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Ezra-Yu committed Apr 13, 2023
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12 changes: 7 additions & 5 deletions configs/glip/README.md
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Expand Up @@ -21,7 +21,7 @@ This paper presents a grounded language-image pre-training (GLIP) model for lear
```python
import torch
from mmpretrain import get_model
model = get_model('vit-t_glip-pre_3rdparty', pretrained=True)
model = get_model('swin-t_glip-pre_3rdparty', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
Expand All @@ -30,16 +30,18 @@ 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 (`vit-t_glip-pre_3rdparty`)\* | O365,GoldG,CC3M,SBU | 224x224 | [model](https://download.openmmlab.com/mmclassification/v1/glip/vit-t_glip-pre_3rdparty_merged_20230411-d85813b5.pth) |
| GLIP-L (`vit-l_glip-pre_3rdparty_384px`)\* | FourODs,GoldG,CC3M+12M,SBU | 384x384 | [model](https://download.openmmlab.com/mmclassification/v1/glip/vit-l_glip-pre_3rdparty_merged-384px_20230412-04b198e8.pth) |
| 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).*

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2 changes: 1 addition & 1 deletion configs/glip/glip-l_headless.py
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Expand Up @@ -4,12 +4,12 @@
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(
num_classes=1000,
# RGB format normalization parameters
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
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7 changes: 6 additions & 1 deletion configs/glip/glip-t_headless.py
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@@ -1,6 +1,11 @@
model = dict(
type='ImageClassifier',
backbone=dict(type='SwinTransformer', arch='tiny', img_size=224),
backbone=dict(
type='SwinTransformer',
arch='tiny',
img_size=224,
out_indices=(1, 2, 3), # original weight is for detection
),
neck=None,
head=None)

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8 changes: 4 additions & 4 deletions configs/glip/metafile.yml
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Expand Up @@ -15,7 +15,7 @@ Collections:
Version: v1.0.0rc8

Models:
- Name: vit-t_glip-pre_3rdparty
- Name: swin-t_glip-pre_3rdparty
In Collection: GLIP
Metadata:
FLOPs: 4508464128
Expand All @@ -26,12 +26,12 @@ Models:
- CC3M
- SBU
Results: null
Weights: https://download.openmmlab.com/mmclassification/v1/glip/vit-t_glip-pre_3rdparty_merged_20230411-d85813b5.pth
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: vit-l_glip-pre_3rdparty_384px
- Name: swin-l_glip-pre_3rdparty_384px
In Collection: GLIP
Metadata:
FLOPs: 104080343040
Expand All @@ -42,7 +42,7 @@ Models:
- CC3M+12M
- SBU
Results: null
Weights: https://download.openmmlab.com/mmclassification/v1/glip/vit-l_glip-pre_3rdparty_merged-384px_20230412-04b198e8.pth
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
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