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### File: qt_gui_py_common/setup.py
#!/usr/bin/env python
from distutils.core import setup
from catkin_pkg.python_setup import generate_distutils_setup
d = generate_distutils_setup(
packages=['qt_gui_py_common'],
package_dir={'': 'src'}
)
setup(**d)
| {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/qt_gui_core", "path": "qt_gui_py_common/setup.py", "license": "bsd-3-clause", "size": 226} |
### File: qt_gui_py_common/src/qt_gui_py_common/checkbox_group.py
# Software License Agreement (BSD License)
#
# Copyright (c) 2014, Andrew Wilson
# Copyright (c) 2012, Dorian Scholz
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/qt_gui_core", "path": "qt_gui_py_common/src/qt_gui_py_common/checkbox_group.py", "license": "bsd-3-clause", "size": 3388} |
### File: qt_gui_py_common/src/qt_gui_py_common/console_text_edit.py
# Software License Agreement (BSD License)
#
# Copyright (c) 2012, Dorian Scholz
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are m... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/qt_gui_core", "path": "qt_gui_py_common/src/qt_gui_py_common/console_text_edit.py", "license": "bsd-3-clause", "size": 7347} |
### File: qt_gui_py_common/src/qt_gui_py_common/exclusive_options_group.py
# Software License Agreement (BSD License)
#
# Copyright (c) 2012, Dorian Scholz
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
#... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/qt_gui_core", "path": "qt_gui_py_common/src/qt_gui_py_common/exclusive_options_group.py", "license": "bsd-3-clause", "size": 3350} |
### File: qt_gui_py_common/src/qt_gui_py_common/simple_settings_dialog.py
# Software License Agreement (BSD License)
#
# Copyright (c) 2012, Dorian Scholz
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/qt_gui_core", "path": "qt_gui_py_common/src/qt_gui_py_common/simple_settings_dialog.py", "license": "bsd-3-clause", "size": 3947} |
### File: qt_gui_py_common/src/qt_gui_py_common/worker_thread.py
# Software License Agreement (BSD License)
#
# Copyright (c) 2012, Dorian Scholz
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/qt_gui_core", "path": "qt_gui_py_common/src/qt_gui_py_common/worker_thread.py", "license": "bsd-3-clause", "size": 2611} |
### File: rcl_logging_interface/CMakeLists.txt
cmake_minimum_required(VERSION 3.5)
project(rcl_logging_interface)
# Default to C11
if(NOT CMAKE_C_STANDARD)
set(CMAKE_C_STANDARD 11)
endif()
# Default to C++17
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
endif()
if(NO... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_interface/CMakeLists.txt", "license": "apache-2.0", "size": 1682} |
### File: rcl_logging_interface/include/rcl_logging_interface/rcl_logging_interface.h
// Copyright 2020 Open Source Robotics Foundation, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_interface/include/rcl_logging_interface/rcl_logging_interface.h", "license": "apache-2.0", "size": 508... |
### File: rcl_logging_interface/include/rcl_logging_interface/visibility_control.h
// Copyright 2020 Open Source Robotics Foundation, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_interface/include/rcl_logging_interface/visibility_control.h", "license": "apache-2.0", "size": 2078} |
### File: rcl_logging_interface/src/logging_dir.c
// Copyright 2020 Open Source Robotics Foundation, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/l... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_interface/src/logging_dir.c", "license": "apache-2.0", "size": 3400} |
### File: rcl_logging_interface/test/test_get_logging_directory.cpp
// Copyright 2020 Open Source Robotics Foundation, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http:... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_interface/test/test_get_logging_directory.cpp", "license": "apache-2.0", "size": 8725} |
### File: rcl_logging_noop/CMakeLists.txt
cmake_minimum_required(VERSION 3.5)
project(rcl_logging_noop)
# Default to C11
if(NOT CMAKE_C_STANDARD)
set(CMAKE_C_STANDARD 11)
endif()
# Default to C++17
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
endif()
find_package(am... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_noop/CMakeLists.txt", "license": "apache-2.0", "size": 1144} |
### File: rcl_logging_noop/src/rcl_logging_noop.cpp
// Copyright 2018 Open Source Robotics Foundation, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_noop/src/rcl_logging_noop.cpp", "license": "apache-2.0", "size": 1320} |
### File: rcl_logging_spdlog/CMakeLists.txt
cmake_minimum_required(VERSION 3.5)
project(rcl_logging_spdlog)
# Default to C11
if(NOT CMAKE_C_STANDARD)
set(CMAKE_C_STANDARD 11)
endif()
# Default to C++17
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
endif()
