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configs/barlowtwins/benchmarks/resnet50_8xb32-linear-coslr-100e_in1k.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../../_base_/models/resnet50.py',
'../../_base_/datasets/imagenet_bs32_pil_resize.py',
'../../_base_/schedules/imagenet_sgd_coslr_100e.py',
'../../_base_/default_runtime.py',
]
model = dict(
backbone=dict(
frozen_stages=4,
init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')))
# runtime settings
default_hooks = dict(
checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3))
configs/barlowtwins/metafile.yml 0 → 100644
View file @ 495d9ed9
Collections:
- Name: BarlowTwins
Metadata:
Training Data: ImageNet-1k
Training Techniques:
- LARS
Training Resources: 8x A100 GPUs
Architecture:
- ResNet
- BarlowTwins
Paper:
Title: 'Barlow Twins: Self-Supervised Learning via Redundancy Reduction'
URL: https://arxiv.org/abs/2103.03230
README: configs/barlowtwins/README.md
Models:
- Name: barlowtwins_resnet50_8xb256-coslr-300e_in1k
Metadata:
Epochs: 300
Batch Size: 2048
FLOPs: 4109364224
Parameters: 174535744
Training Data: ImageNet-1k
In Collection: BarlowTwins
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k/barlowtwins_resnet50_8xb256-coslr-300e_in1k_20220825-57307488.pth
Config: configs/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k.py
Downstream:
- resnet50_barlowtwins-pre_8xb32-linear-coslr-100e_in1k
- Name: resnet50_barlowtwins-pre_8xb32-linear-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 256
FLOPs: 4109464576
Parameters: 25557032
Training Data: ImageNet-1k
In Collection: BarlowTwins
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 71.8
Weights: https://download.openmmlab.com/mmselfsup/1.x/barlowtwins/barlowtwins_resnet50_8xb256-coslr-300e_in1k/resnet50_linear-8xb32-coslr-100e_in1k/resnet50_linear-8xb32-coslr-100e_in1k_20220825-52fde35f.pth
Config: configs/barlowtwins/benchmarks/resnet50_8xb32-linear-coslr-100e_in1k.py
configs/beit/README.md 0 → 100644
View file @ 495d9ed9
# BEiT
> [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)
<!-- [ALGORITHM] -->
## Abstract
We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first "tokenize" the original image into visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder. Experimental results on image classification and semantic segmentation show that our model achieves competitive results with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K, significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains 86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%).
<div align=center>
<img src="https://user-images.githubusercontent.com/36138628/203688351-adac7146-4e71-4ab6-8958-5cfe643a2dc5.png" width="70%"/>
</div>
## How to use it?
<!-- [TABS-BEGIN] -->
**Predict image**
```python
from mmpretrain import inference_model
predict = inference_model('beit-base-p16_beit-pre_8xb128-coslr-100e_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])
```
**Use the model**
```python
import torch
from mmpretrain import get_model
model = get_model('beit_beit-base-p16_8xb256-amp-coslr-300e_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))
```
**Train/Test Command**
Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
Train:
```shell
python tools/train.py configs/beit/beit_beit-base-p16_8xb256-amp-coslr-300e_in1k.py
```
Test:
```shell
python tools/test.py configs/beit/benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/beit/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20221128-0ca393e9.pth
```
<!-- [TABS-END] -->
## Models and results
### Pretrained models
| Model | Params (M) | Flops (G) | Config | Download |
| :---------------------------------------------- | :--------: | :-------: | :--------------------------------------------------------: | :--------------------------------------------------------------------------: |
| `beit_beit-base-p16_8xb256-amp-coslr-300e_in1k` | 86.53 | 17.58 | [config](beit_beit-base-p16_8xb256-amp-coslr-300e_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/1.x/beit/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221128-ab79e626.pth) \| [log](https://download.openmmlab.com/mmselfsup/1.x/beit/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221128-ab79e626.json) |
### Image Classification on ImageNet-1k
| Model | Pretrain | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) | Config | Download |
| :-------------------------------------- | :----------------------------------------: | :--------: | :-------: | :-------: | :-------: | :--------------------------------------: | :----------------------------------------: |
| `beit-base-p16_beit-pre_8xb128-coslr-100e_in1k` | [BEIT](https://download.openmmlab.com/mmselfsup/1.x/beit/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221128-ab79e626.pth) | 86.53 | 17.58 | 83.10 | N/A | [config](benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/1.x/beit/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20221128-0ca393e9.pth) \| [log](https://download.openmmlab.com/mmselfsup/1.x/beit/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20221128-0ca393e9.json) |
