diff --git a/.circleci/test.yml b/.circleci/test.yml
index 560e344f..67ce1b27 100644
--- a/.circleci/test.yml
+++ b/.circleci/test.yml
@@ -31,7 +31,7 @@ jobs:
name: Check docstring coverage
command: |
pip install interrogate
- interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 60 mmcls
+ interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 60 mmcls
build_cpu:
parameters:
# The python version must match available image tags in
@@ -42,8 +42,6 @@ jobs:
type: string
torchvision:
type: string
- mmcv:
- type: string
docker:
- image: cimg/python:<< parameters.python >>
resource_class: large
@@ -57,31 +55,32 @@ jobs:
- run:
name: Configure Python & pip
command: |
- python -m pip install --upgrade pip
- python -m pip install wheel
+ pip install --upgrade pip
+ pip install wheel
- run:
name: Install PyTorch
command: |
python -V
- python -m pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+ pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
- run:
name: Install mmcls dependencies
command: |
- python -m pip install git+ssh://git@github.com/open-mmlab/mmengine.git@main
- python -m pip install << parameters.mmcv >>
- python -m pip install timm
- python -m pip install -r requirements.txt
+ pip install git+https://github.com/open-mmlab/mmengine.git@main
+ pip install -U openmim
+ mim install 'mmcv >= 2.0.0rc1'
+ pip install timm
+ pip install -r requirements.txt
python -c 'import mmcv; print(mmcv.__version__)'
- run:
name: Build and install
command: |
- python -m pip install -e .
+ pip install -e .
- run:
name: Run unittests
command: |
- python -m coverage run --branch --source mmcls -m pytest tests/
- python -m coverage xml
- python -m coverage report -m
+ coverage run --branch --source mmcls -m pytest tests/
+ coverage xml
+ coverage report -m
build_cuda:
machine:
@@ -96,15 +95,13 @@ jobs:
cudnn:
type: integer
default: 7
- mmcv:
- type: string
steps:
- checkout
- run:
# Cloning repos in VM since Docker doesn't have access to the private key
name: Clone Repos
command: |
- git clone -b main --depth 1 ssh://git@github.com/open-mmlab/mmengine.git /home/circleci/mmengine
+ git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
- run:
name: Build Docker image
command: |
@@ -114,7 +111,8 @@ jobs:
name: Install mmcls dependencies
command: |
docker exec mmcls pip install -e /mmengine
- docker exec mmcls pip install << parameters.mmcv >>
+ docker exec mmcls pip install -U openmim
+ docker exec mmcls mim install 'mmcv >= 2.0.0rc1'
docker exec mmcls pip install -r requirements.txt
docker exec mmcls python -c 'import mmcv; print(mmcv.__version__)'
- run:
@@ -124,7 +122,7 @@ jobs:
- run:
name: Run unittests
command: |
- docker exec mmcls python -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
+ docker exec mmcls python -m pytest tests/ -k 'not timm'
# Invoke jobs via workflows
# See: https://circleci.com/docs/2.0/configuration-reference/#workflows
@@ -138,6 +136,7 @@ workflows:
branches:
ignore:
- dev-1.x
+ - 1.x
pr_stage_test:
when:
not:
@@ -154,15 +153,13 @@ workflows:
torch: 1.6.0
torchvision: 0.7.0
python: 3.6.9 # The lowest python 3.6.x version available on CircleCI images
- mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cpu/torch1.6.0/mmcv_full-2.0.0rc0-cp36-cp36m-manylinux1_x86_64.whl
requires:
- lint
- build_cpu:
name: maximum_version_cpu
- torch: 1.9.0 # TODO: Update the version after mmcv provides more pre-compiled packages.
- torchvision: 0.10.0
+ torch: 1.12.1
+ torchvision: 0.13.1
python: 3.9.0
- mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cpu/torch1.9.0/mmcv_full-2.0.0rc0-cp39-cp39-manylinux1_x86_64.whl
requires:
- minimum_version_cpu
- hold:
@@ -175,7 +172,6 @@ workflows:
# Use double quotation mark to explicitly specify its type
# as string instead of number
cuda: "10.2"
- mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cu102/torch1.8.0/mmcv_full-2.0.0rc0-cp37-cp37m-manylinux1_x86_64.whl
requires:
- hold
merge_stage_test:
@@ -188,7 +184,6 @@ workflows:
torch: 1.6.0
# Use double quotation mark to explicitly specify its type
# as string instead of number
- mmcv: https://download.openmmlab.com/mmcv/dev-2.x/cu101/torch1.6.0/mmcv_full-2.0.0rc0-cp37-cp37m-manylinux1_x86_64.whl
cuda: "10.1"
filters:
branches:
diff --git a/.dev_scripts/benchmark_regression/1-benchmark_valid.py b/.dev_scripts/benchmark_regression/1-benchmark_valid.py
index a9e33c64..eb1541a1 100644
--- a/.dev_scripts/benchmark_regression/1-benchmark_valid.py
+++ b/.dev_scripts/benchmark_regression/1-benchmark_valid.py
@@ -2,15 +2,17 @@ import logging
import re
import tempfile
from argparse import ArgumentParser
+from collections import OrderedDict
from pathlib import Path
from time import time
-from typing import OrderedDict
import mmcv
import numpy as np
import torch
-from mmengine import Config, MMLogger, Runner
-from mmengine.dataset import Compose
+from mmengine import Config, DictAction, MMLogger
+from mmengine.dataset import Compose, default_collate
+from mmengine.fileio import FileClient
+from mmengine.runner import Runner
from modelindex.load_model_index import load
from rich.console import Console
from rich.table import Table
@@ -52,6 +54,16 @@ def parse_args():
'--flops-str',
action='store_true',
help='Output FLOPs and params counts in a string form.')
+ parser.add_argument(
+ '--cfg-options',
+ nargs='+',
+ action=DictAction,
+ help='override some settings in the used config, the key-value pair '
+ 'in xxx=yyy format will be merged into config file. If the value to '
+ 'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+ 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+ 'Note that the quotation marks are necessary and that no white space '
+ 'is allowed.')
args = parser.parse_args()
return args
@@ -62,6 +74,8 @@ def inference(config_file, checkpoint, work_dir, args, exp_name):
cfg.load_from = checkpoint
cfg.log_level = 'WARN'
cfg.experiment_name = exp_name
+ if args.cfg_options is not None:
+ cfg.merge_from_dict(args.cfg_options)
# build the data pipeline
test_dataset = cfg.test_dataloader.dataset
@@ -72,7 +86,8 @@ def inference(config_file, checkpoint, work_dir, args, exp_name):
test_dataset.pipeline.insert(1, dict(type='Resize', scale=32))
data = Compose(test_dataset.pipeline)({'img_path': args.img})
- resolution = tuple(data['inputs'].shape[1:])
+ data = default_collate([data])
+ resolution = tuple(data['inputs'].shape[-2:])
runner: Runner = Runner.from_cfg(cfg)
model = runner.model
@@ -83,26 +98,30 @@ def inference(config_file, checkpoint, work_dir, args, exp_name):
if args.inference_time:
time_record = []
for _ in range(10):
+ model.val_step(data) # warmup before profiling
+ torch.cuda.synchronize()
start = time()
- model.val_step([data])
+ model.val_step(data)
+ torch.cuda.synchronize()
time_record.append((time() - start) * 1000)
result['time_mean'] = np.mean(time_record[1:-1])
result['time_std'] = np.std(time_record[1:-1])
else:
- model.val_step([data])
+ model.val_step(data)
result['model'] = config_file.stem
if args.flops:
- from mmcv.cnn.utils import get_model_complexity_info
+ from fvcore.nn import FlopCountAnalysis, parameter_count
+ from fvcore.nn.print_model_statistics import _format_size
+ _format_size = _format_size if args.flops_str else lambda x: x
with torch.no_grad():
if hasattr(model, 'extract_feat'):
model.forward = model.extract_feat
- flops, params = get_model_complexity_info(
- model,
- input_shape=(3, ) + resolution,
- print_per_layer_stat=False,
- as_strings=args.flops_str)
+ model.to('cpu')
+ inputs = (torch.randn((1, 3, *resolution)), )
+ flops = _format_size(FlopCountAnalysis(model, inputs).total())
+ params = _format_size(parameter_count(model)[''])
result['flops'] = flops if args.flops_str else int(flops)
result['params'] = params if args.flops_str else int(params)
else:
@@ -184,7 +203,6 @@ def main(args):
if args.checkpoint_root is not None:
root = args.checkpoint_root
if 's3://' in args.checkpoint_root:
- from mmcv.fileio import FileClient
from petrel_client.common.exception import AccessDeniedError
file_client = FileClient.infer_client(uri=root)
checkpoint = file_client.join_path(
diff --git a/.dev_scripts/benchmark_regression/2-benchmark_test.py b/.dev_scripts/benchmark_regression/2-benchmark_test.py
index 380e2519..cd49bd09 100644
--- a/.dev_scripts/benchmark_regression/2-benchmark_test.py
+++ b/.dev_scripts/benchmark_regression/2-benchmark_test.py
@@ -62,6 +62,12 @@ def parse_args():
action='store_true',
help='Summarize benchmark test results.')
parser.add_argument('--save', action='store_true', help='Save the summary')
+ parser.add_argument(
+ '--cfg-options',
+ nargs='+',
+ type=str,
+ default=[],
+ help='Config options for all config files.')
args = parser.parse_args()
return args
@@ -76,7 +82,7 @@ def create_test_job_batch(commands, model_info, args, port, script_name):
http_prefix = 'https://download.openmmlab.com/mmclassification/'
if 's3://' in args.checkpoint_root:
- from mmcv.fileio import FileClient
+ from mmengine.fileio import FileClient
from petrel_client.common.exception import AccessDeniedError
file_client = FileClient.infer_client(uri=args.checkpoint_root)
checkpoint = file_client.join_path(
@@ -125,6 +131,7 @@ def create_test_job_batch(commands, model_info, args, port, script_name):
f'--work-dir={work_dir} '
f'--out={result_file} '
f'--cfg-option dist_params.port={port} '
+ f'{" ".join(args.cfg_options)} '
f'--launcher={launcher}\n')
with open(work_dir / 'job.sh', 'w') as f:
diff --git a/.dev_scripts/benchmark_regression/3-benchmark_train.py b/.dev_scripts/benchmark_regression/3-benchmark_train.py
index 6d78be50..9e240cc5 100644
--- a/.dev_scripts/benchmark_regression/3-benchmark_train.py
+++ b/.dev_scripts/benchmark_regression/3-benchmark_train.py
@@ -3,22 +3,48 @@ import json
import os
import os.path as osp
import re
+from collections import OrderedDict
from datetime import datetime
from pathlib import Path
from zipfile import ZipFile
+import yaml
from modelindex.load_model_index import load
from rich.console import Console
from rich.syntax import Syntax
from rich.table import Table
console = Console()
+MMCLS_ROOT = Path(__file__).absolute().parents[2]
+CYCLE_LEVELS = ['month', 'quarter', 'half-year', 'no-training']
METRICS_MAP = {
'Top 1 Accuracy': 'accuracy/top1',
'Top 5 Accuracy': 'accuracy/top5'
}
+class RangeAction(argparse.Action):
+
+ def __call__(self, parser, namespace, values: str, option_string):
+ matches = re.match(r'([><=]*)([-\w]+)', values)
+ if matches is None:
+ raise ValueError(f'Unavailable range option {values}')
+ symbol, range_str = matches.groups()
+ assert range_str in CYCLE_LEVELS, \
+ f'{range_str} are not in {CYCLE_LEVELS}.'
+ level = CYCLE_LEVELS.index(range_str)
+ symbol = symbol or '<='
+ ranges = set()
+ if '=' in symbol:
+ ranges.add(level)
+ if '>' in symbol:
+ ranges.update(range(level + 1, len(CYCLE_LEVELS)))
+ if '<' in symbol:
+ ranges.update(range(level))
+ assert len(ranges) > 0, 'No range are selected.'
+ setattr(namespace, self.dest, ranges)
+
+
def parse_args():
parser = argparse.ArgumentParser(
description='Train models (in bench_train.yml) and compare accuracy.')
@@ -32,6 +58,14 @@ def parse_args():
parser.add_argument('--port', type=int, default=29666, help='dist port')
parser.add_argument(
'--models', nargs='+', type=str, help='Specify model names to run.')
+ parser.add_argument(
+ '--range',
+ type=str,
+ default={0},
+ action=RangeAction,
+ metavar='{month,quarter,half-year,no-training}',
+ help='The training benchmark range, "no-training" means all models '
+ "including those we haven't trained.")
parser.add_argument(
'--work-dir',
default='work_dirs/benchmark_train',
@@ -63,18 +97,33 @@ def parse_args():
'--save',
action='store_true',
help='Save the summary and archive log files.')
