wisconsinaivision / mixnmatch Goto Github PK
View Code? Open in Web Editor NEWPytorch implementation of MixNMatch
Pytorch implementation of MixNMatch
Hey !
Amazing work guys !!! I am able to download the data but when I try the pretrained models it says Error 404 page not found. Just wanted to ask if you guys have moved the pretrained models from the given link ?
Dear author, please upload the codes and pre-trained weights for the model.
@Yuheng-Li Can you explain how transfer learning can be done on this model.
I am trying to train the model on custom dataset.
The train dataset is of the size 400 * 400 px. But while inferring the size of generated output is 128 * 128 px.
Is it possible to change the size of output image? If yes, is some modification required in the code?
for my understanding to run eval.py and create new image (from 4 images ) i need the generator (G.pth) and encoder (E.pth)
so after the first training stage i got folder with Model folder that contain the followings models :
BD_0,DO_O,D1_0,D2_0,E_0,G_0
after training the second stage i have folder EX_0.pth
my general questions what s should i need to do next for making new image?
i need path for 4 images that is ok
but i also need path to a folder with 3 pre trained models G.pth E.pth EX.pth - where are them?
do i need to take E_0,G_0 (from first training stage) and EX_0 (from second training stage) and put them together under the same folder and that folder is the models path?
few questions:
EX.pth that came from secound training stage is for feature mode?
for eval.py why do i need E.pth and EX.pth ?
how many epochs is needed (first and second stage) for getting the same resualts for birds datasets, the paper say nothing about that
thanks
why code_z *4 in Encoder Network ???
Hey,
Thanks for the great work and releasing the code. I was wondering how to set the ratio(76/64) at the resize part here https://github.com/Yuheng-Li/MixNMatch/blob/21095b3581c7d47f67ed1bb360ca8ac3db6c299f/code/datasets.py#L57 ,
To extend the work to other image size for training, which resize ratio would be recommended?
Thanks!
Hi,
What is the significance of these parameters SUPER_CATEGORIES and FINE_GRAINED_CATEGORIES.
Also, Is there a minimum number of recommended Image count for training. Do you have a Image preparation script to suit the model input for training.
is this keeping a video as pose images and then background, texture, and shape is the same image? Can you provide an example for Converting a reference image according to a reference video in eval.py?
Hi, thanks your helpping , I have finish re-train all stage.
There are 6 pictures for result:
- | - | - | - |
---|---|---|---|
pose_file | background_file | shape_file | color_file |
retrain_gen_feature_mode | retrain_gen_code_mode |
pretrain_gen_code_mode | pretrain_gen_feature_mode |
Q1: why object of pic_1 is out of picture in code mode ? such as both pic_1(retrain_gen_code_mode and pretrain_gen_code_mode ) ?
Q2: my retrain model weight maybe failed in capture texture feature ? Did you have some experience for this ? the reason is different random seed or something else? such as all retrain_gen pictures
Q3: Nothing to be generated except background, [such as pic_5(retrain_gen_code_mode
)], or bad backgroud [such as pic_6(pretrain_gen_feature_mode
)]
I tested for pre-trained model and given dataset, but I want to create a new model for my custom dataset, how can I train and create a new model?
I have used labelimg for creating bounding boxes for my custom dataset. But it is giving coordinate values ranging from (0 - 1). Due to this reason I am getting zeroDivisionError in the implementation of the code. So i want to know the correct method and model for creating these bounding box annotations.
Hello. I love this repo and I'd like to use it. Colab notebooks tend to be easy to set up, I tried doing it just now and was unable to. Would you mind creating a colab notebook version of this repo? Thank you!
Thanks for a nice package! Just wanted to point out that there are more requirements than what is stated in the README file; in addition to the ones already listed:
easydict
matplotlib
sklearn
tensorboardX
torchvision==0.4.2 (not sure exactly which versions work, but this one does and some others I tried don't)
Hi, in load_networks functions only three networks have been initialized : netG, netDs, encoder
but only BD not intialization ? why ? and this is your experiment performance ?
I took the top 6 categories in CUB dataset(323 pictures) and only changed SUPER_CATEGORIES to 3 and FINE_GRAINED_CATEGORIES to 6 to run train_first_stage.py but got bad results. In my opinion, deep learning model will easily fit the dataset which has little amount of data, it semms that your model need large amount of data to train? Expect to get your reply, Thanks.
I finished reading this paper . the paper said we only require a loose bounding box around the object to model background, But the author didn't explain it in detail. and i have some questions
1.How we define the bounding box,what is it used for?
2.what should i do to get the bounding box if i wanna train my own dataset?
i‘m looking forward to your reply。
I tried the sample code and concernd about how can I print the EG_Loss?
Thank You.
What implementation did you use to get the bounding box in that format?
I can see that (x, y, w, h) is the pixel distance from the top and left, I just don't know of a library that uses that format.
Hey, guys your work is so interesting, that I want to setting up this model but having issues in doing that, Can you please refer me some videos of it how to set up the dataset in the model. I also want to contribute it by training it on different dataset. Can you please help me?
Hi, does the first p in the mutual information discriminator D(P|Pfm) used in the parent phase of FineGAN refer to the latent code p or the generated fake image p,I tried to read the source code of FineGAN, but I couldn't understand the idea of def train_Gnet in trainer.py about the implementation of the parent mutual information discriminator
Hii @Yuheng-Li @utkarshojha @kkanshul @Johnson-yue
I am trying to generate full body human using this model but when we train this model on custom dataset result was bad after training completion, so can you suggest me how can we improve results on custom datasets.
I am share some details which you will be able to understand easily.
Model configuration :
SUPER_CATEGORIES = 1
FINE_GRAINED_CATEGORIES = 1
FIRST_MAX_EPOCH = 600
SECOND_MAX_EPOCH = 400
Here it's our model configuration as you can see above, now I am sharing two picture first one is ref image and another one is result of our model.
Hi, thank your sharing !!
It is very interesting, I have tested some birds picture with pre-trained model by using eval.py and I find some question
I run the command line code:
python eval.py --z pose/pose-2.png --b background/background-2.png --p shape/shape-2.png --c color/color-2.png --mode **_feature_** --models ../models/bird --out ./feature-2.png
got feature-2.png
and I run the:
python eval.py --z pose/pose-2.png --b background/background-2.png --p shape/shape-2.png --c color/color-2.png --mode **_code_** --models ../models/bird --out ./code-2.png
got code-2.png
the feature-2.png is the same as /code/result/0001.png
but when I check, pose-2.png , background-2.png, shape-2.png , color-2.png.
check image :
Conclusion:
background of background-2.png, (checked )
shape of shape-2.png, (checked )
texture of color-2.png , (checked )
but different pose from pose-2.png (failed)
background of background-2.png, (checked )
shape of shape-2.png, (maybe checked )
texture of color-2.png , (checked )
but different pose from pose-2.png (checked)
My question is why it happened?
background_stage network : this line comment
because ngf = cfg.GAN_GF_DIM = 64
So ngf*8 = 512
.
The output feature of self.fc is ngf*8*4*4 = 512 *4*4
, not 1024*4*4
So this function all comment is wrong
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 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. 📊📈🎉
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 ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.