🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

Overview

Monitor deep learning model training and hardware usage from mobile.

PyPI - Python Version PyPI Status Docs Twitter

🔥 Features

  • Monitor running experiments from mobile phone (or laptop) View Run
  • Monitor hardware usage on any computer with a single command
  • Integrate with just 2 lines of code (see examples below)
  • Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
  • Keep Tensorboard logs organized
  • Save and load checkpoints
  • API for custom visualizations Open In Colab Open In Colab
  • Pretty logs of training progress
  • Change hyper-parameters while the model is training
  • Open source! we also have a small hosted server for the mobile web app

Installation

You can install this package using PIP.

pip install labml

PyTorch example

Open In Colab Kaggle

from labml import tracker, experiment

with experiment.record(name='sample', exp_conf=conf):
    for i in range(50):
        loss, accuracy = train()
        tracker.save(i, {'loss': loss, 'accuracy': accuracy})

PyTorch Lightning example

Open In Colab Kaggle

from labml import experiment
from labml.utils.lightening import LabMLLighteningLogger

trainer = pl.Trainer(gpus=1, max_epochs=5, progress_bar_refresh_rate=20, logger=LabMLLighteningLogger())

with experiment.record(name='sample', exp_conf=conf, disable_screen=True):
        trainer.fit(model, data_loader)

TensorFlow 2.X Keras example

Open In Colab Kaggle

from labml import experiment
from labml.utils.keras import LabMLKerasCallback

with experiment.record(name='sample', exp_conf=conf):
    for i in range(50):
        model.fit(x_train, y_train, epochs=conf['epochs'], validation_data=(x_test, y_test),
                  callbacks=[LabMLKerasCallback()], verbose=None)

📚 Documentation

Guides

🖥 Screenshots

Formatted training loop output

Sample Logs

Custom visualizations based on Tensorboard logs

Analytics

Tools

Hosting your own experiments server

# Install the package
pip install labml-app

# Start the server

labml app-server

Training models on cloud

# Install the package
pip install labml_remote

# Initialize the project
labml_remote init

# Add cloud server(s) to .remote/configs.yaml

# Prepare the remote server(s)
labml_remote prepare

# Start a PyTorch distributed training job
labml_remote helper-torch-launch --cmd 'train.py' --nproc-per-node 2 --env GLOO_SOCKET_IFNAME enp1s0

Monitoring hardware usage

# Install packages and dependencies
pip install labml psutil py3nvml

# Start monitoring
labml monitor

Other Guides

Setting up a local Ubuntu workstation for deep learning

Setting up a local cloud computer for deep learning

Citing

If you use LabML for academic research, please cite the library using the following BibTeX entry.

@misc{labml,
 author = {Varuna Jayasiri, Nipun Wijerathne},
 title = {labml.ai: A library to organize machine learning experiments},
 year = {2020},
 url = {https://labml.ai/},
}
Comments
  • Labml is stuck

    Labml is stuck

    I can't do anything with labml, even if it is as simple as executing: image

    It is stuck forever, and I literally downloaded and executed your notebook https://colab.research.google.com/github/lab-ml/labml/blob/master/guides/monitor.ipynb

    and it gets stuck at the sixth cell: tracker.set_queue('loss.train', 20, True)

    image

    What should I do please?

    bug help wanted 
    opened by shetsecure 11
  • Remove git commits/branches check

    Remove git commits/branches check

    Hello there!

    First of all thanks for your library, used it in my recent open source project!

    Now, I want to share my criticism.

    I have another project, but there we decided to set remotes' names of our repo different from default: bars and upstream, there is no origin as you can see.

