only has 19 classes.
Hi,
After reading this issue, I noticed that you are very professional on this problem.
I have tried a lot for converting caffe weights to numpy. But it's very hard to finish this, some bugs always occurs.
So, could you please release the converted pretrained weights or just send to me?
Thank you so much!
My email: [email protected]
Hello, thank you for your wonderful job! I am trying to reproduce the results of CASENet based on the repository. So I wonder if you can release the final model parameter for better reference, i.e., testing and showing the performance of the code. I will appreciate it very much!
I have run the code to preprocess CityScapes data and found that no directory named 'data_aug', with 'list_train_aug.txt' under that.
It is really confusing.
Hi! I'm trying to test your code but something seems to not be working properly. I'm trying to use the command:
python get_results_for_benchmark.py -m pretrained_models/model_casenet.pth.tar -f lena.png -d images/ -o output/
but got the following error:
(truncated)
score_cls_side5.weight is loaded successfully
score_cls_side5.bias is loaded successfully
upsample_cls_side5.weight is loaded successfully
ce_fusion.weight is loaded successfully
ce_fusion.bias is loaded successfully
Traceback (most recent call last):
File "get_results_for_benchmark.py", line 68, in <module>
for cls_idx in xrange(num_cls):
NameError: name 'xrange' is not defined
I have downloaded the pre-trained model for PyTorch and placed it in the folder pretrained_models/. I have downloaded lena.png image from Wikipedia (just for simple test) and placed it in the folder images/. What am I missing? Can you help me out?
Thank you!
Hi, thanks for sharing your code. I downloaded the gtFine_trainvaltest.zip (241MB) dataset and decompressed it, but I don't quite follow the instructions in the README.
First, python utils/convert_bin_to_hdf5.py
file contains hardcoded file val.txt
, but where can I find this file? I don't see such file in the repository or the dataset.
Also, what should I do to run the whole repo from scratch, for example, from dataset preprocess to training, validating and testing? Some more detailed instructions would be a great help.
python3 utils/convert_bin_to_hdf5.py
python3 main.py
Are the two commands above all that needed to run the repository?
Thanks!
Hello,
I follow your code and run it on a subset of the database (50 images). The code run til the end but the loss doesn't go down. The loss also is extremely high (about 1000000). Is it normal?
Looking forward to your reply
Thanks for sharing the code. Have you ever reproduced the result with this code repo?
Thanks.
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