+ parser.add_argument(
+ '--cfg-options',
+ nargs='+',
+ type=str,
+ default=[],
+ help='Config options for all config files.')
args = parser.parse_args()
return args
+def get_gpu_number(model_info):
+ config = osp.basename(model_info.config)
+ matches = re.match(r'.*[-_](\d+)xb(\d+).*', config)
+ if matches is None:
+ raise ValueError(
+ 'Cannot get gpu numbers from the config name {config}')
+ gpus = int(matches.groups()[0])
+ return gpus
+
+
def create_train_job_batch(commands, model_info, args, port, script_name):
fname = model_info.name
- assert 'Gpus' in model_info.data, \
- f"Haven't specify gpu numbers for {fname}"
- gpus = model_info.data['Gpus']
+ gpus = get_gpu_number(model_info)
+ gpus_per_node = min(gpus, 8)
config = Path(model_info.config)
assert config.exists(), f'"{fname}": {config} not found.'
@@ -101,15 +150,17 @@ def create_train_job_batch(commands, model_info, args, port, script_name):
f'#SBATCH --output {work_dir}/job.%j.out\n'
f'#SBATCH --partition={args.partition}\n'
f'#SBATCH --job-name {job_name}\n'
- f'#SBATCH --gres=gpu:8\n'
+ f'#SBATCH --gres=gpu:{gpus_per_node}\n'
f'{mail_cfg}{quota_cfg}'
- f'#SBATCH --ntasks-per-node=8\n'
+ f'#SBATCH --ntasks-per-node={gpus_per_node}\n'
f'#SBATCH --ntasks={gpus}\n'
f'#SBATCH --cpus-per-task=5\n\n'
f'{runner} -u {script_name} {config} '
f'--work-dir={work_dir} --cfg-option '
- f'dist_params.port={port} '
- f'checkpoint_config.max_keep_ckpts=10 '
+ f'env_cfg.dist_cfg.port={port} '
+ f'{" ".join(args.cfg_options)} '
+ f'default_hooks.checkpoint.max_keep_ckpts=2 '
+ f'default_hooks.checkpoint.save_best="auto" '
f'--launcher={launcher}\n')
with open(work_dir / 'job.sh', 'w') as f:
@@ -124,33 +175,16 @@ def create_train_job_batch(commands, model_info, args, port, script_name):
return work_dir / 'job.sh'
-def train(args):
- models_cfg = load(str(Path(__file__).parent / 'bench_train.yml'))
- models_cfg.build_models_with_collections()
- models = {model.name: model for model in models_cfg.models}
-
+def train(models, args):
script_name = osp.join('tools', 'train.py')
port = args.port
commands = []
- if args.models:
- patterns = [re.compile(pattern) for pattern in args.models]
- filter_models = {}
- for k, v in models.items():
- if any([re.match(pattern, k) for pattern in patterns]):
- filter_models[k] = v
- if len(filter_models) == 0:
- print('No model found, please specify models in:')
- print('\n'.join(models.keys()))
- return
- models = filter_models
for model_info in models.values():
- months = model_info.data.get('Months', range(1, 13))
- if datetime.now().month in months:
- script_path = create_train_job_batch(commands, model_info, args,
- port, script_name)
- port += 1
+ script_path = create_train_job_batch(commands, model_info, args, port,
+ script_name)
+ port += 1
command_str = '\n'.join(commands)
@@ -245,12 +279,14 @@ def show_summary(summary_data):
metric = summary[metric_key]
expect = metric['expect']
last = metric['last']
+ last_epoch = metric['last_epoch']
last_color = set_color(last, expect)
best = metric['best']
best_color = set_color(best, expect)
best_epoch = metric['best_epoch']
row.append(f'{expect:.2f}')
- row.append(f'[{last_color}]{last:.2f}[/{last_color}]')
+ row.append(
+ f'[{last_color}]{last:.2f}[/{last_color}] ({last_epoch})')
row.append(
f'[{best_color}]{best:.2f}[/{best_color}] ({best_epoch})')
table.add_row(*row)
@@ -258,25 +294,11 @@ def show_summary(summary_data):
console.print(table)
-def summary(args):
- models_cfg = load(str(Path(__file__).parent / 'bench_train.yml'))
- models = {model.name: model for model in models_cfg.models}
+def summary(models, args):
work_dir = Path(args.work_dir)
dir_map = {p.name: p for p in work_dir.iterdir() if p.is_dir()}
- if args.models:
- patterns = [re.compile(pattern) for pattern in args.models]
- filter_models = {}
- for k, v in models.items():
- if any([re.match(pattern, k) for pattern in patterns]):
- filter_models[k] = v
- if len(filter_models) == 0:
- print('No model found, please specify models in:')
- print('\n'.join(models.keys()))
- return
- models = filter_models
-
summary_data = {}
for model_name, model_info in models.items():
@@ -287,17 +309,19 @@ def summary(args):
# Skip if not found any vis_data folder.
sub_dir = dir_map[model_name]
- vis_folders = [d for d in sub_dir.iterdir() if d.is_dir()]
- if len(vis_folders) == 0:
- continue
- log_file = sorted(vis_folders)[-1] / 'vis_data' / 'scalars.json'
- if not log_file.exists():
+ log_files = [f for f in sub_dir.glob('*/vis_data/scalars.json')]
+ if len(log_files) == 0:
continue
+ log_file = sorted(log_files)[-1]
# parse train log
with open(log_file) as f:
json_logs = [json.loads(s) for s in f.readlines()]
- val_logs = [log for log in json_logs if 'loss' not in log]
+ val_logs = [
+ log for log in json_logs
+ # TODO: need a better method to extract validate log
+ if 'loss' not in log and 'accuracy/top1' in log
+ ]
if len(val_logs) == 0:
continue
@@ -320,9 +344,10 @@ def summary(args):
summary[key_yml] = dict(
expect=expect_result,
last=last,
+ last_epoch=len(val_logs),
best=best,
- best_epoch=best_epoch)
- summary_data[model_name] = summary
+ best_epoch=best_epoch + 1)
+ summary_data[model_name].update(summary)
show_summary(summary_data)
if args.save:
@@ -332,10 +357,39 @@ def summary(args):
def main():
args = parse_args()
+ model_index_file = MMCLS_ROOT / 'model-index.yml'
+ model_index = load(str(model_index_file))
+ model_index.build_models_with_collections()
+ all_models = {model.name: model for model in model_index.models}
+
+ with open(Path(__file__).parent / 'bench_train.yml', 'r') as f:
+ train_items = yaml.safe_load(f)
+ models = OrderedDict()
+ for item in train_items:
+ name = item['Name']
+ model_info = all_models[name]
+ model_info.cycle = item.get('Cycle', None)
+ cycle = getattr(model_info, 'cycle', 'month')
+ cycle_level = CYCLE_LEVELS.index(cycle)
+ if cycle_level in args.range:
+ models[name] = model_info
+
+ if args.models:
+ patterns = [re.compile(pattern) for pattern in args.models]
+ filter_models = {}
+ for k, v in models.items():
+ if any([re.match(pattern, k) for pattern in patterns]):
+ filter_models[k] = v
+ if len(filter_models) == 0:
+ print('No model found, please specify models in:')
+ print('\n'.join(models.keys()))
+ return
+ models = filter_models
+
if args.summary:
- summary(args)
+ summary(models, args)
else:
- train(args)
+ train(models, args)
if __name__ == '__main__':
diff --git a/.dev_scripts/benchmark_regression/bench_train.yml b/.dev_scripts/benchmark_regression/bench_train.yml
index c7326849..9f6e11eb 100644
--- a/.dev_scripts/benchmark_regression/bench_train.yml
+++ b/.dev_scripts/benchmark_regression/bench_train.yml
@@ -1,88 +1,86 @@
-Models:
- - Name: resnet50
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 76.55
- Top 5 Accuracy: 93.06
- Config: configs/resnet/resnet50_8xb32_in1k.py
- Gpus: 8
+- Name: mobilenet-v2_8xb32_in1k
+ Cycle: month
- - Name: seresnet50
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 77.74
- Top 5 Accuracy: 93.84
- Config: configs/seresnet/seresnet50_8xb32_in1k.py
- Gpus: 8
+- Name: resnet50_8xb32_in1k
+ Cycle: month
- - Name: vit-base
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 82.37
- Top 5 Accuracy: 96.15
- Config: configs/vision_transformer/vit-base-p16_pt-32xb128-mae_in1k-224.py
- Gpus: 32
+- Name: seresnet50_8xb32_in1k
+ Cycle: month
- - Name: mobilenetv2
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 71.86
- Top 5 Accuracy: 90.42
- Config: configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py
- Gpus: 8
+- Name: swin-small_16xb64_in1k
+ Cycle: month
- - Name: swin_tiny
- Results:
- - Dataset: ImageNet
- Metrics:
- Top 1 Accuracy: 81.18
- Top 5 Accuracy: 95.61
- Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth
- Config: configs/swin_transformer/swin-tiny_16xb64_in1k.py
- Gpus: 16
+- Name: vit-base-p16_pt-32xb128-mae_in1k
+ Cycle: month
- - Name: vgg16
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 71.62
- Top 5 Accuracy: 90.49
- Config: configs/vgg/vgg16_8xb32_in1k.py
- Gpus: 8
- Months:
- - 1
- - 4
- - 7
- - 10
+- Name: resnet50_8xb256-rsb-a1-600e_in1k
+ Cycle: quarter
- - Name: shufflenet_v2
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 69.55
- Top 5 Accuracy: 88.92
- Config: configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py
- Gpus: 16
- Months:
- - 2
- - 5
- - 8
- - 11
+- Name: resnext50-32x4d_8xb32_in1k
+ Cycle: quarter
- - Name: resnet-rsb
- Results:
- - Dataset: ImageNet-1k
- Metrics:
- Top 1 Accuracy: 80.12
- Top 5 Accuracy: 94.78
- Config: configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py
- Gpus: 8
- Months:
- - 3
- - 6
- - 9
- - 12
+- Name: shufflenet-v2-1x_16xb64_in1k
+ Cycle: quarter
+
+- Name: vgg16_8xb32_in1k
+ Cycle: quarter
+
+- Name: shufflenet-v1-1x_16xb64_in1k
+ Cycle: half-year
+
+- Name: t2t-vit-t-14_8xb64_in1k
+ Cycle: half-year
+
+- Name: regnetx-1.6gf_8xb128_in1k
+ Cycle: half-year
+
+- Name: van-small_8xb128_in1k
+ Cycle: no-training
+
+- Name: res2net50-w14-s8_3rdparty_8xb32_in1k
+ Cycle: no-training
+
+- Name: repvgg-A2_3rdparty_4xb64-coslr-120e_in1k
+ Cycle: no-training
+
+- Name: tnt-small-p16_3rdparty_in1k
+ Cycle: no-training
+
+- Name: mlp-mixer-base-p16_3rdparty_64xb64_in1k
+ Cycle: no-training
+
+- Name: conformer-small-p16_3rdparty_8xb128_in1k
+ Cycle: no-training
+
+- Name: twins-pcpvt-base_3rdparty_8xb128_in1k
+ Cycle: no-training
+
+- Name: efficientnet-b0_3rdparty_8xb32_in1k
+ Cycle: no-training
+
+- Name: convnext-small_3rdparty_32xb128_in1k
+ Cycle: no-training
+
+- Name: hrnet-w18_3rdparty_8xb32_in1k
+ Cycle: no-training
+
+- Name: repmlp-base_3rdparty_8xb64_in1k
+ Cycle: no-training
+
+- Name: wide-resnet50_3rdparty_8xb32_in1k
+ Cycle: no-training
+
+- Name: cspresnet50_3rdparty_8xb32_in1k
+ Cycle: no-training
+
+- Name: convmixer-768-32_10xb64_in1k
+ Cycle: no-training
+
+- Name: densenet169_4xb256_in1k
+ Cycle: no-training
+
+- Name: poolformer-s24_3rdparty_32xb128_in1k
+ Cycle: no-training
+
+- Name: inception-v3_3rdparty_8xb32_in1k
+ Cycle: no-training
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
new file mode 100644
index 00000000..0c1cd14d
--- /dev/null
+++ b/.github/workflows/lint.yml
@@ -0,0 +1,27 @@
+name: lint
+
+on: [push, pull_request]
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ lint:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python 3.7
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.7
+ - name: Install pre-commit hook
+ run: |
+ pip install pre-commit
+ pre-commit install
+ - name: Linting
+ run: pre-commit run --all-files
+ - name: Check docstring coverage
+ run: |
+ pip install interrogate
+ interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 60 mmcls
diff --git a/.github/workflows/pr_stage_test.yml b/.github/workflows/pr_stage_test.yml
new file mode 100644
index 00000000..67b154a6
--- /dev/null
+++ b/.github/workflows/pr_stage_test.yml
@@ -0,0 +1,87 @@
+name: pr_stage_test
+
+on:
+ pull_request:
+ paths-ignore:
+ - 'README.md'
+ - 'README_zh-CN.md'
+ - 'docs/**'
+ - 'demo/**'
+ - 'tools/**'
+ - 'configs/**'
+ - '.dev_scripts/**'
+ - '.circleci/**'
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ build:
+ runs-on: ubuntu-18.04
+ strategy:
+ matrix:
+ python-version: [3.7]
+ include:
+ - torch: 1.8.1
+ torchvision: 0.9.1
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Upgrade pip
+ run: pip install pip --upgrade
+ - name: Install PyTorch
+ run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+ - name: Install mmcls dependencies
+ run: |
+ pip install git+https://github.com/open-mmlab/mmengine.git@main
+ pip install -U openmim
+ mim install 'mmcv >= 2.0.0rc1'
+ pip install -r requirements.txt
+ - name: Build and install
+ run: pip install -e .