    So I had this error:

    .labml.yml:

    check_repo_dirty: false
    experiments_path: '.labml'
    web_api: 'secret'
    

    Error:

    Traceback (most recent call last):
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/src/train.py", line 395, in <module>
        train()
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/src/train.py", line 354, in train
        with experiment.record(name=MODEL_SAVE_NAME, exp_conf=args.__dict__) if args.labml else ExitStack():
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/labml/experiment.py", line 388, in rec
    ord
        create(name=name,
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/labml/experiment.py", line 86, in crea
    te
        _create_experiment(uuid=uuid,
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/labml/internal/experiment/__init__.py"
    , line 511, in create_experiment
        _internal = Experiment(uuid=uuid,
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/labml/internal/experiment/__init__.py"
    , line 225, in __init__
        self.run.repo_remotes = list(repo.remote().urls)
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/git/remote.py", line 553, in urls
        raise ex
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/git/remote.py", line 529, in urls
        remote_details = self.repo.git.remote("get-url", "--all", self.name)
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/git/cmd.py", line 545, in <lambda>
        return lambda *args, **kwargs: self._call_process(name, *args, **kwargs)
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/git/cmd.py", line 1011, in _call_process
        return self.execute(call, **exec_kwargs)
      File "/media/sviperm/9740514d-d8c8-4f3e-afee-16ce6923340c3/sviperm/Documents/Aurora/Aurora.ContextualMistakes/venv/lib/python3.9/site-packages/git/cmd.py", line 828, in execute
        raise GitCommandError(command, status, stderr_value, stdout_value)
    git.exc.GitCommandError: Cmd('git') failed due to: exit code(128)
      cmdline: git remote get-url --all origin
      stderr: 'fatal: No such remote 'origin''
    

    So my question â„–1 is: why labml check git branches/remote/commits? What is the idea behind this logic? I think that library for training monitoring for ML project doesn't need to do that. If developer / data scientist want to track git and prevent training because of uncommited changes, he/she can write this logic by his own.

    Question â„–2: if i set check_repo_dirty: false why labml still checking repo? And what is the default value of the parameter?

    2 possible suggestions:

    1. Put condition before try in labml/internal/experiment/__init__.py to prevent all this git code :
      if self.check_repo_dirty:
          try:
              repo = git.Repo(lab_singleton().path)
      
              self.run.repo_remotes = list(repo.remote().urls)
              self.run.commit = repo.head.commit.hexsha
              self.run.commit_message = repo.head.commit.message.strip()
              self.run.is_dirty = repo.is_dirty()
              self.run.diff = repo.git.diff()
          except git.InvalidGitRepositoryError:
              if not is_colab() and not is_kaggle():
                  labml_notice(["Not a valid git repository: ",
                                (str(lab_singleton().path), Text.value)])
              self.run.commit = 'unknown'
              self.run.commit_message = ''
              self.run.is_dirty = True
              self.run.diff = ''
      
    2. Or completely remove all this git tracking code or make it deprecated.

    Thanks!

    bug 
    opened by sviperm 6
  • Checkpointing optimizers

    Checkpointing optimizers

    Hi, I am working with your framework. First of all, great job. It really saved me from the usual research mess :) I have some questions about the checkpointing. I've seen that each layer is saved in a .npy format. However, this does not work for other objects that are based on state_dict, for example optimizers. For long trainings they should be saved with the model, since we don't want to retrain the whole model from scratch. I've looked into your checkpointing strategy here. Do you see any significant problem if instead saving all layers in .npy files we directly save the state_dict?

    opened by fabvio 5
  • Network error in comparison section

    Network error in comparison section

    Issue: when runs were added to comparison section then were deleted, there is network error + 404 error

    image image

    Run in app.labml.ai

    How to reproduce: create 2 runs, add one to another to compare, then delete run which was added

    Tested in incognito-mode tab, so this is no cache/cookies problem

    bug 
    opened by sviperm 4
  • 500 Error Issue

    500 Error Issue

    Hi,

    I see this error "Oops! Something went wrong 500 Seems like we are having issues right now".

    I'm also unable to run the app locally. Please advise.

    labml app-server gives me: labml: error: argument command: invalid choice: 'app-server' (choose from 'dashboard', 'capture', 'launch', 'monitor', 'service', 'service-run')

    opened by Ananya-Joshi 3
  • Added tensorboard image logging

    Added tensorboard image logging

    Hi. I've seen that the Artifact class is used to print outputs during training time. I usually keep track of outputs during training. I added support in tensorboard for image artifact logging.