| `beit-base-p16_beit-in21k-pre_3rdparty_in1k`\* | BEIT ImageNet-21k | 86.53 | 17.58 | 85.28 | 97.59 | [config](benchmarks/beit-base-p16_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/beit/beit-base_3rdparty_in1k_20221114-c0a4df23.pth) |
*Models with * are converted from the [official repo](https://github.com/microsoft/unilm/tree/master/beit). The config files of these models are only for inference. We haven't reproduce the training results.*
## Citation
```bibtex
@inproceedings{bao2022beit,
title={{BE}iT: {BERT} Pre-Training of Image Transformers},
author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
booktitle={International Conference on Learning Representations},
year={2022},
}
```
configs/beit/beit_beit-base-p16_8xb256-amp-coslr-300e_in1k.py 0 → 100644
View file @ 495d9ed9
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'ImageNet'
data_root = 'data/imagenet/'
data_preprocessor = dict(
type='TwoNormDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
second_mean=[-31.875, -31.875, -31.875],
second_std=[318.75, 318.75, 318.75],
to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ColorJitter',
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandomResizedCropAndInterpolationWithTwoPic',
size=224,
second_size=112,
interpolation='bicubic',
second_interpolation='lanczos',
scale=(0.08, 1.0)),
dict(
type='BEiTMaskGenerator',
input_size=(14, 14),
num_masking_patches=75,
max_num_patches=None,
min_num_patches=16),
dict(type='PackInputs')
]
train_dataloader = dict(
batch_size=256,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
collate_fn=dict(type='default_collate'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='meta/train.txt',
data_prefix=dict(img_path='train/'),
pipeline=train_pipeline))
# model settings
model = dict(
type='BEiT',
backbone=dict(
type='BEiTPretrainViT',
arch='base',
patch_size=16,
drop_path_rate=0.1,
final_norm=True,
out_type='raw',
layer_scale_init_value=0.1,
init_cfg=[
dict(type='TruncNormal', std=0.02, layer='Linear'),
dict(type='TruncNormal', std=0.02, layer='Conv2d'),
dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
]),
neck=None,
head=dict(
type='BEiTV1Head',
embed_dims=768,
num_embed=8192,
loss=dict(type='CrossEntropyLoss')),
target_generator=dict(
type='DALL-E',
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa: E251
'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/dalle_encoder.pth', # noqa: E501
)))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW', lr=1.5e-3, betas=(0.9, 0.999), weight_decay=0.05),
clip_grad=dict(max_norm=3.0),
paramwise_cfg=dict(
custom_keys={
# the following configurations are designed for BEiT
'.ln': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'q_bias': dict(decay_mult=0.0),
'v_bias': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0),
'.gamma': dict(decay_mult=0.0),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=10,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
eta_min=1e-5,
by_epoch=True,
begin=10,
end=300,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
default_hooks = dict(
# only keeps the latest 3 checkpoints
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
randomness = dict(seed=0, diff_rank_seed=True)
find_unused_parameters = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=2048)
configs/beit/benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../../_base_/datasets/imagenet_bs64_swin_224.py',
'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../../_base_/default_runtime.py'
]
data_preprocessor = dict(
num_classes=1000,
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
to_rgb=True,
)
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='BEiTViT',
arch='base',
img_size=224,
patch_size=16,
drop_path_rate=0.1,
out_type='avg_featmap',
use_abs_pos_emb=False,
use_rel_pos_bias=True,
use_shared_rel_pos_bias=False,
init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=[dict(type='TruncNormal', layer='Linear', std=0.02)]),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=224,
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=[104, 116, 124], interpolation='bicubic')),
dict(
type='RandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=0.3333333333333333,
fill_color=[103.53, 116.28, 123.675],
fill_std=[57.375, 57.12, 58.395]),
dict(type='PackInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=256,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs')
]
train_dataloader = dict(batch_size=128, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(batch_size=128, dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# optimizer wrapper
optim_wrapper = dict(
optimizer=dict(
type='AdamW', lr=4e-3, weight_decay=0.05, betas=(0.9, 0.999)),
constructor='LearningRateDecayOptimWrapperConstructor',
paramwise_cfg=dict(
_delete_=True,
layer_decay_rate=0.65,
custom_keys={
# the following configurations are designed for BEiT
'.ln': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'q_bias': dict(decay_mult=0.0),
'v_bias': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0),
'.gamma': dict(decay_mult=0.0),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=20,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
by_epoch=True,
begin=20,
end=100,
eta_min=1e-6,
convert_to_iter_based=True)
]