find_packag... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "CMake", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_spdlog/CMakeLists.txt", "license": "apache-2.0", "size": 2418} |
### File: rcl_logging_spdlog/src/rcl_logging_spdlog.cpp
// Copyright 2019 Open Source Robotics Foundation, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_spdlog/src/rcl_logging_spdlog.cpp", "license": "apache-2.0", "size": 8006} |
### File: rcl_logging_spdlog/test/benchmark/benchmark_logging_interface.cpp
// Copyright 2020 Open Source Robotics Foundation, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_spdlog/test/benchmark/benchmark_logging_interface.cpp", "license": "apache-2.0", "size": 3762} |
### File: rcl_logging_spdlog/test/test_logging_interface.cpp
// Copyright 2020 Open Source Robotics Foundation, Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.a... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "C++", "repo_name": "minhanghuang/rcl_logging", "path": "rcl_logging_spdlog/test/test_logging_interface.cpp", "license": "apache-2.0", "size": 12908} |
### File: avg_checkpoints.py
#!/usr/bin/env python3
""" Checkpoint Averaging Script
This script averages all model weights for checkpoints in specified path that match
the specified filter wildcard. All checkpoints must be from the exact same model.
For any hope of decent results, the checkpoints should be from the s... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "avg_checkpoints.py", "license": "apache-2.0", "size": 5930} |
### File: benchmark.py
#!/usr/bin/env python3
""" Model Benchmark Script
An inference and train step benchmark script for timm models.
Hacked together by Ross Wightman (https://github.com/rwightman)
"""
import argparse
import csv
import json
import logging
import time
from collections import OrderedDict
from contextl... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "benchmark.py", "license": "apache-2.0", "size": 28416} |
### File: bulk_runner.py
#!/usr/bin/env python3
""" Bulk Model Script Runner
Run validation or benchmark script in separate process for each model
Benchmark all 'vit*' models:
python bulk_runner.py --model-list 'vit*' --results-file vit_bench.csv benchmark.py --amp -b 512
Validate all models:
python bulk_runner.py ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "bulk_runner.py", "license": "apache-2.0", "size": 6518} |
### File: clean_checkpoint.py
#!/usr/bin/env python3
""" Checkpoint Cleaning Script
Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
and outputs a CPU tensor checkpoint with only the `state_dict` along with SHA256
calculation for model zoo compatibility.
Hacked together by / Copyri... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "clean_checkpoint.py", "license": "apache-2.0", "size": 4221} |
### File: convert/convert_from_mxnet.py
import argparse
import hashlib
import os
import mxnet as mx
import gluoncv
import torch
from timm import create_model
parser = argparse.ArgumentParser(description='Convert from MXNet')
parser.add_argument('--model', default='all', type=str, metavar='MODEL',
... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "convert/convert_from_mxnet.py", "license": "apache-2.0", "size": 4033} |
### File: convert/convert_nest_flax.py
"""
Convert weights from https://github.com/google-research/nested-transformer
NOTE: You'll need https://github.com/google/CommonLoopUtils, not included in requirements.txt
"""
import sys
import numpy as np
import torch
from clu import checkpoint
arch_depths = {
'nest_bas... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "convert/convert_nest_flax.py", "license": "apache-2.0", "size": 5582} |
### File: distributed_train.sh
#!/bin/bash
NUM_PROC=$1
shift
torchrun --nproc_per_node=$NUM_PROC train.py "$@"
| {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Shell", "repo_name": "minhanghuang/pytorch-image-models", "path": "distributed_train.sh", "license": "apache-2.0", "size": 81} |
### File: docs/javascripts/tables.js
app.location$.subscribe(function() {
var tables = document.querySelectorAll("article table")
tables.forEach(function(table) {
new Tablesort(table)
})
}) | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "JavaScript", "repo_name": "minhanghuang/pytorch-image-models", "path": "docs/javascripts/tables.js", "license": "apache-2.0", "size": 162} |
### File: docs/models/.templates/generate_readmes.py
"""
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(templates_path: Path... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "Python", "repo_name": "minhanghuang/pytorch-image-models", "path": "docs/models/.templates/generate_readmes.py", "license": "apache-2.0", "size": 1798} |
### File: hfdocs/source/feature_extraction.mdx
# Feature Extraction
All of the models in `timm` have consistent mechanisms for obtaining various types of features from the model for tasks besides classification.