    What I did is checking the is_print value is true while the print_all method is called. This is to avoid unnecessary outputs. Then, in the tensorboard writer, I iterate over Image artifacts and write them using tf.summary.image. The method should be extensible seamlessly to other artifact types.

    I assume that artifacts are flushed once they are printed or overlapped, so this won't create memory issues during training. Am I wrong?

    opened by fabvio 3
  • 502 - Bad Gateway

    502 - Bad Gateway

    Hi, since yesterday, I constantly receive the message '502 Bad Gateway' every time I launch an labml experiment, both from jupyter notebook and from Colab. Here an example:

    Schermata da 2021-10-11 21-10-26

    Moreover, I get this error from https://app.labml.ai/runs : Schermata da 2021-10-11 21-04-28

    Is there a problem with your app?

    Thanks in advantage.

    opened by EMalagoli92 2
  • tensorflow import?

    tensorflow import?

    I don't think the tensorflow import in the experiments.pytorch file is necessary - you can write to tensorboard without tensorflow.

    Indeed, some of your usages are actually deprecated.

    opened by MiroFurtado 2
  • UnicodeEncodeError: 'gbk' codec can't encode character

    UnicodeEncodeError: 'gbk' codec can't encode character

    Hello, first of all thank you for your open source, I'm trying to learn to use this module recently. But today I got the following error when running the model, it seems like there are some errors when I follow the GIt commit? What should I do to fix it ?

    image

    opened by CHENHUI-X 1
  • Some private runs visible on homepage before login or refresh

    Some private runs visible on homepage before login or refresh

    The current app.labml.ai homepage somehow displays some private runs that are not supposed to be visible to public when visiting the website first time, even without login. It only asked for login after one refresh or revisit.

    opened by adrien1018 1
  • Feature request: Allow setting listen address on command line & infer URL from request

    Feature request: Allow setting listen address on command line & infer URL from request

    Currently the app is fixed to listen on 0.0.0.0:5005. It would be great if the bind address & port can be set from command line (e.g. labml app-server --bind-address=127.0.0.1 --port=5678).

    Also, the webpage will always try to fetch data from localhost:5005, making it inconvenient to connect to the server from a non-local machine. The host URL should be inferred from the request instead of being hardcoded.

    enhancement app 
    opened by adrien1018 1
  • Bump setuptools from 62.3.3 to 65.5.1 in /app/server

    Bump setuptools from 62.3.3 to 65.5.1 in /app/server

    Bumps setuptools from 62.3.3 to 65.5.1.

    Release notes

    Sourced from setuptools's releases.

    v65.5.1

    No release notes provided.

    v65.5.0

    No release notes provided.

    v65.4.1

    No release notes provided.

    v65.4.0

    No release notes provided.

    v65.3.0

    No release notes provided.

    v65.2.0

    No release notes provided.

    v65.1.1

    No release notes provided.

    v65.1.0

    No release notes provided.

    v65.0.2

    No release notes provided.

    v65.0.1

    No release notes provided.

    v65.0.0

    No release notes provided.

    v64.0.3

    No release notes provided.

    v64.0.2

    No release notes provided.

    v64.0.1

    No release notes provided.

    v64.0.0

    No release notes provided.

    v63.4.3

    No release notes provided.

    v63.4.2

    No release notes provided.

    ... (truncated)

    Changelog

    Sourced from setuptools's changelog.

    v65.5.1

    Misc ^^^^

    • #3638: Drop a test dependency on the mock package, always use :external+python:py:mod:unittest.mock -- by :user:hroncok
    • #3659: Fixed REDoS vector in package_index.

    v65.5.0

    Changes ^^^^^^^

    • #3624: Fixed editable install for multi-module/no-package src-layout projects.
    • #3626: Minor refactorings to support distutils using stdlib logging module.

    Documentation changes ^^^^^^^^^^^^^^^^^^^^^

    • #3419: Updated the example version numbers to be compliant with PEP-440 on the "Specifying Your Project’s Version" page of the user guide.