# runtime settings
default_hooks = dict(
# save checkpoint per epoch.
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=2))
train_cfg = dict(by_epoch=True, max_epochs=100)
randomness = dict(seed=0)
configs/beit/benchmarks/beit-base-p16_8xb64_in1k.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../../_base_/datasets/imagenet_bs64_swin_224.py',
'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../../_base_/default_runtime.py'
]
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
# convert image from BGR to RGB
to_rgb=True,
)
model = dict(
type='ImageClassifier',
backbone=dict(
type='BEiTViT',
arch='base',
img_size=224,
patch_size=16,
out_type='avg_featmap',
use_abs_pos_emb=False,
use_rel_pos_bias=True,
use_shared_rel_pos_bias=False,
),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=.02),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]))
configs/beit/metafile.yml 0 → 100644
View file @ 495d9ed9
Collections:
- Name: BEiT
Metadata:
Architecture:
- Attention Dropout
- Convolution
- Dense Connections
- Dropout
- GELU
- Layer Normalization
- Multi-Head Attention
- Scaled Dot-Product Attention
- Tanh Activation
Paper:
Title: 'BEiT: BERT Pre-Training of Image Transformers'
URL: https://arxiv.org/abs/2106.08254
README: configs/beit/README.md
Code:
URL: https://github.com/open-mmlab/mmpretrain/blob/main/mmpretrain/models/backbones/beit.py
Version: v1.0.0rc4
Models:
- Name: beit_beit-base-p16_8xb256-amp-coslr-300e_in1k
Metadata:
Epochs: 300
Batch Size: 2048
FLOPs: 17581219584
Parameters: 86530984
Training Data: ImageNet-1k
In Collection: BEiT
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/beit/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221128-ab79e626.pth
Config: configs/beit/beit_beit-base-p16_8xb256-amp-coslr-300e_in1k.py
Downstream:
- beit-base-p16_beit-pre_8xb128-coslr-100e_in1k
- Name: beit-base-p16_beit-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581219584
Parameters: 86530984
Training Data: ImageNet-1k
In Collection: BEiT
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.1
Weights: https://download.openmmlab.com/mmselfsup/1.x/beit/beit_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20221128-0ca393e9.pth
Config: configs/beit/benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py
- Name: beit-base-p16_beit-in21k-pre_3rdparty_in1k
Metadata:
FLOPs: 17581219584
Parameters: 86530984
Training Data:
- ImageNet-21k
- ImageNet-1k
In Collection: BEiT
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 85.28
Top 5 Accuracy: 97.59
Weights: https://download.openmmlab.com/mmclassification/v0/beit/beit-base_3rdparty_in1k_20221114-c0a4df23.pth
Config: configs/beit/benchmarks/beit-base-p16_8xb64_in1k.py
Converted From:
Weights: https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth
Code: https://github.com/microsoft/unilm/tree/master/beit
configs/beitv2/README.md 0 → 100644
View file @ 495d9ed9
# BEiTv2
> [BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://arxiv.org/abs/2208.06366)
<!-- [ALGORITHM] -->
## Abstract
Masked image modeling (MIM) has demonstrated impressive results in self-supervised representation learning by recovering corrupted image patches. However, most existing studies operate on low-level image pixels, which hinders the exploitation of high-level semantics for representation models. In this work, we propose to use a semantic-rich visual tokenizer as the reconstruction target for masked prediction, providing a systematic way to promote MIM from pixel-level to semantic-level. Specifically, we propose vector-quantized knowledge distillation to train the tokenizer, which discretizes a continuous semantic space to compact codes. We then pretrain vision Transformers by predicting the original visual tokens for the masked image patches. Furthermore, we introduce a patch aggregation strategy which associates discrete image patches to enhance global semantic representation. Experiments on image classification and semantic segmentation show that BEiT v2 outperforms all compared MIM methods. On ImageNet-1K (224 size), the base-size BEiT v2 achieves 85.5% top-1 accuracy for fine-tuning and 80.1% top-1 accuracy for linear probing. The large-size BEiT v2 obtains 87.3% top-1 accuracy for ImageNet-1K (224 size) fine-tuning, and 56.7% mIoU on ADE20K for semantic segmentation.
<div align=center>
<img src="https://user-images.githubusercontent.com/36138628/203912182-5967a520-d455-49ea-bc67-dcbd500d76bf.png" width="70%"/>
</div>
## How to use it?
<!-- [TABS-BEGIN] -->
**Predict image**
```python
from mmpretrain import inference_model
predict = inference_model('beit-base-p16_beitv2-pre_8xb128-coslr-100e_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])
```
**Use the model**
```python
import torch
from mmpretrain import get_model
model = get_model('beitv2_beit-base-p16_8xb256-amp-coslr-300e_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))
```
**Train/Test Command**
Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
Train:
```shell
python tools/train.py configs/beitv2/beitv2_beit-base-p16_8xb256-amp-coslr-300e_in1k.py
```
Test:
```shell
python tools/test.py configs/beitv2/benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py https://download.openmmlab.com/mmselfsup/1.x/beitv2/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20221212-d1c0789e.pth
```
<!-- [TABS-END] -->
## Models and results
### Pretrained models
| Model | Params (M) | Flops (G) | Config | Download |