+ - name: Run unittests and generate coverage report
+ run: |
+ coverage run --branch --source mmcls -m pytest tests/ -k 'not timm'
+ coverage xml
+ coverage report -m
+ # Upload coverage report for python3.7 && pytorch1.8.1 cpu
+ - name: Upload coverage to Codecov
+ uses: codecov/codecov-action@v1.0.14
+ with:
+ file: ./coverage.xml
+ flags: unittests
+ env_vars: OS,PYTHON
+ name: codecov-umbrella
+ fail_ci_if_error: false
+
+ build_windows:
+ runs-on: windows-2022
+ strategy:
+ matrix:
+ python: [3.7]
+ platform: [cu111]
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Upgrade pip
+ run: pip install pip --upgrade
+ - name: Install PyTorch
+ run: pip install torch==1.8.2+${{matrix.platform}} torchvision==0.9.2+${{matrix.platform}} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+ - name: Install mmcls dependencies
+ run: |
+ pip install git+https://github.com/open-mmlab/mmengine.git@main
+ pip install -U openmim
+ mim install 'mmcv >= 2.0.0rc1'
+ pip install -r requirements.txt
+ - name: Build and install
+ run: pip install -e .
+ - name: Run unittests
+ run: |
+ pytest tests/ -k 'not timm' --ignore tests/test_models/test_backbones
diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml
new file mode 100644
index 00000000..08936cb2
--- /dev/null
+++ b/.github/workflows/publish-to-pypi.yml
@@ -0,0 +1,22 @@
+name: deploy
+
+on: push
+
+jobs:
+ build-n-publish:
+ runs-on: ubuntu-latest
+ if: startsWith(github.event.ref, 'refs/tags')
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python 3.7
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.7
+ - name: Build MMClassification
+ run: |
+ pip install wheel
+ python setup.py sdist bdist_wheel
+ - name: Publish distribution to PyPI
+ run: |
+ pip install twine
+ twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }}
diff --git a/.github/workflows/test_mim.yml b/.github/workflows/test_mim.yml
new file mode 100644
index 00000000..f437e4e5
--- /dev/null
+++ b/.github/workflows/test_mim.yml
@@ -0,0 +1,44 @@
+name: test-mim
+
+on:
+ push:
+ paths:
+ - 'model-index.yml'
+ - 'configs/**'
+
+ pull_request:
+ paths:
+ - 'model-index.yml'
+ - 'configs/**'
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ build_cpu:
+ runs-on: ubuntu-18.04
+ strategy:
+ matrix:
+ python-version: [3.7]
+ torch: [1.8.0]
+ include:
+ - torch: 1.8.0
+ torch_version: torch1.8
+ torchvision: 0.9.0
+ steps:
+ - uses: actions/checkout@v2
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v2
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Upgrade pip
+ run: pip install pip --upgrade
+ - name: Install PyTorch
+ run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+ - name: Install openmim
+ run: pip install openmim
+ - name: Build and install
+ run: mim install -e .
+ - name: test commands of mim
+ run: mim search mmcls
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 0d19d5f6..c55af0f2 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -44,10 +44,10 @@ repos:
- id: docformatter
args: ["--in-place", "--wrap-descriptions", "79"]
- repo: https://github.com/open-mmlab/pre-commit-hooks
- rev: v0.2.0
+ rev: v0.4.0
hooks:
- id: check-copyright
- args: ["mmcls", "tests", "demo", "tools"]
+ args: ["mmcls", "tests", "demo", "tools", "--excludes", "mmcls/.mim/", "--ignore-file-not-found-error"]
# - repo: local
# hooks:
# - id: clang-format
diff --git a/README.md b/README.md
index 4e2280de..30ffebac 100644
--- a/README.md
+++ b/README.md
@@ -58,6 +58,11 @@ The `1.x` branch works with **PyTorch 1.6+**.
## What's new
+v1.0.0rc1 was released in 30/9/2022.
+
+- Support MViT, EdgeNeXt, Swin-Transformer V2, EfficientFormer and MobileOne.
+- Support BEiT type transformer layer.
+
v1.0.0rc0 was released in 31/8/2022.
This release introduced a brand new and flexible training & test engine, but it's still in progress. Welcome
@@ -115,6 +120,7 @@ Results and models are available in the [model zoo](https://mmclassification.rea
- [x] [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilenet_v2)
- [x] [MobileNetV3](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilenet_v3)
- [x] [Swin-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/swin_transformer)
+- [x] [Swin-Transformer V2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/swin_transformer_v2)
- [x] [RepVGG](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/repvgg)
- [x] [Vision-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/vision_transformer)
- [x] [Transformer-in-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/tnt)
@@ -125,6 +131,7 @@ Results and models are available in the [model zoo](https://mmclassification.rea
- [x] [T2T-ViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/t2t_vit)
- [x] [Twins](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/twins)
- [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/efficientnet)
+- [x] [EdgeNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/edgenext)
- [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/convnext)
- [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/hrnet)
- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/van)
@@ -132,6 +139,9 @@ Results and models are available in the [model zoo](https://mmclassification.rea
- [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/cspnet)
- [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/poolformer)
- [x] [Inception V3](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/inception_v3)
+- [x] [MobileOne](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobileone)
+- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/efficientformer)
+- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mvit)
diff --git a/README_zh-CN.md b/README_zh-CN.md
index e626e269..0b3ae763 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -57,6 +57,11 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O
## 更新日志
+2022/9/30 发布了 v1.0.0rc1 版本
+
+- 支持了 MViT，EdgeNeXt，Swin-Transformer V2，EfficientFormer，MobileOne 等主干网络。
+- 支持了 BEiT 风格的 transformer 层。
+
2022/8/31 发布了 v1.0.0rc0 版本
这个版本引入一个全新的，可扩展性强的训练和测试引擎，但目前仍在开发中。欢迎根据[文档](https://mmclassification.readthedocs.io/zh_CN/1.x/)进行试用。
@@ -114,6 +119,7 @@ mim install -e .
- [x] [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilenet_v2)
- [x] [MobileNetV3](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobilenet_v3)
- [x] [Swin-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/swin_transformer)
+- [x] [Swin-Transformer V2](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/swin_transformer_v2)
- [x] [RepVGG](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/repvgg)
- [x] [Vision-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/vision_transformer)
- [x] [Transformer-in-Transformer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/tnt)
@@ -124,6 +130,7 @@ mim install -e .
- [x] [T2T-ViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/t2t_vit)
- [x] [Twins](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/twins)
- [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/efficientnet)
+- [x] [EdgeNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/edgenext)
- [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/convnext)
- [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/hrnet)
- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/van)
@@ -131,6 +138,9 @@ mim install -e .
- [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/cspnet)
- [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/poolformer)
- [x] [Inception V3](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/inception_v3)
+- [x] [MobileOne](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mobileone)
+- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/efficientformer)
+- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/mvit)
diff --git a/configs/_base_/datasets/imagenet_bs64_edgenext_256.py b/configs/_base_/datasets/imagenet_bs64_edgenext_256.py
new file mode 100644
index 00000000..18f78064
--- /dev/null
+++ b/configs/_base_/datasets/imagenet_bs64_edgenext_256.py
@@ -0,0 +1,83 @@
+# dataset settings
+dataset_type = 'ImageNet'
+data_preprocessor = dict(
+ # RGB format normalization parameters
+ mean=[123.675, 116.28, 103.53],
+ std=[58.395, 57.12, 57.375],
+ # convert image from BGR to RGB
+ to_rgb=True,
+)
+
+bgr_mean = data_preprocessor['mean'][::-1]
+bgr_std = data_preprocessor['std'][::-1]
+
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='RandomResizedCrop',
+ scale=256,
+ backend='pillow',
+ interpolation='bicubic'),
+ dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+ dict(
+ type='RandAugment',
+ policies='timm_increasing',
+ num_policies=2,
+ total_level=10,
+ magnitude_level=9,
+ magnitude_std=0.5,
+ hparams=dict(
+ pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
+ dict(
+ type='RandomErasing',
+ erase_prob=0.25,
+ mode='rand',
+ min_area_ratio=0.02,
+ max_area_ratio=1 / 3,
+ fill_color=bgr_mean,
+ fill_std=bgr_std),
+ dict(type='PackClsInputs'),
+]
+
+test_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='ResizeEdge',
+ scale=292,
+ edge='short',
+ backend='pillow',
+ interpolation='bicubic'),
+ dict(type='CenterCrop', crop_size=256),
+ dict(type='PackClsInputs')
+]
+
+train_dataloader = dict(
+ batch_size=64,
+ num_workers=5,
+ dataset=dict(
+ type=dataset_type,
+ data_root='data/imagenet',
+ ann_file='meta/train.txt',
+ data_prefix='train',
+ pipeline=train_pipeline),
+ sampler=dict(type='DefaultSampler', shuffle=True),
+ persistent_workers=True,
+)
+
+val_dataloader = dict(
+ batch_size=64,
+ num_workers=5,
+ dataset=dict(
+ type=dataset_type,
+ data_root='data/imagenet',
+ ann_file='meta/val.txt',
+ data_prefix='val',
+ pipeline=test_pipeline),
+ sampler=dict(type='DefaultSampler', shuffle=False),
+ persistent_workers=True,
+)
+val_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator
diff --git a/configs/_base_/datasets/imagenet_bs64_swin_256.py b/configs/_base_/datasets/imagenet_bs64_swin_256.py
new file mode 100644
index 00000000..db32589e
--- /dev/null
+++ b/configs/_base_/datasets/imagenet_bs64_swin_256.py
@@ -0,0 +1,83 @@
+# dataset settings
+dataset_type = 'ImageNet'
+data_preprocessor = dict(
+ # RGB format normalization parameters
+ mean=[123.675, 116.28, 103.53],
+ std=[58.395, 57.12, 57.375],
+ # convert image from BGR to RGB
+ to_rgb=True,
+)
+
+bgr_mean = data_preprocessor['mean'][::-1]
+bgr_std = data_preprocessor['std'][::-1]
+
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='RandomResizedCrop',
+ scale=256,
+ backend='pillow',
+ interpolation='bicubic'),
+ dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+ dict(
+ type='RandAugment',
+ policies='timm_increasing',
+ num_policies=2,
+ total_level=10,
+ magnitude_level=9,
+ magnitude_std=0.5,
+ hparams=dict(
+ pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
+ dict(
+ type='RandomErasing',
+ erase_prob=0.25,
+ mode='rand',
+ min_area_ratio=0.02,
+ max_area_ratio=1 / 3,
+ fill_color=bgr_mean,
+ fill_std=bgr_std),
+ dict(type='PackClsInputs'),
+]
+
+test_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='ResizeEdge',
+ scale=292, # ( 256 / 224 * 256 )
+ edge='short',
+ backend='pillow',
+ interpolation='bicubic'),
+ dict(type='CenterCrop', crop_size=256),
+ dict(type='PackClsInputs'),
+]
+
+train_dataloader = dict(
+ batch_size=64,
+ num_workers=5,
+ dataset=dict(
+ type=dataset_type,
+ data_root='data/imagenet',
+ ann_file='meta/train.txt',
+ data_prefix='train',
+ pipeline=train_pipeline),
+ sampler=dict(type='DefaultSampler', shuffle=True),
+ persistent_workers=True,
+)
+
+val_dataloader = dict(
+ batch_size=64,
+ num_workers=5,
+ dataset=dict(
+ type=dataset_type,
+ data_root='data/imagenet',
+ ann_file='meta/val.txt',
+ data_prefix='val',
+ pipeline=test_pipeline),
+ sampler=dict(type='DefaultSampler', shuffle=False),
+ persistent_workers=True,
+)
+val_evaluator = dict(type='Accuracy', topk=(1, 5))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator
diff --git a/configs/_base_/models/edgenext/edgenext-base.py b/configs/_base_/models/edgenext/edgenext-base.py
new file mode 100644
index 00000000..37839729
--- /dev/null
+++ b/configs/_base_/models/edgenext/edgenext-base.py
@@ -0,0 +1,23 @@
+# Model settings
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='EdgeNeXt',
+ arch='base',
+ out_indices=(3, ),
+ drop_path_rate=0.1,
+ gap_before_final_norm=True,
+ init_cfg=[
+ dict(
+ type='TruncNormal',
+ layer=['Conv2d', 'Linear'],
+ std=.02,
+ bias=0.),
+ dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+ ]),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=584,
+ loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+ ))
