pytorch-image-models | PyTorch image models , scripts , pretrained weights -- ResNet | Machine Learning library
kandi X-RAY | pytorch-image-models Summary
kandi X-RAY | pytorch-image-models Summary
PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results. The work of many others is present here. I've tried to make sure all source material is acknowledged via links to github, arxiv papers, etc in the README, documentation, and code docstrings. Please let me know if I missed anything.
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Top functions reviewed by kandi - BETA
- Create an Optimizer V2 .
- Create selecs .
- Initialize the network .
- Decode a block string .
- Create a loader .
- Train one epoch .
- Extract tar files from a directory .
- Create a scheduler .
- Generate mobilenet V3 .
- Create a dataset .
pytorch-image-models Key Features
pytorch-image-models Examples and Code Snippets
@article{DBLP:journals/corr/abs-1804-00097,
author = {Alexey Kurakin and
Ian J. Goodfellow and
Samy Bengio and
Yinpeng Dong and
Fangzhou Liao and
Ming Liang and
@article{DBLP:journals/corr/abs-1804-00097,
author = {Alexey Kurakin and
Ian J. Goodfellow and
Samy Bengio and
Yinpeng Dong and
Fangzhou Liao and
Ming Liang and
import timm
model = timm.create_model('{{ model_name }}', pretrained=True)
model.eval()
import urllib
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
config = resolve_data_co
"""
Run this script to generate the model-index files in `models` from the templates in `.templates/models`.
"""
import argparse
from pathlib import Path
from jinja2 import Environment, FileSystemLoader
import modelindex
def generate_readmes(tem
app.location$.subscribe(function() {
var tables = document.querySelectorAll("article table")
tables.forEach(function(table) {
new Tablesort(table)
})
})
Community Discussions
Trending Discussions on pytorch-image-models
QUESTION
I'm looking at the timm
implementation of visual transformers and for the positional embedding, he is initializing his position embedding with zeros as follows:
ANSWER
Answered 2021-Mar-11 at 21:30The positional embedding is a parameter that gets included in the computational graph and gets updated during training. So, it doesn't matter if you initialize with zeros; they are learned during training.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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