| :------------------------------------------------ | :--------: | :-------: | :----------------------------------------------------------: | :----------------------------------------------------------------------: |
| `beitv2_beit-base-p16_8xb256-amp-coslr-300e_in1k` | 192.81 | 17.58 | [config](beitv2_beit-base-p16_8xb256-amp-coslr-300e_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/1.x/beitv2/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221212-a157be30.pth) \| [log](https://download.openmmlab.com/mmselfsup/1.x/beitv2/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221212-a157be30.json) |
### Image Classification on ImageNet-1k
| Model | Pretrain | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) | Config | Download |
| :-------------------------------------- | :----------------------------------------: | :--------: | :-------: | :-------: | :-------: | :--------------------------------------: | :----------------------------------------: |
| `beit-base-p16_beitv2-pre_8xb128-coslr-100e_in1k` | [BEITV2](https://download.openmmlab.com/mmselfsup/1.x/beitv2/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221212-a157be30.pth) | 86.53 | 17.58 | 85.00 | N/A | [config](benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py) | [model](https://download.openmmlab.com/mmselfsup/1.x/beitv2/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20221212-d1c0789e.pth) \| [log](https://download.openmmlab.com/mmselfsup/1.x/beitv2/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20221212-d1c0789e.json) |
| `beit-base-p16_beitv2-in21k-pre_3rdparty_in1k`\* | BEITV2 ImageNet-21k | 86.53 | 17.58 | 86.47 | 97.99 | [config](benchmarks/beit-base-p16_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/beit/beitv2-base_3rdparty_in1k_20221114-73e11905.pth) |
*Models with * are converted from the [official repo](https://github.com/microsoft/unilm/tree/master/beit2). The config files of these models are only for inference. We haven't reproduce the training results.*
## Citation
```bibtex
@article{beitv2,
title={{BEiT v2}: Masked Image Modeling with Vector-Quantized Visual Tokenizers},
author={Zhiliang Peng and Li Dong and Hangbo Bao and Qixiang Ye and Furu Wei},
year={2022},
eprint={2208.06366},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
configs/beitv2/beitv2_beit-base-p16_8xb256-amp-coslr-1600e_in1k.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../_base_/datasets/imagenet_bs256_beitv2.py',
'../_base_/default_runtime.py',
]
# model settings
vqkd_encoder = dict(
arch='base',
img_size=224,
patch_size=16,
in_channels=3,
out_indices=-1,
drop_rate=0.,
drop_path_rate=0.,
norm_cfg=dict(type='LN', eps=1e-6),
final_norm=True,
out_type='featmap',
with_cls_token=True,
frozen_stages=-1,
use_abs_pos_emb=True,
use_rel_pos_bias=False,
use_shared_rel_pos_bias=False,
layer_scale_init_value=0.,
interpolate_mode='bicubic',
patch_cfg=dict(),
layer_cfgs=dict(),
init_cfg=None)
layer_scale_init_value = 0.1
drop_path_rate = 0.1 # 0. for 300 epochs and 0.1 for 1600 epochs.
model = dict(
type='BEiT',
backbone=dict(
type='BEiTPretrainViT',
arch='base',
patch_size=16,
out_indices=[-4, -1],
drop_path_rate=drop_path_rate,
final_norm=False,
out_type='raw',
layer_scale_init_value=layer_scale_init_value,
init_cfg=[
dict(type='TruncNormal', std=0.02, layer='Linear'),
dict(type='TruncNormal', std=0.02, layer='Conv2d'),
dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
]),
neck=dict(
type='BEiTV2Neck',
num_layers=2,
early_layers=9,
backbone_arch='base',
drop_path_rate=drop_path_rate,
layer_scale_init_value=layer_scale_init_value,
),
head=dict(
type='BEiTV2Head',
embed_dims=768,
num_embed=8192,
loss=dict(type='CrossEntropyLoss')),
target_generator=dict(
type='VQKD',
encoder_config=vqkd_encoder,
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa
'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/vqkd_encoder.pth' # noqa
)))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
# betas: (0.9, 0.98) for 300 epochs and (0.9, 0.999) for 1600 epochs.
optimizer=dict(
type='AdamW', lr=1.5e-3, betas=(0.9, 0.999), weight_decay=0.05),
clip_grad=dict(max_norm=3.0),
paramwise_cfg=dict(
custom_keys={
# the following configurations are designed for BEiT
'.ln': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'q_bias': dict(decay_mult=0.0),
'v_bias': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0),
'.gamma': dict(decay_mult=0.0),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=10,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
eta_min=1e-5,
by_epoch=True,
begin=10,
end=1600,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1600)
default_hooks = dict(
# only keeps the latest 3 checkpoints
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
randomness = dict(seed=0, diff_rank_seed=True)
find_unused_parameters = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=2048)
configs/beitv2/beitv2_beit-base-p16_8xb256-amp-coslr-300e_in1k.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../_base_/datasets/imagenet_bs256_beitv2.py',
'../_base_/default_runtime.py',
]
# model settings
vqkd_encoder = dict(
arch='base',
img_size=224,
patch_size=16,
in_channels=3,
out_indices=-1,
drop_rate=0.,
drop_path_rate=0.,
norm_cfg=dict(type='LN', eps=1e-6),
final_norm=True,
out_type='featmap',
with_cls_token=True,
frozen_stages=-1,
use_abs_pos_emb=True,
use_rel_pos_bias=False,
use_shared_rel_pos_bias=False,
layer_scale_init_value=0.,
interpolate_mode='bicubic',
patch_cfg=dict(),
layer_cfgs=dict(),
init_cfg=None)
layer_scale_init_value = 0.1
drop_path_rate = 0. # 0. for 300 epochs and 0.1 for 1600 epochs.