    Misc ^^^^

    • #3569: Improved information about conflicting entries in the current working directory and editable install (in documentation and as an informational warning).
    • #3576: Updated version of validate_pyproject.

    v65.4.1

    Misc ^^^^

    v65.4.0

    Changes ^^^^^^^

    v65.3.0

    ... (truncated)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies python 
    opened by dependabot[bot] 0
  • Bump certifi from 2022.5.18.1 to 2022.12.7 in /app/server

    Bump certifi from 2022.5.18.1 to 2022.12.7 in /app/server

    Bumps certifi from 2022.5.18.1 to 2022.12.7.

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies python 
    opened by dependabot[bot] 0
  • Tracker bug: UnicodeEncodeError: 'charmap' codec can't encode characters

    Tracker bug: UnicodeEncodeError: 'charmap' codec can't encode characters

    After I stopped the training with tracker and start it again, I see the following error from experiment.record(name=args.experiment_name)

    Traceback (most recent call last):
      File "model_combined.py", line 282, in <module>
        with experiment.record(name=args.experiment_name):
      File "C:\Users\miles\anaconda3\envs\idio\lib\site-packages\labml\experiment.py", line 439, in record
        return start()
      File "C:\Users\miles\anaconda3\envs\idio\lib\site-packages\labml\experiment.py", line 278, in start
        return _experiment_singleton().start(run_uuid=_load_run_uuid, checkpoint=_load_checkpoint)
      File "C:\Users\miles\anaconda3\envs\idio\lib\site-packages\labml\internal\experiment\__init__.py", line 463, in start
        self.run.save_info()
      File "C:\Users\miles\anaconda3\envs\idio\lib\site-packages\labml\internal\experiment\experiment_run.py", line 249, in save_info
        f.write(self.diff)
      File "C:\Users\me\anaconda3\envs\idio\lib\encodings\cp1252.py", line 19, in encode
        return codecs.charmap_encode(input,self.errors,encoding_table)[0]
    UnicodeEncodeError: 'charmap' codec can't encode characters in position 2827-2831: character maps to <undefined>
    
    opened by MeNicefellow 0
  • Hardware Naming In Monitor

    Hardware Naming In Monitor

    I'm trying to monitor multiple machines' usages. Their names in the dashbord are always My Computer. It seems there is no option in configs.yaml for naming a machine.

    opened by MeNicefellow 1
  • Bump d3-color and d3 in /app/ui

    Bump d3-color and d3 in /app/ui

    Bumps d3-color to 3.1.0 and updates ancestor dependency d3. These dependencies need to be updated together.

    Updates d3-color from 1.4.1 to 3.1.0

    Release notes

    Sourced from d3-color's releases.

    v3.1.0

    v3.0.1

    • Make build reproducible.

    v3.0.0

    • Adopt type: module.

    This package now requires Node.js 12 or higher. For more, please read Sindre Sorhus’s FAQ.

    v2.0.0

    This release adopts ES2015 language features such as for-of and drops support for older browsers, including IE. If you need to support pre-ES2015 environments, you should stick with d3-color 1.x or use a transpiler.

    Commits

    Updates d3 from 5.16.0 to 7.6.1

    Release notes

    Sourced from d3's releases.

    v7.6.1

    v7.6.0

    v7.5.0

    v7.4.5

    v7.4.4

    • Fix incorrect behavior of d3.bisector when given an asymmetric comparator.

    v7.4.3

    v7.4.2

    • Fix off-by-one bin assignment due to rounding error in d3.bin.

    v7.4.1

    • Significantly improve the performance of d3.bin.
    • Fix the implementation of d3.thresholdScott.
    • d3.pack and d3.packEnclose are now fully deterministic.
    • d3.pack and d3.packEnclose now handle certain floating point errors better.

    v7.4.0

    v7.3.0

    v7.2.1

    • Fix stratify.path when the top-level directory is only a single character.

    ... (truncated)

    Commits

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies javascript 
    opened by dependabot[bot] 0
Releases(v0.4.132)
Owner
labml.ai
Tools to help deep learning researchers
labml.ai
The official PyTorch implementation for the paper "sMGC: A Complex-Valued Graph Convolutional Network via Magnetic Laplacian for Directed Graphs".