## Penultimate Layer Features (Pre-Classifier Features)
The features from the penultimate model layer can... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/feature_extraction.mdx", "license": "apache-2.0", "size": 6285} |
### File: hfdocs/source/hf_hub.mdx
# Sharing and Loading Models From the Hugging Face Hub
The `timm` library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub.
In this short guide, we'll see how to:
1. Share a `timm` model on the Hub
2. How to load that ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/hf_hub.mdx", "license": "apache-2.0", "size": 1860} |
### File: hfdocs/source/index.mdx
# timm
<img class="float-left !m-0 !border-0 !dark:border-0 !shadow-none !max-w-lg w-[150px]" src="https://huggingface.co/front/thumbnails/docs/timm.png"/>
`timm` is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentatio... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/index.mdx", "license": "apache-2.0", "size": 1579} |
### File: hfdocs/source/installation.mdx
# Installation
Before you start, you'll need to setup your environment and install the appropriate packages. `timm` is tested on **Python 3+**.
## Virtual Environment
You should install `timm` in a [virtual environment](https://docs.python.org/3/library/venv.html) to keep thi... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/installation.mdx", "license": "apache-2.0", "size": 2042} |
### File: hfdocs/source/models.mdx
# Model Summaries
The model architectures included come from a wide variety of sources. Sources, including papers, original impl ("reference code") that I rewrote / adapted, and PyTorch impl that I leveraged directly ("code") are listed below.
Most included models have pretrained we... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models.mdx", "license": "apache-2.0", "size": 12526} |
### File: hfdocs/source/models/adversarial-inception-v3.mdx
# Adversarial Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolu... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/adversarial-inception-v3.mdx", "license": "apache-2.0", "size": 5877} |
### File: hfdocs/source/models/advprop.mdx
# AdvProp (EfficientNet)
**AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlyi... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/advprop.mdx", "license": "apache-2.0", "size": 14755} |
### File: hfdocs/source/models/big-transfer.mdx
# Big Transfer (BiT)
**Big Transfer (BiT)** is a type of pretraining recipe that pre-trains on a large supervised source dataset, and fine-tunes the weights on the target task. Models are trained on the JFT-300M dataset. The finetuned models contained in this collection... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/big-transfer.mdx", "license": "apache-2.0", "size": 10292} |
### File: hfdocs/source/models/csp-darknet.mdx
# CSP-DarkNet
**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then mer... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/csp-darknet.mdx", "license": "apache-2.0", "size": 4921} |
### File: hfdocs/source/models/csp-resnet.mdx
# CSP-ResNet
**CSPResNet** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNet](https://paperswithcode.com/method/resnet). The CSPNet partitions the feature map of the base layer into two parts and then merges them ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/csp-resnet.mdx", "license": "apache-2.0", "size": 4780} |
### File: hfdocs/source/models/csp-resnext.mdx
# CSP-ResNeXt
**CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/csp-resnext.mdx", "license": "apache-2.0", "size": 4820} |
### File: hfdocs/source/models/densenet.mdx
# DenseNet
**DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each othe... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/densenet.mdx", "license": "apache-2.0", "size": 10538} |
### File: hfdocs/source/models/dla.mdx
# Deep Layer Aggregation
Extending “shallow” skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/dla.mdx", "license": "apache-2.0", "size": 16511} |
### File: hfdocs/source/models/dpn.mdx
# Dual Path Network (DPN)
A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while DenseNet enables new fe... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/dpn.mdx", "license": "apache-2.0", "size": 9510} |
### File: hfdocs/source/models/ecaresnet.mdx
# ECA-ResNet
An **ECA ResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that utilises an [Efficient Channel Attention module](https://paperswithcode.com/method/efficient-channel-attention). Efficient Channel Attention is an architectural unit bas... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/ecaresnet.mdx", "license": "apache-2.0", "size": 9334} |