model = dict(
type='BEiT',
backbone=dict(
type='BEiTPretrainViT',
arch='base',
patch_size=16,
out_indices=[-4, -1],
drop_path_rate=drop_path_rate,
final_norm=False,
out_type='raw',
layer_scale_init_value=layer_scale_init_value,
init_cfg=[
dict(type='TruncNormal', std=0.02, layer='Linear'),
dict(type='TruncNormal', std=0.02, layer='Conv2d'),
dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
]),
neck=dict(
type='BEiTV2Neck',
num_layers=2,
early_layers=9,
backbone_arch='base',
drop_path_rate=drop_path_rate,
layer_scale_init_value=layer_scale_init_value,
),
head=dict(
type='BEiTV2Head',
embed_dims=768,
num_embed=8192,
loss=dict(type='CrossEntropyLoss')),
target_generator=dict(
type='VQKD',
encoder_config=vqkd_encoder,
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa
'https://download.openmmlab.com/mmselfsup/1.x/target_generator_ckpt/vqkd_encoder.pth' # noqa
)))
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
# betas: (0.9, 0.98) for 300 epochs and (0.9, 0.999) for 1600 epochs.
optimizer=dict(
type='AdamW', lr=1.5e-3, betas=(0.9, 0.98), weight_decay=0.05),
clip_grad=dict(max_norm=3.0),
paramwise_cfg=dict(
custom_keys={
# the following configurations are designed for BEiT
'.ln': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'q_bias': dict(decay_mult=0.0),
'v_bias': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0),
'.gamma': dict(decay_mult=0.0),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=10,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
eta_min=1e-5,
by_epoch=True,
begin=10,
end=300,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=300)
default_hooks = dict(
# only keeps the latest 3 checkpoints
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
randomness = dict(seed=0, diff_rank_seed=True)
find_unused_parameters = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=2048)
configs/beitv2/benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../../_base_/datasets/imagenet_bs64_swin_224.py',
'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../../_base_/default_runtime.py'
]
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='BEiTViT',
arch='base',
img_size=224,
patch_size=16,
# 0.2 for 1600 epochs pretrained models and 0.1 for 300 epochs.
drop_path_rate=0.1,
out_type='avg_featmap',
use_abs_pos_emb=False,
use_rel_pos_bias=True,
use_shared_rel_pos_bias=False,
init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.')),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=[dict(type='TruncNormal', layer='Linear', std=0.02)]),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=224,
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=[104, 116, 124], interpolation='bicubic')),
dict(
type='RandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=0.3333333333333333,
fill_color=[103.53, 116.28, 123.675],
fill_std=[57.375, 57.12, 58.395]),
dict(type='PackInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=256,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs')
]
train_dataloader = dict(batch_size=128, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(batch_size=128, dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
# optimizer wrapper
optim_wrapper = dict(
optimizer=dict(
type='AdamW', lr=5e-4, weight_decay=0.05, betas=(0.9, 0.999)),
constructor='LearningRateDecayOptimWrapperConstructor',
paramwise_cfg=dict(
_delete_=True,
# 0.6 for 1600 epochs pretrained models and 0.65 for 300 epochs
layer_decay_rate=0.65,
custom_keys={
# the following configurations are designed for BEiT
'.ln': dict(decay_mult=0.0),
'.bias': dict(decay_mult=0.0),
'q_bias': dict(decay_mult=0.0),
'v_bias': dict(decay_mult=0.0),
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0),
'.gamma': dict(decay_mult=0.0),
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=20,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
by_epoch=True,
begin=20,
end=100,
eta_min=1e-6,
convert_to_iter_based=True)
]
# runtime settings
default_hooks = dict(
# save checkpoint per epoch.
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=2))
train_cfg = dict(by_epoch=True, max_epochs=100)
randomness = dict(seed=0)
configs/beitv2/benchmarks/beit-base-p16_8xb64_in1k.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../../_base_/datasets/imagenet_bs64_swin_224.py',
'../../_base_/schedules/imagenet_bs1024_adamw_swin.py',
'../../_base_/default_runtime.py'
]
model = dict(
type='ImageClassifier',
backbone=dict(
type='BEiTViT',
arch='base',
img_size=224,
patch_size=16,
out_type='avg_featmap',
use_abs_pos_emb=False,
use_rel_pos_bias=True,
use_shared_rel_pos_bias=False,
),
neck=None,
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=.02),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]))
configs/beitv2/metafile.yml 0 → 100644
View file @ 495d9ed9
Collections:
- Name: BEiTv2
Metadata:
Architecture:
- Attention Dropout
- Convolution
- Dense Connections
- Dropout
- GELU
- Layer Normalization
- Multi-Head Attention
- Scaled Dot-Product Attention
- Tanh Activation
Paper:
Title: 'BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers'
URL: https://arxiv.org/abs/2208.06366
README: configs/beitv2/README.md
Code:
URL: https://github.com/open-mmlab/mmpretrain/blob/main/mmpretrain/models/backbones/beit.py
Version: v1.0.0rc4
Models:
- Name: beitv2_beit-base-p16_8xb256-amp-coslr-300e_in1k
Metadata:
Epochs: 300
Batch Size: 2048
FLOPs: 17581223424
Parameters: 192811376
Training Data: ImageNet-1k
In Collection: BEiTv2
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/beitv2/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221212-a157be30.pth
Config: configs/beitv2/beitv2_beit-base-p16_8xb256-amp-coslr-300e_in1k.py
Downstream:
- beit-base-p16_beitv2-pre_8xb128-coslr-100e_in1k
- Name: beit-base-p16_beitv2-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581219584
Parameters: 86530984
Training Data: ImageNet-1k
In Collection: BEiTv2
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.0
Weights: https://download.openmmlab.com/mmselfsup/1.x/beitv2/beitv2_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20221212-d1c0789e.pth
Config: configs/beitv2/benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py
- Name: beit-base-p16_beitv2-in21k-pre_3rdparty_in1k
Metadata:
FLOPs: 17581219584
Parameters: 86530984
Training Data:
- ImageNet-21k
- ImageNet-1k
In Collection: BEiTv2
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 86.47
Top 5 Accuracy: 97.99
Weights: https://download.openmmlab.com/mmclassification/v0/beit/beitv2-base_3rdparty_in1k_20221114-73e11905.pth
Config: configs/beitv2/benchmarks/beit-base-p16_8xb64_in1k.py
Converted From:
Weights: https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21kto1k.pth
Code: https://github.com/microsoft/unilm/tree/master/beit2
configs/blip/README.md 0 → 100644
View file @ 495d9ed9
# BLIP
> [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086)
<!-- [ALGORITHM] -->
## Abstract
Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner.
