Comments (3)
Hey @irfanICMLL .
The pretrained model "90k_bn" comes from the source code of ICNet:
https://github.com/hszhao/ICNet/tree/master/evaluation/model
You can fine-tune on it, but I think it cannot be much better.
The reason is, this model have been already pruned by the original author. So, if you focus on the number of kernels, you will find all the numbers of kernels are half of those in PSPNet.
So the best approach is, fine-tuning on the model before pruning, and then do model compression.
Hope my answer can help you. Btw, I keep training ADE20k dataset on it, and get result in the bottom of README.
from icnet-tensorflow.
So the decrease of the accuracy is caused by the purning?
And if I want to fune-tuning the "90k", I need to write another script? What is the loss function of the ICNET. I found that the ICNET only has one branch for output, while the ICNET_BN has three and the loss is in a combined form. The loss function described in the artical is also a combined form. I feel really confused....
from icnet-tensorflow.
@irfanICMLL , I don't think the decrease of the accuracy is caused by the pruning, I still can't find the answer to the strange accuracy. Maybe the model is different to each other.
You can directly modified train.py
, I have create the loss formula in it. You can take a look on it.
from icnet-tensorflow.
Related Issues (20)
- Errors in restoring the session evalucation.py and network.py
- ValueError: Shape must be rank 4 but is rank 3 for 'data_sub2' (op: 'ResizeBilinear') with input shapes: [720,720,3], [2]. HOT 3
- Why is it suddenly 'killed' run train.py? HOT 1
- How predict the result to use my training model ckpt.meta?
- Dimension not equal HOT 1
- ValueError:Variable conv does not exist
- ValueError when using own dataset HOT 2
- Training own dataset
- Inference time is too high(about 3.5x as supposed to be ~0.04s)
- multi GPU training?
- tensorflow's version HOT 1
- How to use the pre-trained modle of ade20k provided by author,I use the code in demo.ipynb,but it can't open the file,the cityscapes works well.
- Training over-fitting after every epochs
- same classification result with every pixel HOT 1
- can get correct result
- bad results of voc2012
- The update stops and the loss does not drop HOT 4
- Assign requires shapes of both tensors to match. lhs shape= [13] rhs shape= [150]
- 上一个项目
- 关于ade20k的分割结果,颜色和标签有对应关系吗?
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from icnet-tensorflow.