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segmentation/README.md

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+# FlashInternImage for Semantic Segmentation
+
+This folder contains the implementation of the InternImage for semantic segmentation. 
+
+Our segmentation code is developed on top of [MMSegmentation v0.27.0](https://github.com/open-mmlab/mmsegmentation/tree/v0.27.0).
+
+## Usage
+
+### Install
+
+- Clone this repo:
+
+```bash
+git clone https://github.com/OpenGVLab/DCNv4.git
+cd DCNv4
+```
+
+- Create a conda virtual environment and activate it:
+
+```bash
+conda create -n dcnv4 python=3.7 -y
+conda activate dcnv4
+```
+
+- Install `CUDA>=10.2` with `cudnn>=7` following
+  the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
+- Install `PyTorch>=1.10.0` and `torchvision>=0.9.0` with `CUDA>=10.2`:
+
+For examples, to install torch==1.11 with CUDA==11.3 and nvcc:
+```bash
+conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch -y
+conda install -c conda-forge cudatoolkit-dev=11.3 -y # to install nvcc
+```
+
+- Install other requirements:
+
+  note: conda opencv will break torchvision as not to support GPU, so we need to install opencv using pip. 	  
+
+```bash
+conda install -c conda-forge termcolor yacs pyyaml scipy pip -y
+pip install opencv-python
+```
+
+- Install `timm` and `mmcv-full` and `mmsegmentation':
+
+```bash
+pip install -U openmim
+mim install mmcv-full==1.5.0
+mim install mmsegmentation==0.27.0
+pip install timm==0.6.11 mmdet==2.28.1
+```
+
+- Install DCNv4
+```bash
+pip install DCNv4
+```
+
+### Data Preparation
+
+Prepare datasets according to the [guidelines](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#prepare-datasets) in MMSegmentation.
+
+
+### Evaluation
+
+To evaluate our `FlashInternImage` on ADE20K val, run:
+
+```bash
+sh dist_test.sh <config-file> <checkpoint> <gpu-num> --eval mIoU
+```
+You can download checkpoint files from [here](https://huggingface.co/OpenGVLab/DCNv4). Then place it to segmentation/checkpoint_dir/seg.
+
+For example, to evaluate the `FlashInternImage-T` with a single GPU:
+
+```bash
+python test.py configs/ade20k/upernet_flash_internimage_t_512_160k_ade20k.py checkpoint_dir/seg/upernet_flash_internimage_t_512_160k_ade20k.pth --eval mIoU
+```
+
+For example, to evaluate the `FlashInternImage-B` with a single node with 8 GPUs:
+
+```bash
+sh dist_test.sh configs/ade20k/upernet_flash_internimage_b_512_160k_ade20k.py checkpoint_dir/seg/upernet_flash_internimage_b_512_160k_ade20k.pth 8 --eval mIoU
+```
+
+### Training
+
+To train an `FlashInternImage` on ADE20K, run:
+
+```bash
+sh dist_train.sh <config-file> <gpu-num>
+```
+
+For example, to train `FlashInternImage-T` with 8 GPU on 1 node (total batch size 16), run:
+
+```bash
+sh dist_train.sh configs/ade20k/upernet_flash_internimage_t_512_160k_ade20k.py 8
+```