<div align=center>
<img src="https://user-images.githubusercontent.com/26739999/236374275-94d2f94b-d9a7-4f12-b694-f15a2be00be6.png" width="90%"/>
</div>
## How to use it?
<!-- [TABS-BEGIN] -->
**Use the model**
```python
from mmpretrain import inference_model
result = inference_model('blip-base_3rdparty_caption', 'demo/cat-dog.png')
print(result)
# {'pred_caption': 'a puppy and a cat sitting on a blanket'}
```
**Test Command**
Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
Test:
```shell
python tools/test.py configs/blip/blip-base_8xb32_caption.py https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-caption_20230419-a5b71af3.pth
```
<!-- [TABS-END] -->
## Models and results
### Image Caption on COCO
| Model | Params (M) | BLEU-4 | CIDER | Config | Download |
| :----------------------------- | :--------: | :----: | :----: | :------------------------------------: | :------------------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_caption`\* | 223.97 | 40.12 | 132.82 | [config](./blip-base_8xb32_caption.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-caption_20230419-a5b71af3.pth) |
### Image Caption on NoCaps
| Model | Params (M) | SPICE | CIDER | Config | Download |
| :----------------------------- | :--------: | :---: | :----: | :-----------------------------------: | :--------------------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_caption`\* | 223.97 | 14.69 | 109.12 | [config](./blip-base_8xb32_nocaps.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-caption_20230419-a5b71af3.pth) |
### Image Caption on Flickr30k
| Model | Params (M) | SPICE | CIDER | Config | Download |
| :----------------------------- | :--------: | :---: | :---: | :----------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_caption`\* | 223.97 | 15.58 | 68.89 | [config](./blip-base_8xb32_caption_flickr30k.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-caption_20230419-a5b71af3.pth) |
### Visual Grounding on RefCOCO
| Model | Params (M) | Accuracy (testA) | Accuracy (testB) | Config | Download |
| :------------------------ | :--------: | :--------------: | :--------------: | :----------------------------------: | :-----------------------------------------------------------------------------------------------: |
| `blip-base_8xb16_refcoco` | 498.49 | 86.14 | 77.33 | [config](blip-base_8xb16_refcoco.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_8xb16_refcoco_20230508-d2d10f4c.pth) \| [log](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_8xb16_refcoco_20230508-d2d10f4c.json) |
### Visual Question Answering on VQAv2
| Model | Params (M) | Accuracy | Config | Download |
| :------------------------- | :--------: | :------: | :--------------------------------: | :-------------------------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_vqa`\* | 361.48 | 78.20 | [config](./blip-base_8xb32_vqa.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty-capflit_vqa_20230505-81488941.pth) |
### Visual Question Answering on OK-VQA
| Model | Params (M) | Accuracy | Config | Download |
| :------------------------- | :--------: | :------: | :----------------------------------: | :-------------------------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_vqa`\* | 361.48 | 40.59# | [config](./blip-base_8xb32_okvqa.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty-capflit_vqa_20230505-81488941.pth) |
### Visual Question Answering on OCR-VQA
| Model | Params (M) | Accuracy | Config | Download |
| :------------------------- | :--------: | :------: | :-----------------------------------: | :-------------------------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_vqa`\* | 361.48 | 28.30# | [config](./blip-base_8xb32_ocrvqa.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty-capflit_vqa_20230505-81488941.pth) |
### Image-To-Text Retrieval on COCO
| Model | Params (M) | Recall@1 | Recall@5 | Config | Download |
| :------------------------------- | :--------: | :------: | :------: | :--------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_retrieval`\* | 447.49 | 82.52 | 95.34 | [config](./blip-base_8xb32_retrieval.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-retrieval_20230419-a1804d2c.pth) |
### Text-To-Image Retrieval on COCO
| Model | Params (M) | Recall@1 | Recall@5 | Config | Download |
| :------------------------------- | :--------: | :------: | :------: | :--------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_retrieval`\* | 447.49 | 64.82 | 86.28 | [config](./blip-base_8xb32_retrieval.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-retrieval_20230419-a1804d2c.pth) |
### Image-To-Text Retrieval on Flickr30k
| Model | Params (M) | Recall@1 | Recall@5 | Config | Download |
