Comments (8)
hello, are you changing any parts of the code? i have double-checked before uploading. how about downloading the code and run it again?
from deepemd.
Hello, I downloaded the code again and ran it again. But the result is still the same.
from deepemd.
how about the dataset?
from deepemd.
Thank you very much for your reply. I use miniimagenet dataset downloaded from other places. Will this have any impact? Has your dataset been processed in advance?Then I'll download your dataset for testing and look at the results.
from deepemd.
hi, you may refer to this issue. might be related.
from deepemd.
Thank you. I'll download the dataset and test it again.
In addition, I have another question. When I train the cub dataset, there will always be such errors. Have you encountered them? How did you solve them?
RuntimeError: cuda runtime error (59) : device-side assert triggered at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:110
from deepemd.
Hello, I've solved that problem. I used the dataset you provided, and the test results are similar to the data you provided. In addition, I have solved the problem of cub dataset. In datautils.py of dataloder:
elif args.dataset == 'cub':
args.num_class = 100
The value of num_class needs to be changed to 100.
Finally, can you provide the visualization program in the paper?
I am really desperate for the answer.
Thanks in advance for your help.
from deepemd.
hi , thank your pointing our the issue.
for the visualization program, it is not compatible with the released code. You may drop me an email and I can send you the raw code for reference. you may also write one on your own.
After you obtain the flow matrix from opencv or QPTH, you actually get a correspondence matrix. Then you only need to write a script to crop&paste patches based on it. Then you use normalized weights to control the brightness.
from deepemd.
Related Issues (20)
- Unable to REPEAT previous results. HOT 1
- About the effect of meta-train HOT 2
- CUB dataset is not original one HOT 1
- Code for Visualization of Fig 6 & 7 in the paper HOT 2
- Reproduction of the results of tiered-imagenet HOT 1
- hyperparameters for pre-training on TieredImageNet? HOT 1
- retun statement in the encode function of class DeepEMD
- training hyperparameters for CUB and CIFAR
- About the acc on miniImageNet HOT 2
- Fail to reproduce accuracy for miniimagenet HOT 1
- About sfc_lr in eval.py HOT 1
- dataset structure? HOT 1
- The memory of QPTH-solver(5way-1shot) is about 22GB HOT 1
- Pretrain model overfitting HOT 1
- Question on how to use this for inference HOT 1
- Can't achieve the accuracy in the papar HOT 5
- All the results are lower than paper with the default settings and feature pyramid 5,2,1 HOT 1
- Why repeat data_shot HOT 2
- MiniImageNet Dataset and TieredImageNet Dataset HOT 2
- I'm trying to reproduce under a framework named LibFewShot HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from deepemd.