interactive-deep-colorization | Deep learning software | Machine Learning library
kandi X-RAY | interactive-deep-colorization Summary
kandi X-RAY | interactive-deep-colorization Summary
Project Page | Paper | Demo Video | SIGGRAPH Talk. 04/10/2020 Update: @mabdelhack provided a windows installation guide for the PyTorch model in Python 3.6. Check out the Windows branch for the guide. 10/3/2019 Update: Our technology is also now available in Adobe Photoshop Elements 2020. See this blog and video for more details. 9/3/2018 Update: The code now supports a backend PyTorch model (with PyTorch 0.5.0+). Please find the Local Hints Network training code in the colorization-pytorch repository. Real-Time User-Guided Image Colorization with Learned Deep Priors. Richard Zhang*, Jun-Yan Zhu*, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, and Alexei A. Efros. In ACM Transactions on Graphics (SIGGRAPH 2017). (*indicates equal contribution). We first describe the system (0) Prerequisities and steps for (1) Getting started. We then describe the interactive colorization demo (2) Interactive Colorization (Local Hints Network). There are two demos: (a) a "barebones" version in iPython notebook and (b) the full GUI we used in our paper. We then provide an example of the (3) Global Hints Network.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- R snap the color of the input array
- Convert a lab to RGB
- Convert from RGB to Lab labels
- Perform the net forward
- Transpose lab to RGB
- Transpose rgb to lab
- Read image
- Predict the color of the image
- Mouse press event
- Calibrate the color of the image
- Network forward computation
- Performs the net forward transformation
- Calibrate color based on given position
- Moves the mouse click event
- Create directories
- Set color
- Return the full resolution of the full image
- Go to the next image
- Mouse press event
- Set image
- Performs a net forward computation
- Load image
- Update image image
- Parse command line arguments
- Handle key press events
- Perform the net forward computation
- Compute the PSNR of the result
interactive-deep-colorization Key Features
interactive-deep-colorization Examples and Code Snippets
@article{Lee_2020_CTHA,
author = {Lee, Junyong and Son, Hyeongseok and Lee, Gunhee and Lee, Jonghyeop and Cho, Sunghyun and Lee, Seungyong},
title = {Deep Color Transfer using Histogram Analogy},
journal = {The Visual Computer},
volume = {36}
@inproceedings{tatiya2019deep,
title={Deep Multi-Sensory Object Category Recognition Using Interactive Behavioral Exploration},
author={Tatiya, Gyan and Sinapov, Jivko},
booktitle={2019 International Conference on Robotics and Automation (ICRA)
@article{HenzGastalOliveira_2018,
author = {Bernardo Henz and Eduardo S. L. Gastal and Manuel M. Oliveira},
title = {Deep Joint Design of Color Filter Arrays and Demosaicing},
journal = {Computer Graphics Forum},
volume = {37},
def interactTreap(root: Node | None, args: str) -> Node | None:
"""
Commands:
+ value to add value into treap
- value to erase all nodes with value
>>> root = interactTreap(None, "+1")
>>> inorder
def main(_):
"""Run an interactive console."""
code.interact()
return 0
Community Discussions
Trending Discussions on Machine Learning
QUESTION
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.
...ANSWER
Answered 2022-Feb-17 at 10:47You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
QUESTION
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset.
...ANSWER
Answered 2022-Jan-14 at 23:47Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model)
is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true
and y_pred
.
QUESTION
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi
I see these processes running
ANSWER
Answered 2021-Nov-23 at 06:13This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
QUESTION
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWER
Answered 2021-Nov-04 at 21:17No, they aren't comparable.
Your baseline model used X_train
to fit the model. Then you're using the fitted model to score the X_train
sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
X_train
sample. - Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
X_val
per fold).
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of best_model.best_score_
? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
QUESTION
I am not able to access jupyter lab created on google cloud
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
I tried the diagnostic tool, which gave the following result:
but didn't fix it.
Thanks in advance.
...ANSWER
Answered 2021-Aug-20 at 14:00You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
QUESTION
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:
...ANSWER
Answered 2021-Sep-29 at 22:47Turns out its just documented incorrectly.
In reality the export from brain.js is this:
QUESTION
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:
...ANSWER
Answered 2021-Sep-04 at 06:43You're right. Just one thing to consider for choosing OrdinalEncoder
or OneHotEncoder
is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]
orshirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white
category should be encoded as 0
and black
should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
QUESTION
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
QUESTION
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
Table example:
...ANSWER
Answered 2021-Aug-11 at 15:55You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install interactive-deep-colorization
Download the reference model
Install Caffe or PyTorch and 3rd party Python libraries (OpenCV, scikit-learn and scikit-image). See the Requirements for more details.
Install Caffe or PyTorch. The Caffe model is official. PyTorch is a reimplementation. You also need to add pycaffe to your PYTHONPATH. Use vi ~/.bashrc to edit the environment variables.
Install Caffe or PyTorch. The Caffe model is official. PyTorch is a reimplementation. Install Caffe: see the Caffe installation and Ubuntu installation document. Please compile the Caffe with the python layer support (set WITH_PYTHON_LAYER=1 in the Makefile.config) and build Caffe python library by make pycaffe. You also need to add pycaffe to your PYTHONPATH. Use vi ~/.bashrc to edit the environment variables. PYTHONPATH=/path/to/caffe/python:$PYTHONPATH LD_LIBRARY_PATH=/path/to/caffe/build/lib:$LD_LIBRARY_PATH Install PyTorch: see the PyTorch installation guide.
Install scikit-image, scikit-learn, opencv, Qt4, and QDarkStyle pacakges:
Docker: [OSX Docker file] and [OSX Installation video] by @vbisbest, [Docker file 2] (by @sabrinawallner) based on DL Docker.
More installation help (by @SleepProgger).
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page