eclique / rise Goto Github PK
View Code? Open in Web Editor NEWDetect model's attention
License: MIT License
Detect model's attention
License: MIT License
Since D-RISE is archived, I cannot post an issue on this repo, but I actually think it could happen to have the same issue with RISE, I apologize if it does not suit.
So, I have a bad model (with a very small recall, around 0.1): when the mask is applied, I get very few labels and thus the saliency map remains at 0 often. I guess I could decrease the probability of the masked superpixels but is it a good idea to decrease it too much ? I still have pretty bad results with 0.3. I also decreased the detect threshold of the model (I have no predicted labels for the masks over 0.2, and I start to have a correct percentage of null saliency maps at 0.08... but is it a good idea ?).
Anyways, thanks a lot for your work!
I believe both RISE and D-RISE (cvpr'21) are surprising works. When will you be releasing your D-RISE codes? What's your codebase for detectors? Thanks!
In def generate_masks(N, s, p1):
Couldn't find what does N,s,p1 stands for in the code. I assume N stands for the number of filters generated? Since variable names don't imply their purpose, Commenting on its purpose or what it stands for would be great.
Is any code available for evaluation using of tensorflow.
Hi ! My question is about the computation of the AuC in the Causal Metrics script.
I wonder why the computation is :
(arr.sum() - arr[0] / 2 - arr[-1] / 2) / (arr.shape[0] - 1)
Instead of the regular area under the curve:
arr.sum() / (arr.shape[0] - 1)
To me, the normalized version implemented substracts the mean of the first and last score and I don't understand why that is.
Could you clarify that?
Thank you.
Hi, could you give an explanation for the reason of substracting the halves of the head and tail elements in an array when calculating the AUC?
I tried setting a threshold to distinguish it, but the effect was not very good
Is any possible to see "random-masking/run02/explanations/exp_{:05}-{:05}.npy " while evaluating a batch of explanations?
@eclique thanks for open sourcing the code just having a query
Hi there,
I am trying to implement RISE for greyscale images. However, in easystart.ipynb with def load_img (see below), the input size is converted from greyscale into RGB images.
def load_img(path):
img = image.load_img(path, target_size=model.input_size)
So, do you know if RISE is only usable with RGB images?
Best regards,
Daan
Hello,
The masks that have been generated are stored somewhere? Is it possible to to get access to the path where the masks stored? Also, what is 's' in the generate_mask function?
Hi,
I noticed that your code of "evaluation.py" does not include the normalization of images in the single_run or evaluate.
Shouldn't the evaluation of the insertion and deletion game includes the normalization of images?
Thank you in advance,
Hanwei
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.