diff --git a/configs/_base_/models/edgenext/edgenext-small.py b/configs/_base_/models/edgenext/edgenext-small.py
new file mode 100644
index 00000000..e1f7e172
--- /dev/null
+++ b/configs/_base_/models/edgenext/edgenext-small.py
@@ -0,0 +1,23 @@
+# Model settings
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='EdgeNeXt',
+ arch='small',
+ out_indices=(3, ),
+ drop_path_rate=0.1,
+ gap_before_final_norm=True,
+ init_cfg=[
+ dict(
+ type='TruncNormal',
+ layer=['Conv2d', 'Linear'],
+ std=.02,
+ bias=0.),
+ dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+ ]),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=304,
+ loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+ ))
diff --git a/configs/_base_/models/edgenext/edgenext-xsmall.py b/configs/_base_/models/edgenext/edgenext-xsmall.py
new file mode 100644
index 00000000..69c7d0d6
--- /dev/null
+++ b/configs/_base_/models/edgenext/edgenext-xsmall.py
@@ -0,0 +1,23 @@
+# Model settings
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='EdgeNeXt',
+ arch='xsmall',
+ out_indices=(3, ),
+ drop_path_rate=0.1,
+ gap_before_final_norm=True,
+ init_cfg=[
+ dict(
+ type='TruncNormal',
+ layer=['Conv2d', 'Linear'],
+ std=.02,
+ bias=0.),
+ dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+ ]),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=192,
+ loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+ ))
diff --git a/configs/_base_/models/edgenext/edgenext-xxsmall.py b/configs/_base_/models/edgenext/edgenext-xxsmall.py
new file mode 100644
index 00000000..fb688195
--- /dev/null
+++ b/configs/_base_/models/edgenext/edgenext-xxsmall.py
@@ -0,0 +1,23 @@
+# Model settings
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='EdgeNeXt',
+ arch='xxsmall',
+ out_indices=(3, ),
+ drop_path_rate=0.1,
+ gap_before_final_norm=True,
+ init_cfg=[
+ dict(
+ type='TruncNormal',
+ layer=['Conv2d', 'Linear'],
+ std=.02,
+ bias=0.),
+ dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.),
+ ]),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=168,
+ loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+ ))
diff --git a/configs/_base_/models/efficientformer-l1.py b/configs/_base_/models/efficientformer-l1.py
new file mode 100644
index 00000000..37dc62cd
--- /dev/null
+++ b/configs/_base_/models/efficientformer-l1.py
@@ -0,0 +1,18 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='EfficientFormer',
+ arch='l1',
+ drop_path_rate=0,
+ init_cfg=[
+ dict(
+ type='TruncNormal',
+ layer=['Conv2d', 'Linear'],
+ std=.02,
+ bias=0.),
+ dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+ dict(type='Constant', layer=['LayerScale'], val=1e-5)
+ ]),
+ neck=dict(type='GlobalAveragePooling', dim=1),
+ head=dict(
+ type='EfficientFormerClsHead', in_channels=448, num_classes=1000))
diff --git a/configs/_base_/models/mobileone/mobileone_s0.py b/configs/_base_/models/mobileone/mobileone_s0.py
new file mode 100644
index 00000000..39624e55
--- /dev/null
+++ b/configs/_base_/models/mobileone/mobileone_s0.py
@@ -0,0 +1,19 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='MobileOne',
+ arch='s0',
+ out_indices=(3, ),
+ ),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=1024,
+ loss=dict(
+ type='LabelSmoothLoss',
+ label_smooth_val=0.1,
+ mode='original',
+ ),
+ topk=(1, 5),
+ ))
diff --git a/configs/_base_/models/mobileone/mobileone_s1.py b/configs/_base_/models/mobileone/mobileone_s1.py
new file mode 100644
index 00000000..cea7762e
--- /dev/null
+++ b/configs/_base_/models/mobileone/mobileone_s1.py
@@ -0,0 +1,19 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='MobileOne',
+ arch='s1',
+ out_indices=(3, ),
+ ),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=1280,
+ loss=dict(
+ type='LabelSmoothLoss',
+ label_smooth_val=0.1,
+ mode='original',
+ ),
+ topk=(1, 5),
+ ))
diff --git a/configs/_base_/models/mobileone/mobileone_s2.py b/configs/_base_/models/mobileone/mobileone_s2.py
new file mode 100644
index 00000000..dfae0e1f
--- /dev/null
+++ b/configs/_base_/models/mobileone/mobileone_s2.py
@@ -0,0 +1,19 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='MobileOne',
+ arch='s2',
+ out_indices=(3, ),
+ ),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=2048,
+ loss=dict(
+ type='LabelSmoothLoss',
+ label_smooth_val=0.1,
+ mode='original',
+ ),
+ topk=(1, 5),
+ ))
diff --git a/configs/_base_/models/mobileone/mobileone_s3.py b/configs/_base_/models/mobileone/mobileone_s3.py
new file mode 100644
index 00000000..81356753
--- /dev/null
+++ b/configs/_base_/models/mobileone/mobileone_s3.py
@@ -0,0 +1,19 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='MobileOne',
+ arch='s3',
+ out_indices=(3, ),
+ ),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=2048,
+ loss=dict(
+ type='LabelSmoothLoss',
+ label_smooth_val=0.1,
+ mode='original',
+ ),
+ topk=(1, 5),
+ ))
diff --git a/configs/_base_/models/mobileone/mobileone_s4.py b/configs/_base_/models/mobileone/mobileone_s4.py
new file mode 100644
index 00000000..282eec8b
--- /dev/null
+++ b/configs/_base_/models/mobileone/mobileone_s4.py
@@ -0,0 +1,19 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='MobileOne',
+ arch='s4',
+ out_indices=(3, ),
+ ),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=2048,
+ loss=dict(
+ type='LabelSmoothLoss',
+ label_smooth_val=0.1,
+ mode='original',
+ ),
+ topk=(1, 5),
+ ))
diff --git a/configs/_base_/models/mvit/mvitv2-base.py b/configs/_base_/models/mvit/mvitv2-base.py
new file mode 100644
index 00000000..137b15b2
--- /dev/null
+++ b/configs/_base_/models/mvit/mvitv2-base.py
@@ -0,0 +1,19 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(type='MViT', arch='base', drop_path_rate=0.3),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ in_channels=768,
+ num_classes=1000,
+ loss=dict(
+ type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+ ),
+ init_cfg=[
+ dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+ dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+ ],
+ train_cfg=dict(augments=[
+ dict(type='Mixup', alpha=0.8, num_classes=1000),
+ dict(type='CutMix', alpha=1.0, num_classes=1000)
+ ]))
diff --git a/configs/_base_/models/mvit/mvitv2-large.py b/configs/_base_/models/mvit/mvitv2-large.py
new file mode 100644
index 00000000..4a1abd2b
--- /dev/null
+++ b/configs/_base_/models/mvit/mvitv2-large.py
@@ -0,0 +1,23 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='MViT',
+ arch='large',
+ drop_path_rate=0.5,
+ dim_mul_in_attention=False),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ in_channels=1152,
+ num_classes=1000,
+ loss=dict(
+ type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+ ),
+ init_cfg=[
+ dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+ dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+ ],
+ train_cfg=dict(augments=[
+ dict(type='Mixup', alpha=0.8, num_classes=1000),
+ dict(type='CutMix', alpha=1.0, num_classes=1000)
+ ]))
diff --git a/configs/_base_/models/mvit/mvitv2-small.py b/configs/_base_/models/mvit/mvitv2-small.py
new file mode 100644
index 00000000..e3640c3d
--- /dev/null
+++ b/configs/_base_/models/mvit/mvitv2-small.py
@@ -0,0 +1,19 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(type='MViT', arch='small', drop_path_rate=0.1),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ in_channels=768,
+ num_classes=1000,
+ loss=dict(
+ type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+ ),
+ init_cfg=[
+ dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+ dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+ ],
+ train_cfg=dict(augments=[
+ dict(type='Mixup', alpha=0.8, num_classes=1000),
+ dict(type='CutMix', alpha=1.0, num_classes=1000)
+ ]))
diff --git a/configs/_base_/models/mvit/mvitv2-tiny.py b/configs/_base_/models/mvit/mvitv2-tiny.py
new file mode 100644
index 00000000..3b7fc690
--- /dev/null
+++ b/configs/_base_/models/mvit/mvitv2-tiny.py
@@ -0,0 +1,19 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(type='MViT', arch='tiny', drop_path_rate=0.1),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ in_channels=768,
+ num_classes=1000,
+ loss=dict(
+ type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+ ),
+ init_cfg=[
+ dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+ dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+ ],
+ train_cfg=dict(augments=[
+ dict(type='Mixup', alpha=0.8, num_classes=1000),
+ dict(type='CutMix', alpha=1.0, num_classes=1000)
+ ]))
diff --git a/configs/_base_/models/swin_transformer_v2/base_256.py b/configs/_base_/models/swin_transformer_v2/base_256.py
new file mode 100644
index 00000000..0e00004e
--- /dev/null
+++ b/configs/_base_/models/swin_transformer_v2/base_256.py
@@ -0,0 +1,26 @@
+# model settings
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='SwinTransformerV2',
+ arch='base',
+ img_size=256,
+ drop_path_rate=0.5),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=1024,
+ init_cfg=None, # suppress the default init_cfg of LinearClsHead.
+ loss=dict(
+ type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+ cal_acc=False),
+ init_cfg=[
+ dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+ dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+ ],
+ train_cfg=dict(augments=[
+ dict(type='Mixup', alpha=0.8, num_classes=1000),
+ dict(type='CutMix', alpha=1.0, num_classes=1000)
+ ]),
+)
diff --git a/configs/_base_/models/swin_transformer_v2/base_384.py b/configs/_base_/models/swin_transformer_v2/base_384.py
new file mode 100644
index 00000000..5fb9aead
--- /dev/null
+++ b/configs/_base_/models/swin_transformer_v2/base_384.py
@@ -0,0 +1,17 @@
+# model settings
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='SwinTransformerV2',
+ arch='base',
+ img_size=384,
+ drop_path_rate=0.2),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=1024,
+ init_cfg=None, # suppress the default init_cfg of LinearClsHead.
+ loss=dict(
+ type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+ cal_acc=False))
diff --git a/configs/_base_/models/swin_transformer_v2/large_256.py b/configs/_base_/models/swin_transformer_v2/large_256.py
new file mode 100644
index 00000000..fe557c32
--- /dev/null
+++ b/configs/_base_/models/swin_transformer_v2/large_256.py
@@ -0,0 +1,16 @@
+# model settings
+# Only for evaluation
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='SwinTransformerV2',
+ arch='large',
+ img_size=256,
+ drop_path_rate=0.2),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=1536,
+ loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+ topk=(1, 5)))
diff --git a/configs/_base_/models/swin_transformer_v2/large_384.py b/configs/_base_/models/swin_transformer_v2/large_384.py
new file mode 100644
index 00000000..a626c407
--- /dev/null
+++ b/configs/_base_/models/swin_transformer_v2/large_384.py
@@ -0,0 +1,16 @@
+# model settings
+# Only for evaluation
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='SwinTransformerV2',
+ arch='large',
+ img_size=384,
+ drop_path_rate=0.2),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=1536,
+ loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+ topk=(1, 5)))
diff --git a/configs/_base_/models/swin_transformer_v2/small_256.py b/configs/_base_/models/swin_transformer_v2/small_256.py
new file mode 100644
index 00000000..5c296dc9
--- /dev/null
+++ b/configs/_base_/models/swin_transformer_v2/small_256.py
@@ -0,0 +1,26 @@
+# model settings
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='SwinTransformerV2',
+ arch='small',
+ img_size=256,
+ drop_path_rate=0.3),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=768,
+ init_cfg=None, # suppress the default init_cfg of LinearClsHead.
+ loss=dict(
+ type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+ cal_acc=False),
+ init_cfg=[
+ dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+ dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+ ],
+ train_cfg=dict(augments=[
+ dict(type='Mixup', alpha=0.8, num_classes=1000),
+ dict(type='CutMix', alpha=1.0, num_classes=1000)
+ ]),
+)
diff --git a/configs/_base_/models/swin_transformer_v2/tiny_256.py b/configs/_base_/models/swin_transformer_v2/tiny_256.py
new file mode 100644
index 00000000..73059428
--- /dev/null
+++ b/configs/_base_/models/swin_transformer_v2/tiny_256.py
@@ -0,0 +1,26 @@
+# model settings
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='SwinTransformerV2',
+ arch='tiny',
+ img_size=256,
+ drop_path_rate=0.2),
+ neck=dict(type='GlobalAveragePooling'),
+ head=dict(
+ type='LinearClsHead',
+ num_classes=1000,
+ in_channels=768,
+ init_cfg=None, # suppress the default init_cfg of LinearClsHead.
