diff --git a/README.md b/README.md
index a71e22cc..d90bacd2 100644
--- a/README.md
+++ b/README.md
@@ -200,6 +200,7 @@ Results and models are available in the [model zoo](https://mmpretrain.readthedo
<li><a href="configs/xcit">XCiT</a></li>
<li><a href="configs/levit">LeViT</a></li>
<li><a href="configs/riformer">RIFormer</a></li>
+ <li><a href="configs/glip">GLIP</a></li>
</ul>
</td>
<td>
diff --git a/README_zh-CN.md b/README_zh-CN.md
index 7caf6c03..b8d41714 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -196,6 +196,7 @@ mim install -e .
<li><a href="configs/xcit">XCiT</a></li>
<li><a href="configs/levit">LeViT</a></li>
<li><a href="configs/riformer">RIFormer</a></li>
+ <li><a href="configs/glip">GLIP</a></li>
</ul>
</td>
<td>
diff --git a/configs/glip/README.md b/configs/glip/README.md
new file mode 100644
index 00000000..48ee3056
--- /dev/null
+++ b/configs/glip/README.md
@@ -0,0 +1,57 @@
+# GLIP
+
+> [Grounded Language-Image Pre-training](https://arxiv.org/abs/2112.03857)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich. In our experiments, we pre-train GLIP on 27M grounding data, including 3M human-annotated and 24M web-crawled image-text pairs. The learned representations demonstrate strong zero-shot and few-shot transferability to various object-level recognition tasks. 1) When directly evaluated on COCO and LVIS (without seeing any images in COCO during pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many supervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13 downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head.
+
+<div align="center">
+<img src="https://github.com/microsoft/GLIP/blob/main/docs/lead.png" width="70%"/>
+</div>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Use the model**
+
+```python
+import torch
+from mmpretrain import get_model
+model = get_model('swin-t_glip-pre_3rdparty', 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))
+```
+
+<!-- [TABS-END] -->
+
+## Results and models
+
+### Pre-trained models
+
+The pre-trained models are used to fine-tune, and therefore don't have evaluation results.
+
+| Model | Pretrain | resolution | Download |
+| :------------------------------------------ | :------------------------: | :--------: | :-------------------------------------------------------------------------------------------------------------------: |
+| GLIP-T (`swin-t_glip-pre_3rdparty`)\* | O365,GoldG,CC3M,SBU | 224x224 | [model](https://download.openmmlab.com/mmclassification/v1/glip/swin-t_glip-pre_3rdparty_20230413-d85813b5.pth) |
+| GLIP-L (`swin-l_glip-pre_3rdparty_384px`)\* | FourODs,GoldG,CC3M+12M,SBU | 384x384 | [model](https://download.openmmlab.com/mmclassification/v1/glip/swin-l_glip-pre_3rdparty_384px_20230413-04b198e8.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/microsoft/GLIP).*
+
+## Citation
+
+```bibtex
+@inproceedings{li2021grounded,
+ title={Grounded Language-Image Pre-training},
+ author={Liunian Harold Li* and Pengchuan Zhang* and Haotian Zhang* and Jianwei Yang and Chunyuan Li and Yiwu Zhong and Lijuan Wang and Lu Yuan and Lei Zhang and Jenq-Neng Hwang and Kai-Wei Chang and Jianfeng Gao},
+ year={2022},
+ booktitle={CVPR},
+}
+```
diff --git a/configs/glip/glip-l_headless.py b/configs/glip/glip-l_headless.py
new file mode 100644
index 00000000..991b6b85
--- /dev/null
+++ b/configs/glip/glip-l_headless.py
@@ -0,0 +1,18 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='SwinTransformer',
+ arch='large',
+ img_size=384,
+ out_indices=(1, 2, 3), # original weight is for detection
+ stage_cfgs=dict(block_cfgs=dict(window_size=12))),
+ neck=None,
+ head=None)
+
+data_preprocessor = dict(
+ # RGB format normalization parameters
+ mean=[103.53, 116.28, 123.675],
+ std=[57.375, 57.12, 58.395],
+ # convert image from BGR to RGB
+ to_rgb=False,
+)
diff --git a/configs/glip/glip-t_headless.py b/configs/glip/glip-t_headless.py
new file mode 100644
index 00000000..08b89f8f
--- /dev/null
+++ b/configs/glip/glip-t_headless.py
@@ -0,0 +1,18 @@
+model = dict(
+ type='ImageClassifier',
+ backbone=dict(
+ type='SwinTransformer',
+ arch='tiny',
+ img_size=224,
+ out_indices=(1, 2, 3), # original weight is for detection
+ ),
+ neck=None,
+ head=None)
+
+data_preprocessor = dict(
+ # RGB format normalization parameters