### File: hfdocs/source/models/efficientnet-pruned.mdx
# EfficientNet (Knapsack Pruned)
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales t... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/efficientnet-pruned.mdx", "license": "apache-2.0", "size": 7711} |
### File: hfdocs/source/models/efficientnet.mdx
# EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the Efficie... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/efficientnet.mdx", "license": "apache-2.0", "size": 12819} |
### File: hfdocs/source/models/ensemble-adversarial.mdx
# # Ensemble Adversarial Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replac... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/ensemble-adversarial.mdx", "license": "apache-2.0", "size": 5680} |
### File: hfdocs/source/models/ese-vovnet.mdx
# ESE-VoVNet
**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new out... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/ese-vovnet.mdx", "license": "apache-2.0", "size": 5255} |
### File: hfdocs/source/models/fbnet.mdx
# FBNet
**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2)... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/fbnet.mdx", "license": "apache-2.0", "size": 4852} |
### File: hfdocs/source/models/gloun-inception-v3.mdx
# (Gluon) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/gloun-inception-v3.mdx", "license": "apache-2.0", "size": 5243} |
### File: hfdocs/source/models/gloun-resnet.mdx
# (Gluon) ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these l... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/gloun-resnet.mdx", "license": "apache-2.0", "size": 17300} |
### File: hfdocs/source/models/gloun-resnext.mdx
# (Gluon) ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *c... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/gloun-resnext.mdx", "license": "apache-2.0", "size": 6930} |
### File: hfdocs/source/models/gloun-senet.mdx
# (Gluon) SENet
A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weight... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/gloun-senet.mdx", "license": "apache-2.0", "size": 4347} |
### File: hfdocs/source/models/gloun-seresnext.mdx
# (Gluon) SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/gloun-seresnext.mdx", "license": "apache-2.0", "size": 6541} |
### File: hfdocs/source/models/gloun-xception.mdx
# (Gluon) Xception
**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) layers.
The weights from this model were ported from [Gluon](https... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/gloun-xception.mdx", "license": "apache-2.0", "size": 4387} |
### File: hfdocs/source/models/hrnet.mdx
# HRNet
**HRNet**, or **High-Resolution Net**, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a hi... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/hrnet.mdx", "license": "apache-2.0", "size": 12884} |
### File: hfdocs/source/models/ig-resnext.mdx
# Instagram ResNeXt WSL
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/ig-resnext.mdx", "license": "apache-2.0", "size": 8412} |
### File: hfdocs/source/models/inception-resnet-v2.mdx
# Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatena... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/inception-resnet-v2.mdx", "license": "apache-2.0", "size": 4687} |
### File: hfdocs/source/models/inception-v3.mdx
# Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/inception-v3.mdx", "license": "apache-2.0", "size": 5245} |
### File: hfdocs/source/models/inception-v4.mdx
# Inception v4
**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/inception-v4.mdx", "license": "apache-2.0", "size": 4536} |
### File: hfdocs/source/models/legacy-se-resnet.mdx
# (Legacy) SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-w... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/legacy-se-resnet.mdx", "license": "apache-2.0", "size": 9452} |
### File: hfdocs/source/models/legacy-se-resnext.mdx
# (Legacy) SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic chan... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/legacy-se-resnext.mdx", "license": "apache-2.0", "size": 7043} |
### File: hfdocs/source/models/legacy-senet.mdx
# (Legacy) SENet
A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weig... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/legacy-senet.mdx", "license": "apache-2.0", "size": 4488} |