+ loss=dict(
+ type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+ cal_acc=False),
+ init_cfg=[
+ dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+ dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+ ],
+ train_cfg=dict(augments=[
+ dict(type='Mixup', alpha=0.8, num_classes=1000),
+ dict(type='CutMix', alpha=1.0, num_classes=1000)
+ ]),
+)
diff --git a/configs/_base_/schedules/imagenet_bs1024_adamw_swin.py b/configs/_base_/schedules/imagenet_bs1024_adamw_swin.py
index 3a2797bc..5b52ea6e 100644
--- a/configs/_base_/schedules/imagenet_bs1024_adamw_swin.py
+++ b/configs/_base_/schedules/imagenet_bs1024_adamw_swin.py
@@ -23,18 +23,11 @@ param_scheduler = [
type='LinearLR',
start_factor=1e-3,
by_epoch=True,
- begin=0,
end=20,
# update by iter
convert_to_iter_based=True),
# main learning rate scheduler
- dict(
- type='CosineAnnealingLR',
- T_max=280,
- eta_min=1e-5,
- by_epoch=True,
- begin=20,
- end=300)
+ dict(type='CosineAnnealingLR', eta_min=1e-5, by_epoch=True, begin=20)
]
# train, val, test setting
diff --git a/configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py b/configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py
index 18b8554d..cf38d473 100644
--- a/configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py
+++ b/configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py
@@ -6,14 +6,8 @@ optim_wrapper = dict(
# learning policy
param_scheduler = [
- dict(
- type='ConstantLR',
- factor=0.1,
- by_epoch=True,
- begin=0,
- end=5,
- convert_to_iter_based=True),
- dict(type='PolyLR', eta_min=0, by_epoch=True, begin=5, end=300)
+ dict(type='ConstantLR', factor=0.1, by_epoch=False, begin=0, end=5000),
+ dict(type='PolyLR', eta_min=0, by_epoch=False, begin=5000)
]
# train, val, test setting
diff --git a/configs/_base_/schedules/imagenet_bs2048_AdamW.py b/configs/_base_/schedules/imagenet_bs2048_AdamW.py
index 13011a5a..bbfae8ef 100644
--- a/configs/_base_/schedules/imagenet_bs2048_AdamW.py
+++ b/configs/_base_/schedules/imagenet_bs2048_AdamW.py
@@ -11,25 +11,22 @@ optim_wrapper = dict(
)
# learning policy
+warmup_epochs = 15 # about 10000 iterations for ImageNet-1k
param_scheduler = [
# warm up learning rate scheduler
dict(
type='LinearLR',
start_factor=1e-3,
by_epoch=True,
- begin=0,
- # about 10000 iterations for ImageNet-1k
- end=15,
+ end=warmup_epochs,
# update by iter
convert_to_iter_based=True),
# main learning rate scheduler
dict(
type='CosineAnnealingLR',
- T_max=285,
eta_min=1e-5,
by_epoch=True,
- begin=15,
- end=300)
+ begin=warmup_epochs)
]
# train, val, test setting
diff --git a/configs/conformer/README.md b/configs/conformer/README.md
index 5b7d96b7..c0825847 100644
--- a/configs/conformer/README.md
+++ b/configs/conformer/README.md
@@ -16,12 +16,12 @@ Within Convolutional Neural Network (CNN), the convolution operations are good a
### ImageNet-1k
-| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
-| :-------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------: | :-----------------------------------------------------------------------: |
-| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) |
-| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) |
-| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) |
-| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) |
+| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+| :-------------------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------: | :------------------------------------------------------------------------------------------------: |
+| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](./conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) |
+| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](./conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) |
+| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](./conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) |
+| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](./conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) |
*Models with * are converted from the [official repo](https://github.com/pengzhiliang/Conformer). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
diff --git a/configs/convmixer/README.md b/configs/convmixer/README.md
index 763bad3c..d3ffa8af 100644
--- a/configs/convmixer/README.md
+++ b/configs/convmixer/README.md
@@ -20,11 +20,11 @@ Although convolutional networks have been the dominant architecture for vision t
### ImageNet-1k
-| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
-| :-----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :------------------------------------------------------------------------: |
-| ConvMixer-768/32\* | 21.11 | 19.62 | 80.16 | 95.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-768-32_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-768-32_3rdparty_10xb64_in1k_20220323-bca1f7b8.pth) |
-| ConvMixer-1024/20\* | 24.38 | 5.55 | 76.94 | 93.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-1024-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1024-20_3rdparty_10xb64_in1k_20220323-48f8aeba.pth) |
-| ConvMixer-1536/20\* | 51.63 | 48.71 | 81.37 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-1536-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1536_20_3rdparty_10xb64_in1k_20220323-ea5786f3.pth) |
+| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+| :-----------------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
+| ConvMixer-768/32\* | 21.11 | 19.62 | 80.16 | 95.08 | [config](./convmixer-768-32_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-768-32_3rdparty_10xb64_in1k_20220323-bca1f7b8.pth) |
+| ConvMixer-1024/20\* | 24.38 | 5.55 | 76.94 | 93.36 | [config](./convmixer-1024-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1024-20_3rdparty_10xb64_in1k_20220323-48f8aeba.pth) |
+| ConvMixer-1536/20\* | 51.63 | 48.71 | 81.37 | 95.61 | [config](./convmixer-1536-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1536_20_3rdparty_10xb64_in1k_20220323-ea5786f3.pth) |
*Models with * are converted from the [official repo](https://github.com/locuslab/convmixer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
diff --git a/configs/convmixer/metafile.yml b/configs/convmixer/metafile.yml
index 7831d746..7f75d527 100644
--- a/configs/convmixer/metafile.yml
+++ b/configs/convmixer/metafile.yml
@@ -15,7 +15,7 @@ Models:
Metadata:
FLOPs: 19623051264
Parameters: 21110248
- In Collections: ConvMixer
+ In Collection: ConvMixer
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -31,7 +31,7 @@ Models:
Metadata:
FLOPs: 5550112768
Parameters: 24383464
- In Collections: ConvMixer
+ In Collection: ConvMixer
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -47,7 +47,7 @@ Models:
Metadata:
FLOPs: 48713170944
Parameters: 51625960
- In Collections: ConvMixer
+ In Collection: ConvMixer
Results:
- Dataset: ImageNet-1k
Metrics:
diff --git a/configs/convnext/README.md b/configs/convnext/README.md
index 7db81366..077931ff 100644
--- a/configs/convnext/README.md
+++ b/configs/convnext/README.md
@@ -20,15 +20,15 @@ The "Roaring 20s" of visual recognition began with the introduction of Vision Tr
### ImageNet-1k
-| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
-| :-----------: | :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------: | :---------------------------------------------------------------------: |
-| ConvNeXt-T\* | From scratch | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) |
-| ConvNeXt-S\* | From scratch | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) |
-| ConvNeXt-B\* | From scratch | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) |
-| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) |
-| ConvNeXt-L\* | From scratch | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) |
-| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) |
-| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) |
+| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+| :-----------: | :----------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------: | :------------------------------------------------------------------------------------------------: |
+| ConvNeXt-T\* | From scratch | 28.59 | 4.46 | 82.05 | 95.86 | [config](./convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) |
+| ConvNeXt-S\* | From scratch | 50.22 | 8.69 | 83.13 | 96.44 | [config](./convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) |
+| ConvNeXt-B\* | From scratch | 88.59 | 15.36 | 83.85 | 96.74 | [config](./convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) |
+| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | 85.81 | 97.86 | [config](./convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) |
+| ConvNeXt-L\* | From scratch | 197.77 | 34.37 | 84.30 | 96.89 | [config](./convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) |
+| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | 86.61 | 98.04 | [config](./convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) |
+| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | 86.97 | 98.20 | [config](./convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) |
*Models with * are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
diff --git a/configs/convnext/metafile.yml b/configs/convnext/metafile.yml
index 823f3327..74b0a041 100644
--- a/configs/convnext/metafile.yml
+++ b/configs/convnext/metafile.yml
@@ -18,7 +18,7 @@ Models:
Metadata:
FLOPs: 4457472768
Parameters: 28589128
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -35,7 +35,7 @@ Models:
Training Data: ImageNet-1k
FLOPs: 4457472768
Parameters: 28589128
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -49,9 +49,10 @@ Models:
Code: https://github.com/facebookresearch/ConvNeXt
- Name: convnext-small_3rdparty_32xb128_in1k
Metadata:
+ Training Data: ImageNet-1k
FLOPs: 8687008512
Parameters: 50223688
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -68,7 +69,7 @@ Models:
Training Data: ImageNet-1k
FLOPs: 8687008512
Parameters: 50223688
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -82,9 +83,10 @@ Models:
Code: https://github.com/facebookresearch/ConvNeXt
- Name: convnext-base_3rdparty_32xb128_in1k
Metadata:
+ Training Data: ImageNet-1k
FLOPs: 15359124480
Parameters: 88591464
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -101,7 +103,7 @@ Models:
Training Data: ImageNet-1k
FLOPs: 15359124480
Parameters: 88591464
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -118,7 +120,7 @@ Models:
Training Data: ImageNet-21k
FLOPs: 15359124480
Parameters: 88591464
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results: null
Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_in21k_20220124-13b83eec.pth
Converted From:
@@ -131,7 +133,7 @@ Models:
- ImageNet-1k
FLOPs: 15359124480
Parameters: 88591464
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -145,9 +147,10 @@ Models:
Code: https://github.com/facebookresearch/ConvNeXt
- Name: convnext-large_3rdparty_64xb64_in1k
Metadata:
+ Training Data: ImageNet-1k
FLOPs: 34368026112
Parameters: 197767336
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -164,7 +167,7 @@ Models:
Training Data: ImageNet-21k
FLOPs: 34368026112
Parameters: 197767336
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results: null
Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_in21k_20220124-41b5a79f.pth
Converted From:
@@ -177,7 +180,7 @@ Models:
- ImageNet-1k
FLOPs: 34368026112
Parameters: 197767336
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -194,7 +197,7 @@ Models:
Training Data: ImageNet-21k
FLOPs: 60929820672
Parameters: 350196968
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results: null
Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_3rdparty_in21k_20220124-f909bad7.pth
Converted From:
@@ -207,7 +210,7 @@ Models:
- ImageNet-1k
FLOPs: 60929820672
Parameters: 350196968
- In Collections: ConvNeXt
+ In Collection: ConvNeXt
Results:
- Dataset: ImageNet-1k
Metrics:
diff --git a/configs/cspnet/README.md b/configs/cspnet/README.md
index 10eb9d0d..63267da5 100644
--- a/configs/cspnet/README.md
+++ b/configs/cspnet/README.md
@@ -20,11 +20,11 @@ Neural networks have enabled state-of-the-art approaches to achieve incredible r
### ImageNet-1k
-| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
-| :------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------: | :---------------------------------------------------------------------: |
-| CSPDarkNet50\* | From scratch | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) |
-| CSPResNet50\* | From scratch | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) |
-| CSPResNeXt50\* | From scratch | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) |
+| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+| :------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------: | :---------------------------------------------------------------------------------------------------: |
+| CSPDarkNet50\* | From scratch | 27.64 | 5.04 | 80.05 | 95.07 | [config](./cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) |
+| CSPResNet50\* | From scratch | 21.62 | 3.48 | 79.55 | 94.68 | [config](./cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) |
+| CSPResNeXt50\* | From scratch | 20.57 | 3.11 | 79.96 | 94.96 | [config](./cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) |
*Models with * are converted from the [timm repo](https://github.com/rwightman/pytorch-image-models). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
diff --git a/configs/cspnet/metafile.yml b/configs/cspnet/metafile.yml
index 8c4a78ed..815ca56a 100644
--- a/configs/cspnet/metafile.yml
+++ b/configs/cspnet/metafile.yml
@@ -17,7 +17,7 @@ Models:
Metadata:
FLOPs: 5040000000
Parameters: 27640000
- In Collections: CSPNet
+ In Collection: CSPNet
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -34,7 +34,7 @@ Models:
Training Data: ImageNet-1k
FLOPs: 3480000000
Parameters: 21620000
- In Collections: CSPNet
+ In Collection: CSPNet
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -50,7 +50,7 @@ Models:
Metadata:
FLOPs: 3110000000
Parameters: 20570000
- In Collections: CSPNet
+ In Collection: CSPNet
Results:
- Dataset: ImageNet-1k
Metrics:
diff --git a/configs/deit/README.md b/configs/deit/README.md
index e3103658..d3a32cc0 100644
--- a/configs/deit/README.md
+++ b/configs/deit/README.md
@@ -18,17 +18,17 @@ Recently, neural networks purely based on attention were shown to address image
The teacher of the distilled version DeiT is RegNetY-16GF.
-| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
-| :-------------------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------: | :--------------------------------------------------------------: |
-| DeiT-tiny | From scratch | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) |
-| DeiT-tiny distilled\* | From scratch | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) |
-| DeiT-small | From scratch | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) |
-| DeiT-small distilled\* | From scratch | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) |
-| DeiT-base | From scratch | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) |
-| DeiT-base\* | From scratch | 86.57 | 16.86 | 81.79 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth) |
-| DeiT-base distilled\* | From scratch | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) |
-| DeiT-base 384px\* | ImageNet-1k | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) |
-| DeiT-base distilled 384px\* | ImageNet-1k | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) |
+| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+| :-------------------------: | :----------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------: | :---------------------------------------------------------------------: |
+| DeiT-tiny | From scratch | 5.72 | 1.08 | 74.50 | 92.24 | [config](./deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) |
+| DeiT-tiny distilled\* | From scratch | 5.72 | 1.08 | 74.51 | 91.90 | [config](./deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) |
+| DeiT-small | From scratch | 22.05 | 4.24 | 80.69 | 95.06 | [config](./deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) |
+| DeiT-small distilled\* | From scratch | 22.05 | 4.24 | 81.17 | 95.40 | [config](./deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) |
+| DeiT-base | From scratch | 86.57 | 16.86 | 81.76 | 95.81 | [config](./deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) |
+| DeiT-base\* | From scratch | 86.57 | 16.86 | 81.79 | 95.59 | [config](./deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth) |
+| DeiT-base distilled\* | From scratch | 86.57 | 16.86 | 83.33 | 96.49 | [config](./deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) |
+| DeiT-base 384px\* | ImageNet-1k | 86.86 | 49.37 | 83.04 | 96.31 | [config](./deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) |
+| DeiT-base distilled 384px\* | ImageNet-1k | 86.86 | 49.37 | 85.55 | 97.35 | [config](./deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) |
*Models with * are converted from the [official repo](https://github.com/facebookresearch/deit). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
diff --git a/configs/densenet/README.md b/configs/densenet/README.md
index c0d3e1fe..b30f1dcf 100644
--- a/configs/densenet/README.md
+++ b/configs/densenet/README.md
@@ -16,12 +16,12 @@ Recent work has shown that convolutional networks can be substantially deeper, m
### ImageNet-1k
-| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
-| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: |
-| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) |
-| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) |
-| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) |
-| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) |
+| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+| :-----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------: | :----------------------------------------------------------------------------------------------------------------: |
+| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](./densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) |
+| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](./densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) |
+| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](./densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) |
+| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](./densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) |
*Models with * are converted from [pytorch](https://pytorch.org/vision/stable/models.html), guided by [original repo](https://github.com/liuzhuang13/DenseNet). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
diff --git a/configs/densenet/metafile.yml b/configs/densenet/metafile.yml
index 84366b23..ea93537a 100644
--- a/configs/densenet/metafile.yml
+++ b/configs/densenet/metafile.yml
@@ -14,7 +14,7 @@ Models:
Metadata:
FLOPs: 2881695488
Parameters: 7978856
- In Collections: DenseNet
+ In Collection: DenseNet
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -30,7 +30,7 @@ Models:
Metadata:
FLOPs: 3416860160
Parameters: 14149480
- In Collections: DenseNet
+ In Collection: DenseNet
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -46,7 +46,7 @@ Models:
Metadata:
FLOPs: 4365236736
Parameters: 20013928
- In Collections: DenseNet
+ In Collection: DenseNet
Results:
- Dataset: ImageNet-1k
Metrics:
@@ -62,7 +62,7 @@ Models:
Metadata:
FLOPs: 7816363968
Parameters: 28681000
- In Collections: DenseNet
+ In Collection: DenseNet
Results:
- Dataset: ImageNet-1k
Metrics:
diff --git a/configs/edgenext/README.md b/configs/edgenext/README.md
new file mode 100644
index 00000000..e2ff2983
--- /dev/null
+++ b/configs/edgenext/README.md
@@ -0,0 +1,43 @@
+# EdgeNeXt
+
+> [EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications](https://arxiv.org/abs/2206.10589)
+
+
+
+## Abstract
+
+
+
+In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are usually developed. Such models demand high computational resources and therefore cannot be deployed on edge devices. It is of great interest to build resource-efficient general purpose networks due to their usefulness in several application areas. In this work, we strive to effectively combine the strengths of both CNN and Transformer models and propose a new efficient hybrid architecture EdgeNeXt. Specifically in EdgeNeXt, we introduce split depth-wise transpose attention (SDTA) encoder that splits input tensors into multiple channel groups and utilizes depth-wise convolution along with self-attention across channel dimensions to implicitly increase the receptive field and encode multi-scale features. Our extensive experiments on classification, detection and segmentation tasks, reveal the merits of the proposed approach, outperforming state-of-the-art methods with comparatively lower compute requirements. Our EdgeNeXt model with 1.3M parameters achieves 71.2% top-1 accuracy on ImageNet-1K, outperforming MobileViT with an absolute gain of 2.2% with 28% reduction in FLOPs. Further, our EdgeNeXt model with 5.6M parameters achieves 79.4% top-1 accuracy on ImageNet-1K.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@@ -370,7 +370,7 @@ test_cfg = dict() # Use the default test loop.
In fact, in OpenMMLab 2.0, we introduced `Loop` to control the behaviors in training, validation and test. And
-the functionalities of `Runner` are also changed. You can find more details in [the MMEngine tutorials](TODO).
+the functionalities of `Runner` are also changed. You can find more details in {external+mmengine:doc}`the MMEngine tutorials `.
### Runtime settings
@@ -468,7 +468,7 @@ Changes in **`workflow`**: `workflow` related functionalities are removed.
New field **`visualizer`**: The visualizer is a new design in OpenMMLab 2.0 architecture. We use a
visualizer instance in the runner to handle results & log visualization and save to different backends.
-See the [MMEngine tutorial](TODO) for more details.
+See the {external+mmengine:doc}`MMEngine tutorial ` for more details.
```python
visualizer = dict(
@@ -481,7 +481,7 @@ visualizer = dict(
)
```
-New field **`default_scope`**: The start point to search module for all registries. The `default_scope` in MMClassification is `mmcls`. See [the registry tutorial](TODO) for more details.
+New field **`default_scope`**: The start point to search module for all registries. The `default_scope` in MMClassification is `mmcls`. See {external+mmengine:doc}`the registry tutorial ` for more details.
## Packages
diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md
deleted file mode 100644
index 83e6ec5d..00000000
--- a/docs/en/model_zoo.md
+++ /dev/null
@@ -1,155 +0,0 @@
-# Model Zoo
-
-## ImageNet
-
-ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/).
-The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize.
-
-| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
-| :--------------------------------: | :-------------------------------: | :-----------------------------: | :-------: | :-------: | :---------------------------------------: | :-----------------------------------------: |
-| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) |
-| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) |
-| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) |
-| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.log.json) |
-| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) |
-| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) |
-| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) |
-| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json) |
-| RepVGG-A0\* | 9.11（train) \| 8.31 (deploy) | 1.52 (train) \| 1.36 (deploy) | 72.41 | 90.50 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) |
-| RepVGG-A1\* | 14.09 (train) \| 12.79 (deploy) | 2.64 (train) \| 2.37 (deploy) | 74.47 | 91.85 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) |
-| RepVGG-A2\* | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) |
-| RepVGG-B0\* | 15.82 (train) \| 14.34 (deploy) | 3.42 (train) \| 3.06 (deploy) | 75.14 | 92.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) |
-| RepVGG-B1\* | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) |
-| RepVGG-B1g2\* | 45.78 (train) \| 41.36 (deploy) | 9.82 (train) \| 8.82 (deploy) | 77.79 | 93.88 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) |
-| RepVGG-B1g4\* | 39.97 (train) \| 36.13 (deploy) | 8.15 (train) \| 7.32 (deploy) | 77.58 | 93.84 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) |
-| RepVGG-B2\* | 89.02 (train) \| 80.32 (deploy) | 20.46 (train) \| 18.39 (deploy) | 78.78 | 94.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) |
-| RepVGG-B2g4\* | 61.76 (train) \| 55.78 (deploy) | 12.63 (train) \| 11.34 (deploy) | 79.38 | 94.68 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) |
-| RepVGG-B3\* | 123.09 (train) \| 110.96 (deploy) | 29.17 (train) \| 26.22 (deploy) | 80.52 | 95.26 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) |
-| RepVGG-B3g4\* | 83.83 (train) \| 75.63 (deploy) | 17.9 (train) \| 16.08 (deploy) | 80.22 | 95.10 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) |
-| RepVGG-D2se\* | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) |
-| ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.log.json) |
-| ResNet-34 | 21.8 | 3.68 | 73.85 | 91.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.log.json) |
-| ResNet-50 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) |
-| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.log.json) |
-| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.log.json) |
-| Res2Net-50-14w-8s\* | 25.06 | 4.22 | 78.14 | 93.85 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) |
-| Res2Net-50-26w-8s\* | 48.40 | 8.39 | 79.20 | 94.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) |
-| Res2Net-101-26w-4s\* | 45.21 | 8.12 | 79.19 | 94.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) |
-| ResNeSt-50\* | 27.48 | 5.41 | 81.13 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest50_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) |
-| ResNeSt-101\* | 48.28 | 10.27 | 82.32 | 96.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest101_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) |
-| ResNeSt-200\* | 70.2 | 17.53 | 82.41 | 96.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest200_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) |
-| ResNeSt-269\* | 110.93 | 22.58 | 82.70 | 96.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest269_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) |
-| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) |
-| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) |
-| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.7 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) |
-| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) |
-| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.71 | 94.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) |
-| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.23 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x8d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) |
-| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.93 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) |
-| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) |
-| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) |
-| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211208_143316.log.json) |
-| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211207_143037.log.json) |
-| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211208_143018.log.json) |
-| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211208_142720.log.json) |
-| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211207_150431.log.json) |
-| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211213_172748.log.json) |
-| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211208_103250.log.json) |
-| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211208_143713.log.json) |
-| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) |
-| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) |
-| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) |
-| ViT-B/16\* | 86.86 | 33.03 | 85.43 | 97.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth) |
-| ViT-B/32\* | 88.3 | 8.56 | 84.01 | 97.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth) |
-| ViT-L/16\* | 304.72 | 116.68 | 85.63 | 97.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth) |
-| Swin-Transformer tiny | 28.29 | 4.36 | 81.18 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-tiny_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925.log.json) |
-| Swin-Transformer small | 49.61 | 8.52 | 83.02 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219.log.json) |
-| Swin-Transformer base | 87.77 | 15.14 | 83.36 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742.log.json) |
-| Transformer in Transformer small\* | 23.76 | 3.36 | 81.52 | 95.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/tnt/tnt-s-p16_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth) |
-| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.log.json) |
-| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.log.json) |
-| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.log.json) |
-| Mixer-B/16\* | 59.88 | 12.61 | 76.68 | 92.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth) |
-| Mixer-L/16\* | 208.2 | 44.57 | 72.34 | 88.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth) |
-| DeiT-tiny | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) |
-| DeiT-tiny distilled\* | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) |
-| DeiT-small | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) |
-| DeiT-small distilled\* | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) |
-| DeiT-base | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) |
-| DeiT-base distilled\* | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) |
-| DeiT-base 384px\* | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) |
-| DeiT-base distilled 384px\* | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) |
-| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) |
-| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) |
-| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) |
-| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) |
-| PCPVT-small\* | 24.11 | 3.67 | 81.14 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth) |
-| PCPVT-base\* | 43.83 | 6.45 | 82.66 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth) |