+ mean=[103.53, 116.28, 123.675],
+ std=[57.375, 57.12, 58.395],
+ # convert image from BGR to RGB
+ to_rgb=False,
+)
diff --git a/configs/glip/metafile.yml b/configs/glip/metafile.yml
new file mode 100644
index 00000000..0691fd0d
--- /dev/null
+++ b/configs/glip/metafile.yml
@@ -0,0 +1,49 @@
+Collections:
+ - Name: GLIP
+ Metadata:
+ Training Techniques:
+ - AdamW
+ - Weight Decay
+ Architecture:
+ - Shift Window Multihead Self Attention
+ Paper:
+ URL: https://arxiv.org/abs/2112.03857
+ Title: "Grounded Language-Image Pre-training"
+ README: configs/glip/README.md
+ Code:
+ URL: https://github.com/open-mmlab/mmpretrain/blob/main/mmpretrain/models/backbones/vit.py
+ Version: v1.0.0rc8
+
+Models:
+ - Name: swin-t_glip-pre_3rdparty
+ In Collection: GLIP
+ Metadata:
+ FLOPs: 4508464128
+ Parameters: 29056354
+ Training Data:
+ - O365
+ - GoldG
+ - CC3M
+ - SBU
+ Results: null
+ Weights: https://download.openmmlab.com/mmclassification/v1/glip/swin-t_glip-pre_3rdparty_20230413-d85813b5.pth
+ Converted From:
+ Weights: https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/glip_tiny_model_o365_goldg_cc_sbu.pth
+ Code: https://github.com/microsoft/GLIP
+ Config: configs/glip/glip-t_headless.py
+ - Name: swin-l_glip-pre_3rdparty_384px
+ In Collection: GLIP
+ Metadata:
+ FLOPs: 104080343040
+ Parameters: 196735516
+ Training Data:
+ - FourODs
+ - GoldG
+ - CC3M+12M
+ - SBU
+ Results: null
+ Weights: https://download.openmmlab.com/mmclassification/v1/glip/swin-l_glip-pre_3rdparty_384px_20230413-04b198e8.pth
+ Converted From:
+ Weights: https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/glip_large_model.pth
+ Code: https://github.com/microsoft/GLIP
+ Config: configs/glip/glip-l_headless.py
diff --git a/model-index.yml b/model-index.yml
index 3f2677a2..bc8bb137 100644
--- a/model-index.yml
+++ b/model-index.yml
@@ -68,3 +68,4 @@ Import:
- configs/milan/metafile.yml
- configs/riformer/metafile.yml
- configs/sam/metafile.yml
+ - configs/glip/metafile.yml
diff --git a/tools/model_converters/glip_to_mmpretrain.py b/tools/model_converters/glip_to_mmpretrain.py
new file mode 100644
index 00000000..91f39620
--- /dev/null
+++ b/tools/model_converters/glip_to_mmpretrain.py
@@ -0,0 +1,76 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+from collections import OrderedDict
+
+import mmengine
+import torch
+from mmengine.runner import CheckpointLoader
+
+
+def convert_glip(ckpt):
+
+ def correct_unfold_reduction_order(x):
+ out_channel, in_channel = x.shape
+ x = x.reshape(out_channel, 4, in_channel // 4)
+ x = x[:, [0, 2, 1, 3], :].transpose(1,
+ 2).reshape(out_channel, in_channel)
+ return x
+
+ def correct_unfold_norm_order(x):
+ in_channel = x.shape[0]
+ x = x.reshape(4, in_channel // 4)
+ x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel)
+ return x
+
+ new_ckpt = OrderedDict()
+
+ for k, v in list(ckpt.items()):
+ if 'language_backbone' in k or 'backbone' not in k or 'fpn' in k:
+ continue
+ new_v = v
+ new_k = k.replace('body.', '')
+ new_k = new_k.replace('module.', '')
+ if new_k.startswith('backbone.layers'):
+ new_k = new_k.replace('backbone.layers', 'backbone.stages')
+ if 'mlp' in new_k:
+ new_k = new_k.replace('mlp.fc1', 'ffn.layers.0.0')
+ new_k = new_k.replace('mlp.fc2', 'ffn.layers.1')
+ elif 'attn' in new_k:
+ new_k = new_k.replace('attn', 'attn.w_msa')
+ elif 'patch_embed' in k:
+ new_k = new_k.replace('proj', 'projection')
+ elif 'downsample' in new_k:
+ if 'reduction.' in k:
+ new_v = correct_unfold_reduction_order(new_v)
+ elif 'norm.' in k:
+ new_v = correct_unfold_norm_order(new_v)
+
+ new_ckpt[new_k] = new_v
+ return new_ckpt
+
+
+def main():
+ parser = argparse.ArgumentParser(
+ description='Convert keys in pretrained glip models to mmcls style.')
+ parser.add_argument('src', help='src model path or url')
+ # The dst path must be a full path of the new checkpoint.
+ parser.add_argument('dst', help='save path')
+ args = parser.parse_args()
+
+ checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')
+
+ if 'model' in checkpoint:
+ state_dict = checkpoint['model']
+ else:
+ state_dict = checkpoint
+
+ weight = convert_glip(state_dict)
+ mmengine.mkdir_or_exist(osp.dirname(args.dst))
+ torch.save(weight, args.dst)
+
+ print('Done!!')
+
+
+if __name__ == '__main__':
+ main()