rmaphoh
commited on
Commit
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8943654
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Parent(s):
2ac90ad
v1.0
Browse files- README.md +10 -12
- requirement.txt +4 -1
README.md
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@@ -19,21 +19,17 @@ Keras version implemented by Yuka Kihara can be found [here](https://github.com/
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- A [visualisation demo](https://github.com/rmaphoh/RETFound_MAE/blob/main/RETFound_visualize.ipynb) is added
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### Install
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Create
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```
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conda create -n retfound python=3.7.5 -y
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conda activate retfound
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```
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```
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pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
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```
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Install others
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```
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git clone https://github.com/rmaphoh/RETFound_MAE/
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cd RETFound_MAE
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@@ -43,7 +39,9 @@ pip install -r requirement.txt
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### Fine-tuning with RETFound weights
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-
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<table><tbody>
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<!-- START TABLE -->
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<!-- TABLE HEADER -->
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</tr>
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</tbody></table>
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-
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<p align="left">
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<img src="./pic/file_index.jpg" width="160">
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</p>
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-
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```
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@@ -85,7 +83,7 @@ python -m torch.distributed.launch --nproc_per_node=1 --master_port=48798 main_f
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```
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-
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```
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@@ -106,7 +104,7 @@ python -m torch.distributed.launch --nproc_per_node=1 --master_port=48798 main_f
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### Load the model and weights (if you want to call the model in your code)
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```
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import torch
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import models_vit
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from util.pos_embed import interpolate_pos_embed
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- A [visualisation demo](https://github.com/rmaphoh/RETFound_MAE/blob/main/RETFound_visualize.ipynb) is added
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### Install environment
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1. Create environment with conda:
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```
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conda create -n retfound python=3.7.5 -y
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conda activate retfound
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```
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2. Install dependencies
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```
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git clone https://github.com/rmaphoh/RETFound_MAE/
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cd RETFound_MAE
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### Fine-tuning with RETFound weights
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To fine tune RETFound on your own data, follow these steps:
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1. Download the RETFound pre-trained weights
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<table><tbody>
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<!-- START TABLE -->
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<!-- TABLE HEADER -->
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</tr>
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</tbody></table>
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2. Organise your data into this directory structure (using IDRiD as an [example](Example.ipynb))
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<p align="left">
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<img src="./pic/file_index.jpg" width="160">
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</p>
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3. Start fine-tuning (use IDRiD as example). A fine-tuned checkpoint will be saved during training. Evaluation will be run after training.
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```
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```
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4. For evaluation only
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```
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### Load the model and weights (if you want to call the model in your code)
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```python
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import torch
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import models_vit
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from util.pos_embed import interpolate_pos_embed
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requirement.txt
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opencv-python==4.5.3.56
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pandas==0.25.3
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Pillow==8.3.1
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tensorboard-plugin-wit==1.8.0
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timm==0.3.2
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tqdm==4.62.1
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-
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--find-links https://download.pytorch.org/whl/torch_stable.html
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torch==1.8.1+cu111
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torchvision==0.9.1+cu111
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torchaudio==0.8.1
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opencv-python==4.5.3.56
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pandas==0.25.3
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Pillow==8.3.1
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tensorboard-plugin-wit==1.8.0
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timm==0.3.2
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tqdm==4.62.1
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