Magnetic Graph Convolutional Networks About The official PyTorch implementation for the paper sMGC: A Complex-Valued Graph Convolutional Network via M

3 Feb 25, 2022
Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers.

Less is More: Pay Less Attention in Vision Transformers Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers. By

73 Jan 01, 2023
A curated list of awesome Model-Based RL resources

Awesome Model-Based Reinforcement Learning This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository w

OpenDILab 427 Jan 03, 2023
Episodic-memory - Ego4D Episodic Memory Benchmark

Ego4D Episodic Memory Benchmark EGO4D is the world's largest egocentric (first p

3 Feb 18, 2022
Generate indoor scenes with Transformers

SceneFormer: Indoor Scene Generation with Transformers Initial code release for the Sceneformer paper, contains models, train and test scripts for the

Chandan Yeshwanth 110 Dec 06, 2022
Full Stack Deep Learning Labs

Full Stack Deep Learning Labs Welcome! Project developed during lab sessions of the Full Stack Deep Learning Bootcamp. We will build a handwriting rec

Full Stack Deep Learning 1.2k Dec 31, 2022
HyperPose is a library for building high-performance custom pose estimation applications.

HyperPose is a library for building high-performance custom pose estimation applications.

TensorLayer Community 1.2k Jan 04, 2023
RSNA Intracranial Hemorrhage Detection with python

RSNA Intracranial Hemorrhage Detection This is the source code for the first place solution to the RSNA2019 Intracranial Hemorrhage Detection Challeng

24 Nov 30, 2022
MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet.

Lightweight-Detection-and-KD MMdet2-based reposity about lightweight detection model: Nanodet, PicoDet. This repo also includes detection knowledge di

Egqawkq 12 Jan 05, 2023
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition Introduction Run attack: SGADV.py Objective function: foolbox/attacks/gradi

1 Jul 18, 2022
Neural Network to colorize grayscale images

#colornet Neural Network to colorize grayscale images Results Grayscale Prediction Ground Truth Eiji K used colornet for anime colorization Sources Au

Pavel Hanchar 3.6k Dec 24, 2022
CNN visualization tool in TensorFlow

tf_cnnvis A blog post describing the library: https://medium.com/@falaktheoptimist/want-to-look-inside-your-cnn-we-have-just-the-right-tool-for-you-ad

InFoCusp 778 Jan 02, 2023
Pytorch implementation of MLP-Mixer with loading pre-trained models.

MLP-Mixer-Pytorch PyTorch implementation of MLP-Mixer: An all-MLP Architecture for Vision with the function of loading official ImageNet pre-trained p

Qiushi Yang 2 Sep 29, 2022
Implementation of paper "DeepTag: A General Framework for Fiducial Marker Design and Detection"

Implementation of paper DeepTag: A General Framework for Fiducial Marker Design and Detection. Project page: https://herohuyongtao.github.io/research/

Yongtao Hu 46 Dec 12, 2022
Compressed Video Action Recognition

Compressed Video Action Recognition Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl. In CVPR, 2018. [Proj

Chao-Yuan Wu 479 Dec 26, 2022
Pytorch version of VidLanKD: Improving Language Understanding viaVideo-Distilled Knowledge Transfer

VidLanKD Implementation of VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer by Zineng Tang, Jaemin Cho, Hao Tan, Mohi

Zineng Tang 54 Dec 20, 2022
Code for unmixing audio signals in four different stems "drums, bass, vocals, others". The code is adapted from "Jukebox: A Generative Model for Music"

Status: Archive (code is provided as-is, no updates expected) Disclaimer This code is a based on "Jukebox: A Generative Model for Music" Paper We adju

Wadhah Zai El Amri 24 Dec 29, 2022
An Implementation of SiameseRPN with Feature Pyramid Networks

SiameseRPN with FPN This project is mainly based on HelloRicky123/Siamese-RPN. What I've done is just add a Feature Pyramid Network method to the orig

3 Apr 16, 2022
BraTs-VNet - BraTS(Brain Tumour Segmentation) using V-Net

BraTS(Brain Tumour Segmentation) using V-Net This project is an approach to dete

Rituraj Dutta 7 Nov 27, 2022