| :------------------------------- | :--------: | :------: | :------: | :------------------------------------------------: | :------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_retrieval`\* | 447.49 | 95.10# | 99.60# | [config](./blip-base_8xb32_retrieval_flickr30k.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-retrieval_20230419-a1804d2c.pth) |
### Text-To-Image Retrieval on Flickr30k
| Model | Params (M) | Recall@1 | Recall@5 | Config | Download |
| :------------------------------- | :--------: | :------: | :------: | :------------------------------------------------: | :------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_retrieval`\* | 447.49 | 85.26# | 96.58# | [config](./blip-base_8xb32_retrieval_flickr30k.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-retrieval_20230419-a1804d2c.pth) |
### NLVR on NLVR2
| Model | Params (M) | Top-1 (%) | Config | Download |
| :-------------------------- | :--------: | :-------: | :---------------------------------: | :------------------------------------------------------------------------------------------------------------: |
| `blip-base_3rdparty_nlvr`\* | 259.37 | 82.33 | [config](./blip-base_8xb32_nlvr.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_nlvr_20230427-3b14d33f.pth) |
*Models with * are converted from the [official repo](https://github.com/salesforce/LAVIS). The config files of these models are only for inference. We haven't reproduce the training results.*
*Results with # denote zero-shot evaluation. The corresponding model hasn't been finetuned on that dataset.*
## Citation
```bibtex
@inproceedings{li2022blip,
title={BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
author={Junnan Li and Dongxu Li and Caiming Xiong and Steven Hoi},
year={2022},
booktitle={ICML},
}
```
configs/blip/blip-base_8xb16_refcoco.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../_base_/datasets/refcoco.py',
'../_base_/default_runtime.py',
]
med_config = {
'architectures': ['BertModel'],
'attention_probs_dropout_prob': 0.1,
'hidden_act': 'gelu',
'hidden_dropout_prob': 0.1,
'hidden_size': 768,
'initializer_range': 0.02,
'intermediate_size': 3072,
'layer_norm_eps': 1e-12,
'max_position_embeddings': 512,
'model_type': 'bert',
'num_attention_heads': 12,
'num_hidden_layers': 12,
'pad_token_id': 0,
'add_type_embeddings': False,
'vocab_size': 30524,
'encoder_width': 768,
'add_cross_attention': True
}
model = dict(
type='BlipGrounding',
visual_encoder=dict(
type='VisionTransformer',
arch='b',
img_size=384,
patch_size=16,
out_type='raw',
),
text_encoder=dict(
type='XBertEncoder',
med_config=med_config,
),
multimodal_encoder=dict(
type='XBertEncoder',
med_config=med_config,
),
tokenizer=dict(type='BlipTokenizer', name_or_path='bert-base-uncased'),
head=dict(
type='GroundingHead',
decoder=dict(
type='XBertLMHeadDecoder',
med_config=med_config,
),
box_l1_loss_coeff=4.0,
box_giou_loss_coeff=2.0,
),
)
# schedule settings
optimizer = dict(type='AdamW', lr=1.5e-5, weight_decay=0.02)
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)
param_scheduler = [dict(type='CosineAnnealingLR', by_epoch=True)]
train_cfg = dict(by_epoch=True, max_epochs=120)
val_cfg = dict()
test_cfg = dict()
configs/blip/blip-base_8xb32_caption.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../_base_/datasets/coco_caption.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
type='BlipCaption',
vision_encoder=dict(
type='VisionTransformer',
arch='b',
img_size=384,
patch_size=16,
out_type='raw',
),
tokenizer=dict(type='BlipTokenizer', name_or_path='bert-base-uncased'),
decoder_head=dict(
type='SeqGenerationHead',
decoder=dict(
type='XBertLMHeadDecoder',
med_config=dict(
architectures=['BertModel'],
attention_probs_dropout_prob=0.1,
hidden_act='gelu',
hidden_dropout_prob=0.1,
hidden_size=768,
initializer_range=0.02,
intermediate_size=3072,
layer_norm_eps=1e-12,
max_position_embeddings=512,
model_type='bert',
num_attention_heads=12,
num_hidden_layers=12,
pad_token_id=0,
add_type_embeddings=False,
vocab_size=30524,
encoder_width=768,
add_cross_attention=True),
),
),
prompt='a picture of ',
max_txt_len=20,
)
# schedule settings
optim_wrapper = dict(optimizer=dict(type='AdamW', lr=1e-5, weight_decay=0.05))
param_scheduler = [
dict(
type='CosineAnnealingLR',
by_epoch=True,
begin=0,
end=10,
)
]
train_cfg = dict(max_epochs=10)
val_cfg = dict()
test_cfg = dict()
configs/blip/blip-base_8xb32_caption_flickr30k.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../_base_/datasets/flickr30k_caption.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
type='BlipCaption',
vision_encoder=dict(
type='VisionTransformer',
arch='b',
img_size=384,
patch_size=16,
out_type='raw',
),
tokenizer=dict(type='BlipTokenizer', name_or_path='bert-base-uncased'),
