| # InternViT-6B for Semantic Segmentation |
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| This folder contains the implementation of the InternViT-6B for semantic segmentation. |
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| Our segmentation code is developed on top of [MMSegmentation v0.30.0](https://github.com/open-mmlab/mmsegmentation/tree/v0.30.0). |
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| ## 🛠️ Installation |
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| See [INSTALLATION.md](../INSTALLATION.md) |
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| ## 📦 Data Preparation |
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| Prepare the ADE20K dataset according to the [guidelines](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#prepare-datasets) in MMSegmentation. |
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| ## 📦 Model Preparation |
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| | model name | type | download | size | |
| | ------------------ | ------- | ----------------------------------------------------------------------------------------- | :---: | |
| | InternViT-6B-224px | pytorch | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL/blob/main/intern_vit_6b_224px.pth) | 12 GB | |
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| Please download the above model weight and place it in the `pretrained/` folder. |
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| The directory structure is: |
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| ```sh |
| pretrained |
| └── intern_vit_6b_224px.pth |
| ``` |
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| ## 🔥 Training |
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| > Please note, this open-source code does not include DeepSpeed in MMSegmentation, so it currently only supports training for linear probing and head tuning, and does not support full-parameter training. |
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| To train a linear classifier for `InternViT-6B` with 8 GPU on 1 node (total batch size 16), run: |
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| ```bash |
| sh dist_train.sh configs/intern_vit_6b/linear_probing/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py 8 |
| # or manage jobs with slurm |
| GPUS=8 sh slurm_train.sh <partition> <job-name> configs/intern_vit_6b/linear_probing/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py |
| ``` |
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| ## 📊 Evaluation |
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| | type | backbone | head | mIoU | config | download | |
| | --------------- | --------------------- | :-----: | :--: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | |
| | few-shot (1/16) | InternViT-6B | Linear | 46.5 | [config](./configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_5k_ade20k_bs16_lr4e-5_1of16.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_5k_ade20k_bs16_lr4e-5_1of16.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_5k_ade20k_bs16_lr4e-5_1of16.log) | |
| | few-shot (1/8) | InternViT-6B | Linear | 50.0 | [config](./configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_10k_ade20k_bs16_lr4e-5_1of8.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_10k_ade20k_bs16_lr4e-5_1of8.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_10k_ade20k_bs16_lr4e-5_1of8.log) | |
| | few-shot (1/4) | InternViT-6B | Linear | 53.3 | [config](./configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_20k_ade20k_bs16_lr4e-5_1of4.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_20k_ade20k_bs16_lr4e-5_1of4.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_20k_ade20k_bs16_lr4e-5_1of4.log) | |
| | few-shot (1/2) | InternViT-6B | Linear | 55.8 | [config](./configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.log) | |
| | few-shot (1/1) | InternViT-6B | Linear | 57.2 | [config](./configs/intern_vit_6b/few_shot/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_1of1.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_1of1.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_1of1.log) | |
| | linear probing | InternViT-6B (frozen) | Linear | 47.2 | [config](./configs/intern_vit_6b/linear_probing/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.log) | |
| | head tuning | InternViT-6B (frozen) | UperNet | 54.9 | [config](./configs/intern_vit_6b/head_tuning/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.log) | |
| | full tuning | InternViT-6B | UperNet | 58.9 | [config](./configs/intern_vit_6b/full_tuning/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5.py) | [ckpt](https://huggingface.co/OpenGVLab/InternVL/resolve/main/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5.pth) \| [log](https://huggingface.co/OpenGVLab/InternVL/raw/main/upernet_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5.log) | |
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| You can download checkpoints from [here](https://huggingface.co/OpenGVLab/InternVL/tree/main) or from the table above. Then place them to `segmentation/checkpoints/`. |
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| For example, to evaluate the `InternViT-6B` with a single GPU: |
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| ```bash |
| python test.py configs/intern_vit_6b/linear_probing/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py checkpoints/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.pth --eval mIoU |
| ``` |
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| For example, to evaluate the `InternViT-6B` with a single node with 8 GPUs: |
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| ```bash |
| sh dist_test.sh configs/intern_vit_6b/linear_probing/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.py checkpoints/linear_intern_vit_6b_504_80k_ade20k_bs16_lr4e-5_frozen.pth 8 --eval mIoU |
| ``` |
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