-| PCPVT-large\* | 60.99 | 9.51 | 83.09 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth) |
-| SVT-small\* | 24.06 | 2.82 | 81.77 | 95.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth) |
-| SVT-base\* | 56.07 | 8.35 | 83.13 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth) |
-| SVT-large\* | 99.27 | 14.82 | 83.60 | 96.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth) |
-| EfficientNet-B0\* | 5.29 | 0.02 | 76.74 | 93.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth) |
-| EfficientNet-B0 (AA)\* | 5.29 | 0.02 | 77.26 | 93.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth) |
-| EfficientNet-B0 (AA + AdvProp)\* | 5.29 | 0.02 | 77.53 | 93.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth) |
-| EfficientNet-B1\* | 7.79 | 0.03 | 78.68 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth) |
-| EfficientNet-B1 (AA)\* | 7.79 | 0.03 | 79.20 | 94.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth) |
-| EfficientNet-B1 (AA + AdvProp)\* | 7.79 | 0.03 | 79.52 | 94.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth) |
-| EfficientNet-B2\* | 9.11 | 0.03 | 79.64 | 94.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth) |
-| EfficientNet-B2 (AA)\* | 9.11 | 0.03 | 80.21 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth) |
-| EfficientNet-B2 (AA + AdvProp)\* | 9.11 | 0.03 | 80.45 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth) |
-| EfficientNet-B3\* | 12.23 | 0.06 | 81.01 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth) |
-| EfficientNet-B3 (AA)\* | 12.23 | 0.06 | 81.58 | 95.67 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth) |
-| EfficientNet-B3 (AA + AdvProp)\* | 12.23 | 0.06 | 81.81 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth) |
-| EfficientNet-B4\* | 19.34 | 0.12 | 82.57 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth) |
-| EfficientNet-B4 (AA)\* | 19.34 | 0.12 | 82.95 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth) |
-| EfficientNet-B4 (AA + AdvProp)\* | 19.34 | 0.12 | 83.25 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth) |
-| EfficientNet-B5\* | 30.39 | 0.24 | 83.18 | 96.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth) |
-| EfficientNet-B5 (AA)\* | 30.39 | 0.24 | 83.82 | 96.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth) |
-| EfficientNet-B5 (AA + AdvProp)\* | 30.39 | 0.24 | 84.21 | 96.98 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth) |
-| EfficientNet-B6 (AA)\* | 43.04 | 0.41 | 84.05 | 96.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth) |
-| EfficientNet-B6 (AA + AdvProp)\* | 43.04 | 0.41 | 84.74 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth) |
-| EfficientNet-B7 (AA)\* | 66.35 | 0.72 | 84.38 | 96.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth) |
-| EfficientNet-B7 (AA + AdvProp)\* | 66.35 | 0.72 | 85.14 | 97.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth) |
-| EfficientNet-B8 (AA + AdvProp)\* | 87.41 | 1.09 | 85.38 | 97.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth) |
-| ConvNeXt-T\* | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) |
-| ConvNeXt-S\* | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) |
-| ConvNeXt-B\* | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) |
-| ConvNeXt-B\* | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) |
-| ConvNeXt-L\* | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) |
-| ConvNeXt-L\* | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) |
-| ConvNeXt-XL\* | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) |
-| HRNet-W18\* | 21.30 | 4.33 | 76.75 | 93.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth) |
-| HRNet-W30\* | 37.71 | 8.17 | 78.19 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w30_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth) |
-| HRNet-W32\* | 41.23 | 8.99 | 78.44 | 94.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w32_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth) |
-| HRNet-W40\* | 57.55 | 12.77 | 78.94 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w40_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth) |
-| HRNet-W44\* | 67.06 | 14.96 | 78.88 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w44_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth) |
-| HRNet-W48\* | 77.47 | 17.36 | 79.32 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth) |
-| HRNet-W64\* | 128.06 | 29.00 | 79.46 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w64_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth) |
-| HRNet-W18 (ssld)\* | 21.30 | 4.33 | 81.06 | 95.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth) |
-| HRNet-W48 (ssld)\* | 77.47 | 17.36 | 83.63 | 96.79 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth) |
-| WRN-50\* | 68.88 | 11.44 | 81.45 | 95.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_timm_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth) |
-| WRN-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) |
-| CSPDarkNet50\* | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) |
-| CSPResNet50\* | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) |
-| CSPResNeXt50\* | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) |
-| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) |
-| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) |
-| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) |
-| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) |
-| VAN-T\* | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) |
-| VAN-S\* | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) |
-| VAN-B\* | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) |
-| VAN-L\* | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) |
-
-*Models with * are converted from other repos, others are trained by ourselves.*
-
-## CIFAR10
-
-| Model | Params(M) | Flops(G) | Top-1 (%) | Config | Download |
-| :--------------: | :-------: | :------: | :-------: | :----: | :------------------------------------------------------------------------------------------------------------: |
-| ResNet-18-b16x8 | 11.17 | 0.56 | 94.82 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) |
-| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) |
-| ResNet-50-b16x8 | 23.52 | 1.31 | 95.55 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) |
-| ResNet-101-b16x8 | 42.51 | 2.52 | 95.58 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) |
-| ResNet-152-b16x8 | 58.16 | 3.74 | 95.76 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) |
diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md
index 4778bb84..28e3608b 100644
--- a/docs/en/notes/changelog.md
+++ b/docs/en/notes/changelog.md
@@ -1,5 +1,35 @@
# Changelog
+## v1.0.0rc1(30/9/2022)
+
+### New Features
+
+- Support MViT for MMCLS 1.x ([#1023](https://github.com/open-mmlab/mmclassification/pull/1023))
+- Add ViT huge architecture. ([#1049](https://github.com/open-mmlab/mmclassification/pull/1049))
+- Support EdgeNeXt for dev-1.x. ([#1037](https://github.com/open-mmlab/mmclassification/pull/1037))
+- Support Swin Transformer V2 for MMCLS 1.x. ([#1029](https://github.com/open-mmlab/mmclassification/pull/1029))
+- Add efficientformer Backbone for MMCls 1.x. ([#1031](https://github.com/open-mmlab/mmclassification/pull/1031))
+- Add MobileOne Backbone For MMCls 1.x. ([#1030](https://github.com/open-mmlab/mmclassification/pull/1030))
+- Support BEiT Transformer layer. ([#919](https://github.com/open-mmlab/mmclassification/pull/919))
+
+### Improvements
+
+- \[Refactor\] Fix visualization tools. ([#1045](https://github.com/open-mmlab/mmclassification/pull/1045))
+- \[Improve\] Update benchmark scripts ([#1028](https://github.com/open-mmlab/mmclassification/pull/1028))
+- \[Improve\] Update tools to enable `pin_memory` and `persistent_workers` by default. ([#1024](https://github.com/open-mmlab/mmclassification/pull/1024))
+- \[CI\] Update circle-ci and github workflow. ([#1018](https://github.com/open-mmlab/mmclassification/pull/1018))
+
+### Bug Fixes
+
+- Fix verify dataset tool in 1.x. ([#1062](https://github.com/open-mmlab/mmclassification/pull/1062))
+- Fix `loss_weight` in `LabelSmoothLoss`. ([#1058](https://github.com/open-mmlab/mmclassification/pull/1058))
+- Fix the output position of Swin-Transformer. ([#947](https://github.com/open-mmlab/mmclassification/pull/947))
+
+### Docs Update
+
+- Auto generate model summary table. ([#1010](https://github.com/open-mmlab/mmclassification/pull/1010))
+- Refactor new modules tutorial. ([#998](https://github.com/open-mmlab/mmclassification/pull/998))
+
## v1.0.0rc0(31/8/2022)
MMClassification 1.0.0rc0 is the first version of MMClassification 1.x, a part of the OpenMMLab 2.0 projects.
diff --git a/docs/en/notes/faq.md b/docs/en/notes/faq.md
index b6fde971..c8c16914 100644
--- a/docs/en/notes/faq.md
+++ b/docs/en/notes/faq.md
@@ -17,8 +17,9 @@ and make sure you fill in all required information in the template.
| MMClassification version | MMCV version |
| :----------------------: | :--------------------: |
- | 1.0.0rc0 (1.x) | mmcv>=2.0.0rc1 |
- | 0.23.1 (master) | mmcv>=1.4.2, \<1.6.0 |
+ | 1.0.0rc1 (1.x) | mmcv>=2.0.0rc1 |
+ | 0.24.0 (master) | mmcv>=1.4.2, \<1.7.0 |
+ | 0.23.1 | mmcv>=1.4.2, \<1.6.0 |
| 0.22.1 | mmcv>=1.4.2, \<1.6.0 |
| 0.21.0 | mmcv>=1.4.2, \<=1.5.0 |
| 0.20.1 | mmcv>=1.4.2, \<=1.5.0 |
diff --git a/docs/en/stat.py b/docs/en/stat.py
index 8f1e5b2d..030f07b9 100755
--- a/docs/en/stat.py
+++ b/docs/en/stat.py
@@ -1,100 +1,145 @@
#!/usr/bin/env python
-import functools as func
-import glob
-import os
import re
+from collections import defaultdict
from pathlib import Path
-import numpy as np
+from modelindex.load_model_index import load
+from tabulate import tabulate
-MMCLS_ROOT = Path(__file__).absolute().parents[1]
-url_prefix = 'https://github.com/open-mmlab/mmclassification/blob/master/'
+MMCLS_ROOT = Path(__file__).absolute().parents[2]
+PAPERS_ROOT = Path('papers') # Path to save generated paper pages.
+GITHUB_PREFIX = 'https://github.com/open-mmlab/mmclassification/blob/1.x/'
+MODELZOO_TEMPLATE = """
+# Model Zoo Summary
-papers_root = Path('papers')
-papers_root.mkdir(exist_ok=True)
-files = [Path(f) for f in sorted(glob.glob('../../configs/*/README.md'))]
+* Number of papers: {num_papers}
+{type_msg}
-stats = []
-titles = []
-num_ckpts = 0
-num_configs = 0
+* Number of checkpoints: {num_ckpts}
+{paper_msg}
+"""
-for f in files:
- with open(f, 'r') as content_file:
- content = content_file.read()
+model_index = load(str(MMCLS_ROOT / 'model-index.yml'))
- # Extract checkpoints
- ckpts = set(x.lower().strip()
- for x in re.findall(r'\[model\]\((https?.*)\)', content))
- if len(ckpts) == 0:
- continue
- num_ckpts += len(ckpts)
- # Extract paper title
- match_res = list(re.finditer(r'> \[(.*)\]\((.*)\)', content))
- if len(match_res) > 0:
- title, paperlink = match_res[0].groups()
- else:
- title = content.split('\n')[0].replace('# ', '').strip()
- paperlink = None
- titles.append(title)
+def build_collections(model_index):
+ col_by_name = {}
+ for col in model_index.collections:
+ setattr(col, 'models', [])
+ col_by_name[col.name] = col
- # Replace paper link to a button
- if paperlink is not None:
- start = match_res[0].start()
- end = match_res[0].end()
- # link_button = f'{title}'
- link_button = f'[{title}]({paperlink})'
- content = content[:start] + link_button + content[end:]
+ for model in model_index.models:
+ col = col_by_name[model.in_collection]
+ col.models.append(model)
+ setattr(model, 'collection', col)
- # Extract paper type
- _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
- assert len(_papertype) > 0
- papertype = _papertype[0]
- paper = set([(papertype, title)])
+
+build_collections(model_index)
+
+
+def count_papers(collections):
+ total_num_ckpts = 0
+ type_count = defaultdict(int)
+ paper_msgs = []
+
+ for collection in collections:
+ with open(MMCLS_ROOT / collection.readme) as f:
+ readme = f.read()
+ ckpts = set(x.lower().strip()
+ for x in re.findall(r'\[model\]\((https?.*)\)', readme))
+ total_num_ckpts += len(ckpts)
+ title = collection.paper['Title']
+ papertype = collection.data.get('type', 'Algorithm')
+ type_count[papertype] += 1
+
+ readme = PAPERS_ROOT / Path(
+ collection.filepath).parent.with_suffix('.md').name
+ paper_msgs.append(
+ f'\t- [{papertype}] [{title}]({readme}) ({len(ckpts)} ckpts)')
+
+ type_msg = '\n'.join(
+ [f'\t- {type_}: {count}' for type_, count in type_count.items()])
+ paper_msg = '\n'.join(paper_msgs)
+
+ modelzoo = MODELZOO_TEMPLATE.format(
+ num_papers=len(collections),
+ num_ckpts=total_num_ckpts,
+ type_msg=type_msg,
+ paper_msg=paper_msg,
+ )
+
+ with open('modelzoo_statistics.md', 'w') as f:
+ f.write(modelzoo)
+
+
+count_papers(model_index.collections)
+
+
+def generate_paper_page(collection):
+ PAPERS_ROOT.mkdir(exist_ok=True)
# Write a copy of README
- copy = papers_root / (f.parent.name + '.md')
- if copy.exists():
- os.remove(copy)
+ with open(MMCLS_ROOT / collection.readme) as f:
+ readme = f.read()
+ folder = Path(collection.filepath).parent
+ copy = PAPERS_ROOT / folder.with_suffix('.md').name
def replace_link(matchobj):
# Replace relative link to GitHub link.
name = matchobj.group(1)
link = matchobj.group(2)
- if not link.startswith('http') and (f.parent / link).exists():
- rel_link = (f.parent / link).absolute().relative_to(MMCLS_ROOT)
- link = url_prefix + str(rel_link)
+ if not link.startswith('http'):
+ assert (folder / link).exists(), \
+ f'Link not found:\n{collection.readme}: {link}'
+ rel_link = (folder / link).absolute().relative_to(MMCLS_ROOT)
+ link = GITHUB_PREFIX + str(rel_link)
return f'[{name}]({link})'
- content = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', replace_link, content)
+ content = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', replace_link, readme)
with open(copy, 'w') as copy_file:
copy_file.write(content)
- statsmsg = f"""
-\t* [{papertype}] [{title}]({copy}) ({len(ckpts)} ckpts)
-"""
- stats.append(dict(paper=paper, ckpts=ckpts, statsmsg=statsmsg, copy=copy))
-allpapers = func.reduce(lambda a, b: a.union(b),
- [stat['paper'] for stat in stats])
-msglist = '\n'.join(stat['statsmsg'] for stat in stats)
+for collection in model_index.collections:
+ generate_paper_page(collection)
-papertypes, papercounts = np.unique([t for t, _ in allpapers],
- return_counts=True)
-countstr = '\n'.join(
- [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])
-modelzoo = f"""
-# Model Zoo Summary
+def generate_summary_table(models):
+ dataset_rows = defaultdict(list)
+ for model in models:
+ if model.results is None:
+ continue
+ name = model.name
+ params = model.metadata.parameters / 1e6
+ flops = model.metadata.flops / 1e9
+ result = model.results[0]
+ top1 = result.metrics.get('Top 1 Accuracy')
+ top5 = result.metrics.get('Top 5 Accuracy')
+ readme = Path(model.collection.filepath).parent.with_suffix('.md').name
+ page = f'[link]({PAPERS_ROOT / readme})'
+ row = [name, params, flops, top1, top5, page]
+ dataset_rows[result.dataset].append(row)
-* Number of papers: {len(set(titles))}
-{countstr}
+ with open('modelzoo_statistics.md', 'a') as f:
+ for dataset, rows in dataset_rows.items():
+ f.write(f'\n## {dataset}\n')
+ f.write("""```{table}\n:class: model-summary\n""")
+ header = [
+ 'Model',
+ 'Params (M)',
+ 'Flops (G)',
+ 'Top-1 (%)',
+ 'Top-5 (%)',
+ 'Readme',
+ ]
+ table_cfg = dict(
+ tablefmt='pipe',
+ floatfmt='.2f',
+ numalign='right',
+ stralign='center')
+ f.write(tabulate(rows, header, **table_cfg))
+ f.write('\n```\n')
-* Number of checkpoints: {num_ckpts}
-{msglist}
-"""
-with open('modelzoo_statistics.md', 'w') as f:
- f.write(modelzoo)
+generate_summary_table(model_index.models)
diff --git a/docs/en/user_guides/config.md b/docs/en/user_guides/config.md
index 195e7655..c4fe19fb 100644
--- a/docs/en/user_guides/config.md
+++ b/docs/en/user_guides/config.md
@@ -2,7 +2,7 @@
To manage various configurations in a deep-learning experiment, we use a kind of config file to record all of
these configurations. This config system has a modular and inheritance design, and more details can be found in
-[the tutorial in MMEngine](TODO).