### File: hfdocs/source/models/mixnet.mdx
# MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
## How do I use this m... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/mixnet.mdx", "license": "apache-2.0", "size": 6974} |
### File: hfdocs/source/models/mnasnet.mdx
# MnasNet
**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/mnasnet.mdx", "license": "apache-2.0", "size": 5803} |
### File: hfdocs/source/models/mobilenet-v2.mdx
# MobileNet v2
**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are betwe... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/mobilenet-v2.mdx", "license": "apache-2.0", "size": 8705} |
### File: hfdocs/source/models/mobilenet-v3.mdx
# MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/mobilenet-v3.mdx", "license": "apache-2.0", "size": 6533} |
### File: hfdocs/source/models/nasnet.mdx
# NASNet
**NASNet** is a type of convolutional neural network discovered through neural architecture search. The building blocks consist of normal and reduction cells.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/nasnet.mdx", "license": "apache-2.0", "size": 4353} |
### File: hfdocs/source/models/noisy-student.mdx
# Noisy Student (EfficientNet)
**Noisy Student Training** is a semi-supervised learning approach. It extends the idea of self-training
and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/noisy-student.mdx", "license": "apache-2.0", "size": 16635} |
### File: hfdocs/source/models/pnasnet.mdx
# PNASNet
**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/pnasnet.mdx", "license": "apache-2.0", "size": 4648} |
### File: hfdocs/source/models/regnetx.mdx
# RegNetX
**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/regnetx.mdx", "license": "apache-2.0", "size": 16228} |
### File: hfdocs/source/models/regnety.mdx
# RegNetY
**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/regnety.mdx", "license": "apache-2.0", "size": 16816} |
### File: hfdocs/source/models/res2net.mdx
# Res2Net
**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). The motivation is to be able to represent features at multiple scales. This is achieved through a novel building ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/res2net.mdx", "license": "apache-2.0", "size": 10029} |
### File: hfdocs/source/models/res2next.mdx
# Res2NeXt
**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for C... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/res2next.mdx", "license": "apache-2.0", "size": 4908} |
### File: hfdocs/source/models/resnest.mdx
# ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/resnest.mdx", "license": "apache-2.0", "size": 13512} |
### File: hfdocs/source/models/resnet-d.mdx
# ResNet-D
**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/resnet-d.mdx", "license": "apache-2.0", "size": 10035} |
### File: hfdocs/source/models/resnet.mdx
# ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a re... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/resnet.mdx", "license": "apache-2.0", "size": 12639} |
### File: hfdocs/source/models/resnext.mdx
# ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (t... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/resnext.mdx", "license": "apache-2.0", "size": 7727} |
### File: hfdocs/source/models/rexnet.mdx
# RexNet
**Rank Expansion Networks** (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the [ReLU6s](https://www.pa... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/rexnet.mdx", "license": "apache-2.0", "size": 7936} |
### File: hfdocs/source/models/se-resnet.mdx
# SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature reca... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/se-resnet.mdx", "license": "apache-2.0", "size": 5783} |
### File: hfdocs/source/models/selecsls.mdx
# SelecSLS
**SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/selecsls.mdx", "license": "apache-2.0", "size": 6345} |
### File: hfdocs/source/models/seresnext.mdx
# SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resneXt) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/seresnext.mdx", "license": "apache-2.0", "size": 7020} |
### File: hfdocs/source/models/skresnet.mdx
# SK-ResNet
**SK ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in R... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/skresnet.mdx", "license": "apache-2.0", "size": 5655} |
### File: hfdocs/source/models/skresnext.mdx
# SK-ResNeXt
**SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/skresnext.mdx", "license": "apache-2.0", "size": 4605} |
### File: hfdocs/source/models/spnasnet.mdx
# SPNASNet
**Single-Path NAS** is a novel differentiable NAS method for designing hardware-efficient ConvNets in less than 4 hours.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('spnasnet_100', pret... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/spnasnet.mdx", "license": "apache-2.0", "size": 4251} |
### File: hfdocs/source/models/ssl-resnet.mdx
# SSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/ssl-resnet.mdx", "license": "apache-2.0", "size": 6567} |
### File: hfdocs/source/models/swsl-resnet.mdx
# SWSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layer... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/swsl-resnet.mdx", "license": "apache-2.0", "size": 6609} |
### File: hfdocs/source/models/swsl-resnext.mdx
# SWSL ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardi... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/swsl-resnext.mdx", "license": "apache-2.0", "size": 8912} |
### File: hfdocs/source/models/tf-efficientnet-condconv.mdx
# (Tensorflow) EfficientNet CondConv
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/tf-efficientnet-condconv.mdx", "license": "apache-2.0", "size": 8835} |