decoder_head=dict(
type='SeqGenerationHead',
decoder=dict(
type='XBertLMHeadDecoder',
med_config=dict(
architectures=['BertModel'],
attention_probs_dropout_prob=0.1,
hidden_act='gelu',
hidden_dropout_prob=0.1,
hidden_size=768,
initializer_range=0.02,
intermediate_size=3072,
layer_norm_eps=1e-12,
max_position_embeddings=512,
model_type='bert',
num_attention_heads=12,
num_hidden_layers=12,
pad_token_id=0,
add_type_embeddings=False,
vocab_size=30524,
encoder_width=768,
add_cross_attention=True),
),
),
prompt='a picture of ',
max_txt_len=20,
)
# schedule settings
optim_wrapper = dict(optimizer=dict(type='AdamW', lr=1e-5, weight_decay=0.05))
param_scheduler = [
dict(
type='CosineAnnealingLR',
by_epoch=True,
begin=0,
end=10,
)
]
train_cfg = dict(max_epochs=10)
val_cfg = dict()
test_cfg = dict()
configs/blip/blip-base_8xb32_nlvr.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../_base_/datasets/nlvr2.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
type='BlipNLVR',
vision_backbone=dict(
type='VisionTransformer',
arch='b',
img_size=384,
patch_size=16,
out_type='raw',
),
tokenizer=dict(type='BlipTokenizer', name_or_path='bert-base-uncased'),
multimodal_backbone=dict(
type='BertModel',
config=dict(
architectures=['BertModel'],
attention_probs_dropout_prob=0.1,
hidden_act='gelu',
hidden_dropout_prob=0.1,
hidden_size=768,
initializer_range=0.02,
intermediate_size=3072,
layer_norm_eps=1e-12,
max_position_embeddings=512,
model_type='bert',
num_attention_heads=12,
num_hidden_layers=12,
pad_token_id=0,
add_type_embeddings=False,
vocab_size=30524,
encoder_width=768,
add_cross_attention=True,
nlvr=True),
add_pooling_layer=False),
)
# optimizer
optimizer = dict(type='AdamW', lr=2e-5, weight_decay=0.05)
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)
param_scheduler = [
dict(
type='CosineAnnealingLR',
by_epoch=True,
begin=0,
end=10,
)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=10)
val_cfg = dict()
test_cfg = dict()
default_hooks = dict(logger=dict(interval=1))
configs/blip/blip-base_8xb32_nocaps.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../_base_/datasets/nocaps.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
type='BlipCaption',
vision_encoder=dict(
type='VisionTransformer',
arch='b',
img_size=384,
patch_size=16,
out_type='raw',
),
tokenizer=dict(type='BlipTokenizer', name_or_path='bert-base-uncased'),
decoder_head=dict(
type='SeqGenerationHead',
decoder=dict(
type='XBertLMHeadDecoder',
med_config=dict(
architectures=['BertModel'],
attention_probs_dropout_prob=0.1,
hidden_act='gelu',
hidden_dropout_prob=0.1,
hidden_size=768,
initializer_range=0.02,
intermediate_size=3072,
layer_norm_eps=1e-12,
max_position_embeddings=512,
model_type='bert',
num_attention_heads=12,
num_hidden_layers=12,
pad_token_id=0,
add_type_embeddings=False,
vocab_size=30524,
encoder_width=768,
add_cross_attention=True),
),
),
prompt='a picture of ',
max_txt_len=20,
)
val_cfg = dict()
test_cfg = dict()
configs/blip/blip-base_8xb32_ocrvqa.py 0 → 100644
View file @ 495d9ed9
_base_ = [
'../_base_/datasets/ocrvqa.py',
'../_base_/default_runtime.py',
]
# model settings
model = dict(
type='BlipVQA',
tokenizer=dict(type='BlipTokenizer', name_or_path='bert-base-uncased'),
vision_backbone=dict(
type='VisionTransformer',
arch='b',
img_size=480,
patch_size=16,
out_type='raw'),
multimodal_backbone=dict(
type='XBertEncoder',
med_config=dict(
architectures=['BertModel'],
attention_probs_dropout_prob=0.1,
hidden_act='gelu',
hidden_dropout_prob=0.1,
hidden_size=768,
initializer_range=0.02,
intermediate_size=3072,
layer_norm_eps=1e-12,
max_position_embeddings=512,
model_type='bert',
num_attention_heads=12,
num_hidden_layers=12,
pad_token_id=0,
add_type_embeddings=False,
vocab_size=30524,
encoder_width=768,
add_cross_attention=True),
),
head=dict(
type='VQAGenerationHead',
decoder=dict(
type='XBertLMHeadDecoder',
med_config=dict(
architectures=['BertModel'],
attention_probs_dropout_prob=0.1,
hidden_act='gelu',
hidden_dropout_prob=0.1,
hidden_size=768,
initializer_range=0.02,
intermediate_size=3072,
layer_norm_eps=1e-12,
max_position_embeddings=512,
model_type='bert',
num_attention_heads=12,
num_hidden_layers=12,
pad_token_id=0,
add_type_embeddings=False,
vocab_size=30524,
encoder_width=768,
add_cross_attention=True),
),
inference_method='generate',
),
)
# schedule settings
optimizer = dict(type='AdamW', lr=2e-5, weight_decay=0.05)
optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer)
param_scheduler = [dict(type='CosineAnnealingLR', by_epoch=True)]
train_cfg = dict(max_epochs=10, by_epoch=True)
val_cfg = dict()
test_cfg = dict()
# runtime settings
randomness = dict(seed=42)
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