+{external+mmengine:doc}`the tutorial in MMEngine `.
Usually, we use python files as config file. All configuration files are placed under the [`configs`](https://github.com/open-mmlab/mmclassification/tree/1.x/configs) folder, and the directory structure is as follows:
@@ -64,7 +64,7 @@ This primitive config file includes a dict variable `model`, which mainly includ
```{note}
Usually, we use the `type` field to specify the class of the component and use other fields to pass
-the initialization arguments of the class. The [registry tutorial](TODO) describes it in detail.
+the initialization arguments of the class. The {external+mmengine:doc}`registry tutorial ` describes it in detail.
```
Following is the model primitive config of the ResNet50 config file in [`configs/_base_/models/resnet50.py`](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/_base_/models/resnet50.py):
@@ -178,12 +178,12 @@ test loops:
- `optim_wrapper`: The settings of the optimizer wrapper. We use the optimizer wrapper to customize the
optimization process.
- - `optimizer`: Supports all `pytorch` optimizers, refers to the relevant [MMEngine documentation](TODO).
- - `paramwise_cfg`: To set different optimization arguments according to the parameters' type or name, refer to the relevant [learning policy documentation](TODO).
+ - `optimizer`: Supports all `pytorch` optimizers, refers to the relevant {external+mmengine:doc}`MMEngine documentation `.
+ - `paramwise_cfg`: To set different optimization arguments according to the parameters' type or name, refer to the relevant [learning policy documentation](../advanced_guides/schedule.md).
- `accumulative_counts`: Optimize parameters after several backward steps instead of one backward step. You
can use it to simulate large batch size by small batch size.
-- `param_scheduler`: Optimizer parameters policy. You can use it to specify learning rate and momentum curves during training. See the [documentation](TODO) in MMEngine for more details.
-- `train_cfg | val_cfg | test_cfg`: The settings of the training, validation and test loops, refer to the relevant [MMEngine documentation](TODO).
+- `param_scheduler`: Optimizer parameters policy. You can use it to specify learning rate and momentum curves during training. See the {external+mmengine:doc}`documentation ` in MMEngine for more details.
+- `train_cfg | val_cfg | test_cfg`: The settings of the training, validation and test loops, refer to the relevant {external+mmengine:doc}`MMEngine documentation `.
Following is the schedule primitive config of the ResNet50 config in [`configs/_base_/datasets/imagenet_bs32.py`](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/_base_/datasets/imagenet_bs32.py)：
@@ -348,7 +348,7 @@ test_dataloader = dict(dataset=dict(pipeline=val_pipeline))
### Ignore some fields in the base configs
-Sometimes, you need to set `_delete_=True` to ignore some domain content in the basic configuration file. You can refer to the [documentation in MMEngine](TODO) for more instructions.
+Sometimes, you need to set `_delete_=True` to ignore some domain content in the basic configuration file. You can refer to the {external+mmengine:doc}`documentation in MMEngine ` for more instructions.
The following is an example. If you want to use cosine schedule in the above ResNet50 case, just using inheritance and directly modifying it will report `get unexpected keyword 'step'` error, because the `'step'` field of the basic config in `param_scheduler` domain information is reserved, and you need to add `_delete_ =True` to ignore the content of `param_scheduler` related fields in the basic configuration file:
@@ -361,7 +361,7 @@ param_scheduler = dict(type='CosineAnnealingLR', by_epoch=True, _delete_=True)
### Use some fields in the base configs
-Sometimes, you may refer to some fields in the `_base_` config, to avoid duplication of definitions. You can refer to [MMEngine](TODO:) for some more instructions.
+Sometimes, you may refer to some fields in the `_base_` config, to avoid duplication of definitions. You can refer to {external+mmengine:doc}`MMEngine ` for some more instructions.
The following is an example of using auto augment in the training data preprocessing pipeline, refer to [`configs/resnest/resnest50_32xb64_in1k.py`](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/resnest/resnest50_32xb64_in1k.py). When defining `train_pipeline`, just add the definition file name of auto augment to `_base_`, and then use `_base_.auto_increasing_policies` to reference the variables in the primitive config:
diff --git a/docs/en/user_guides/dataset_prepare.md b/docs/en/user_guides/dataset_prepare.md
index dfd521fc..096d0797 100644
--- a/docs/en/user_guides/dataset_prepare.md
+++ b/docs/en/user_guides/dataset_prepare.md
@@ -304,7 +304,7 @@ To find more datasets supported by MMClassification, and get more configurations
## Dataset Wrappers
-The following datawrappers are supported in MMEngine, you can refer to [MMEngine tutorial](TODO:) to learn how to use it.
+The following datawrappers are supported in MMEngine, you can refer to {external+mmengine:doc}`MMEngine tutorial ` to learn how to use it.
- [ConcatDataset](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/basedataset.md#concatdataset)
- [RepeatDataset](https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/basedataset.md#repeatdataset)
diff --git a/docs/en/user_guides/finetune.md b/docs/en/user_guides/finetune.md
index 4f83f849..967f7938 100644
--- a/docs/en/user_guides/finetune.md
+++ b/docs/en/user_guides/finetune.md
@@ -3,7 +3,7 @@
In most scenarios, we want to apply a model on new datasets without training from scratch, which might possibly introduce extra uncertainties about the model convergency and therefore, is time-consuming.
The common sense is to learn from previous models trained on large dataset, which can hopefully provide better knowledge than a random beginner. Roughly speaking, this process is as known as fine-tuning.
Classification models pre-trained on the ImageNet dataset have been demonstrated to be effective for other datasets and other downstream tasks.
-Hence, this tutorial provides instructions for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance.
+Hence, this tutorial provides instructions for users to use the models provided in the [Model Zoo](../modelzoo_statistics.md) for other datasets to obtain better performance.
There are two steps to fine-tune a model on a new dataset.
diff --git a/docs/en/user_guides/inference.md b/docs/en/user_guides/inference.md
index 5221f0a1..a1c970c1 100644
--- a/docs/en/user_guides/inference.md
+++ b/docs/en/user_guides/inference.md
@@ -1,6 +1,6 @@
# Inference with existing models
-MMClassification provides pre-trained models for classification in [Model Zoo](../model_zoo.md).
+MMClassification provides pre-trained models for classification in [Model Zoo](../modelzoo_statistics.md).
This note will show **how to use existing models to inference on given images**.
As for how to test existing models on standard datasets, please see this [guide](./train_test.md#test)
diff --git a/docs/en/user_guides/visualization.md b/docs/en/user_guides/visualization.md
index 1eeea642..dbf3e6f5 100644
--- a/docs/en/user_guides/visualization.md
+++ b/docs/en/user_guides/visualization.md
@@ -1,134 +1,138 @@
-# Visualization Tools (TODO)
+# Visualization Tools
-- [Pipeline Visualization](#pipeline-visualization)
-- [Learning Rate Schedule Visualization](#learning-rate-schedule-visualization)
+- [Browse Dataset](#browse-dataset)
+- [Parameter Schedule Visualization](#parameter-schedule-visualization)
- [Class Activation Map Visualization](#class-activation-map-visualization)
- [FAQs](#faqs)
-## Pipeline Visualization
+## Browse Dataset
```bash
-python tools/visualizations/vis_pipeline.py \
+python tools/visualizations/browse_dataset.py \
${CONFIG_FILE} \
- [--output-dir ${OUTPUT_DIR}] \
- [--phase ${DATASET_PHASE}] \
- [--number ${BUNBER_IMAGES_DISPLAY}] \
- [--skip-type ${SKIP_TRANSFORM_TYPE}] \
- [--mode ${DISPLAY_MODE}] \
- [--show] \
- [--adaptive] \
- [--min-edge-length ${MIN_EDGE_LENGTH}] \
- [--max-edge-length ${MAX_EDGE_LENGTH}] \
- [--bgr2rgb] \
- [--window-size ${WINDOW_SIZE}] \
+ [-o, --output-dir ${OUTPUT_DIR}] \
+ [-p, --phase ${DATASET_PHASE}] \
+ [-n, --show-number ${NUMBER_IMAGES_DISPLAY}] \
+ [-i, --show-interval ${SHOW_INTERRVAL}] \
+ [-m, --mode ${DISPLAY_MODE}] \
+ [-r, --rescale-factor ${RESCALE_FACTOR}] \
+ [-c, --channel-order ${CHANNEL_ORDER}] \
[--cfg-options ${CFG_OPTIONS}]
```
**Description of all arguments**：
- `config` : The path of a model config file.
-- `--output-dir`: The output path for visualized images. If not specified, it will be set to `''`, which means not to save.
-- `--phase`: Phase of visualizing dataset，must be one of `[train, val, test]`. If not specified, it will be set to `train`.
-- `--number`: The number of samples to visualized. If not specified, display all images in the dataset.
-- `--skip-type`: The pipelines to be skipped. If not specified, it will be set to `['ToTensor', 'Normalize', 'ImageToTensor', 'Collect']`.
-- `--mode`: The display mode, can be one of `[original, pipeline, concat]`. If not specified, it will be set to `concat`.
-- `--show`: If set, display pictures in pop-up windows.
-- `--adaptive`: If set, adaptively resize images for better visualization.
-- `--min-edge-length`: The minimum edge length, used when `--adaptive` is set. When any side of the picture is smaller than `${MIN_EDGE_LENGTH}`, the picture will be enlarged while keeping the aspect ratio unchanged, and the short side will be aligned to `${MIN_EDGE_LENGTH}`. If not specified, it will be set to 200.
-- `--max-edge-length`: The maximum edge length, used when `--adaptive` is set. When any side of the picture is larger than `${MAX_EDGE_LENGTH}`, the picture will be reduced while keeping the aspect ratio unchanged, and the long side will be aligned to `${MAX_EDGE_LENGTH}`. If not specified, it will be set to 1000.
-- `--bgr2rgb`: If set, flip the color channel order of images.
-- `--window-size`: The shape of the display window. If not specified, it will be set to `12*7`. If used, it must be in the format `'W*H'`.
-- `--cfg-options` : Modifications to the configuration file, refer to [Tutorial 1: Learn about Configs](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html).
+- `-o, --output-dir`: The output path for visualized images. If not specified, it will be set to `''`, which means not to save.
+- **`-p, --phase`**: Phase of visualizing dataset，must be one of `['train', 'val', 'test']`. If not specified, it will be set to `'train'`.
+- **`-n, --show-number`**: The number of samples to visualized. If not specified, display all images in the dataset.
+- `--show-interval`: The interval of show (s).
+- **`-m, --mode`**: The display mode, can be one of `['original', 'transformed', 'concat', 'pipeline']`. If not specified, it will be set to `'transformed'`.
+- **`-r, --rescale-factor`**: The image rescale factor, which is useful if the output is too large or too small.
+- `-c, --channel-order`: The channel of the showing images, could be "BGR" or "RGB", If not specified, it will be set to 'BGR'.
+- `--cfg-options` : Modifications to the configuration file, refer to [Learn about Configs](./config.md).
```{note}
+1. The `-m, --mode` is about display mode, display original pictures or transformed pictures or comparison pictures:
+- "original" means show images load from disk;
+- "transformed" means to show images after transformed;
+- "concat" means show images stitched by "original" and "transformed" images;
+- "pipeline" means show all the intermediate images throghout the pipeline.
-1. If the `--mode` is not specified, it will be set to `concat` as default, get the pictures stitched together by original pictures and transformed pictures; if the `--mode` is set to `original`, get the original pictures; if the `--mode` is set to `transformed`, get the transformed pictures; if the `--mode` is set to `pipeline`, get all the intermediate images through the pipeline.
-
-2. When `--adaptive` option is set, images that are too large or too small will be automatically adjusted, you can use `--min-edge-length` and `--max-edge-length` to set the adjust size.
+2. The `-r, --rescale-factor` option is set when the label information is too large or too small relative to the picture. For example, when visualizing the CIFAR dataset, since the resolution of the image is very small, `--rescale-factor` can be set to 10.
```
**Examples**：
-1. In **'original'** mode, visualize 100 original pictures in the `CIFAR100` validation set, then display and save them in the `./tmp` folder：
+1. In **'original'** mode:
```shell
-python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb
+python ./tools/visualizations/browse_dataset.py ./configs/resnet/resnet101_8xb16_cifar10.py --phase val --output-dir tmp --mode original --show-number 100 --rescale-factor 10 --channel-order RGB
```
-