### File: hfdocs/source/models/tf-efficientnet-lite.mdx
# (Tensorflow) EfficientNet Lite
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/tf-efficientnet-lite.mdx", "license": "apache-2.0", "size": 9342} |
### File: hfdocs/source/models/tf-efficientnet.mdx
# (Tensorflow) EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these fact... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/tf-efficientnet.mdx", "license": "apache-2.0", "size": 19927} |
### File: hfdocs/source/models/tf-inception-v3.mdx
# (Tensorflow) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, a... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/tf-inception-v3.mdx", "license": "apache-2.0", "size": 5424} |
### File: hfdocs/source/models/tf-mixnet.mdx
# (Tensorflow) MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
The we... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/tf-mixnet.mdx", "license": "apache-2.0", "size": 6222} |
### File: hfdocs/source/models/tf-mobilenet-v3.mdx
# (Tensorflow) MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://pa... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/tf-mobilenet-v3.mdx", "license": "apache-2.0", "size": 11704} |
### File: hfdocs/source/models/tresnet.mdx
# TResNet
A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https:... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/tresnet.mdx", "license": "apache-2.0", "size": 10540} |
### File: hfdocs/source/models/wide-resnet.mdx
# Wide ResNet
**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/wide-resnet.mdx", "license": "apache-2.0", "size": 5425} |
### File: hfdocs/source/models/xception.mdx
# Xception
**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution).
The weights from this model were ported from [Tensorflow/Models](https:/... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/models/xception.mdx", "license": "apache-2.0", "size": 7073} |
### File: hfdocs/source/quickstart.mdx
# Quickstart
This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate `timm` into their model training workflow.
First, you'll need to install `timm`. For more information on installation, see [Installation](installati... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/quickstart.mdx", "license": "apache-2.0", "size": 7436} |
### File: hfdocs/source/reference/data.mdx
# Data
[[autodoc]] timm.data.create_dataset
[[autodoc]] timm.data.create_loader
[[autodoc]] timm.data.create_transform
[[autodoc]] timm.data.resolve_data_config | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/reference/data.mdx", "license": "apache-2.0", "size": 164} |
### File: hfdocs/source/reference/models.mdx
# Models
[[autodoc]] timm.create_model
[[autodoc]] timm.list_models
| {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/reference/models.mdx", "license": "apache-2.0", "size": 70} |
### File: hfdocs/source/reference/optimizers.mdx
# Optimization
This page contains the API reference documentation for learning rate optimizers included in `timm`.
## Optimizers
### Factory functions
[[autodoc]] timm.optim.optim_factory.create_optimizer
[[autodoc]] timm.optim.optim_factory.create_optimizer_v2
### ... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/reference/optimizers.mdx", "license": "apache-2.0", "size": 831} |
### File: hfdocs/source/reference/schedulers.mdx
# Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in `timm`.
## Schedulers
### Factory functions
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler
[[autodoc]] timm.scheduler.scheduler_facto... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/reference/schedulers.mdx", "license": "apache-2.0", "size": 644} |
### File: hfdocs/source/results.mdx
# Results
CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository [results folder](https://github.com/rwightman/pytorch-image-models/tree/master/results).
## Self-trained Weigh... | {"idx": "gitee_code", "domain": "code", "domain2": "gitee-permissive", "header_footer": "", "lang": "en", "source": "gitee_code-0.jsonl", "prog_lang": "MDX", "repo_name": "minhanghuang/pytorch-image-models", "path": "hfdocs/source/results.mdx", "license": "apache-2.0", "size": 4853} |
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