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detection/README.md
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...@@ -4,11 +4,21 @@ This folder contains the implementation of the InternImage for object detection. ...@@ -4,11 +4,21 @@ This folder contains the implementation of the InternImage for object detection.
Our detection code is developed on top of [MMDetection v2.28.1](https://github.com/open-mmlab/mmdetection/tree/v2.28.1). Our detection code is developed on top of [MMDetection v2.28.1](https://github.com/open-mmlab/mmdetection/tree/v2.28.1).
## Usage <!-- TOC -->
### Install - [Installation](#installation)
- [Data Preparation](#data-preparation)
- [Released Models](#released-models)
- [Evaluation](#evaluation)
- [Training](#training)
- [Manage Jobs with Slurm](#manage-jobs-with-slurm)
- [Export](#export)
- Clone this repo: <!-- TOC -->
## Installation
- Clone this repository:
```bash ```bash
git clone https://github.com/OpenGVLab/InternImage.git git clone https://github.com/OpenGVLab/InternImage.git
...@@ -26,17 +36,27 @@ conda activate internimage ...@@ -26,17 +36,27 @@ conda activate internimage
the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) 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`: - 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: For examples, to install `torch==1.11` with `CUDA==11.3`:
```bash ```bash
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
``` ```
- Install `timm==0.6.11` and `mmcv-full==1.5.0`: - 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`, `mmcv-full` and \`mmsegmentation':
```bash ```bash
pip install -U openmim pip install -U openmim
mim install mmcv-full==1.5.0 mim install mmcv-full==1.5.0
mim install mmsegmentation==0.27.0
pip install timm==0.6.11 mmdet==2.28.1 pip install timm==0.6.11 mmdet==2.28.1
``` ```
...@@ -44,10 +64,15 @@ pip install timm==0.6.11 mmdet==2.28.1 ...@@ -44,10 +64,15 @@ pip install timm==0.6.11 mmdet==2.28.1
```bash ```bash
pip install opencv-python termcolor yacs pyyaml scipy pip install opencv-python termcolor yacs pyyaml scipy
# Please use a version of numpy lower than 2.0
pip install numpy==1.26.4
pip install pydantic==1.10.13
``` ```
- Compile CUDA operators - Compile CUDA operators
Before compiling, please use the `nvcc -V` command to check whether your `nvcc` version matches the CUDA version of PyTorch.
```bash ```bash
cd ./ops_dcnv3 cd ./ops_dcnv3
sh ./make.sh sh ./make.sh
...@@ -55,15 +80,40 @@ sh ./make.sh ...@@ -55,15 +80,40 @@ sh ./make.sh
python test.py python test.py
``` ```
- You can also install the operator using .whl files - You can also install the operator using precompiled `.whl` files
[DCNv3-1.0-whl](https://github.com/OpenGVLab/InternImage/releases/tag/whl_files)
[DCNv3-1.0-whl](https://github.com/OpenGVLab/InternImage/releases/tag/whl_files) ## Data Preparation
### Data Preparation Prepare datasets according to the guidelines in [MMDetection v2.28.1](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/1_exist_data_model.md).
Prepare COCO according to the guidelines in [MMDetection v2.28.1](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/1_exist_data_model.md). ## Released Models
### Evaluation <details open>
<summary> Dataset: COCO </summary>
<br>
<div>
| method | backbone | schd | box mAP | mask mAP | #param | FLOPs | Config | Download |
| :--------: | :------------: | :--: | :-----: | :------: | :----: | :---: | :----------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Mask R-CNN | InternImage-T | 1x | 47.2 | 42.5 | 49M | 270G | [config](./configs/coco/mask_rcnn_internimage_t_fpn_1x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_t_fpn_1x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_t_fpn_1x_coco.log.json) |
| Mask R-CNN | InternImage-T | 3x | 49.1 | 43.7 | 49M | 270G | [config](./configs/coco/mask_rcnn_internimage_t_fpn_3x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_t_fpn_3x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_t_fpn_3x_coco.log.json) |
| Mask R-CNN | InternImage-S | 1x | 47.8 | 43.3 | 69M | 340G | [config](./configs/coco/mask_rcnn_internimage_s_fpn_1x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_s_fpn_1x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_s_fpn_1x_coco.log.json) |
| Mask R-CNN | InternImage-S | 3x | 49.7 | 44.5 | 69M | 340G | [config](./configs/coco/mask_rcnn_internimage_s_fpn_3x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_s_fpn_3x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_s_fpn_3x_coco.log.json) |
| Mask R-CNN | InternImage-B | 1x | 48.8 | 44.0 | 115M | 501G | [config](./configs/coco/mask_rcnn_internimage_b_fpn_1x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_b_fpn_1x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_b_fpn_1x_coco.log.json) |
| Mask R-CNN | InternImage-B | 3x | 50.3 | 44.8 | 115M | 501G | [config](./configs/coco/mask_rcnn_internimage_b_fpn_3x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_b_fpn_3x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask_rcnn_internimage_b_fpn_3x_coco.log.json) |
| Cascade | InternImage-L | 1x | 54.9 | 47.7 | 277M | 1399G | [config](./configs/coco/cascade_internimage_l_fpn_1x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_l_fpn_1x_coco.pth) |
| Cascade | InternImage-L | 3x | 56.1 | 48.5 | 277M | 1399G | [config](./configs/coco/cascade_internimage_l_fpn_3x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_l_fpn_3x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_l_fpn_3x_coco.log.json) |
| Cascade | InternImage-XL | 1x | 55.3 | 48.1 | 387M | 1782G | [config](./configs/coco/cascade_internimage_xl_fpn_1x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_xl_fpn_1x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_xl_fpn_1x_coco.log.json) |
| Cascade | InternImage-XL | 3x | 56.2 | 48.8 | 387M | 1782G | [config](./configs/coco/cascade_internimage_xl_fpn_3x_coco.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_xl_fpn_3x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/cascade_internimage_xl_fpn_3x_coco.log.json) |
| DINO | InternImage-T | 1x | 53.9 | - | 49M | - | [config](./configs/coco/dino_4scale_internimage_t_1x_coco_layer_wise_lr.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/dino_4scale_internimage_t_1x_coco.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/dino_4scale_internimage_t_1x_coco.json) |
| DINO | InternImage-L | 1x | 57.6 | - | 241M | - | [config](./configs/coco/dino_4scale_internimage_l_1x_coco_0.1x_backbone_lr.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/dino_4scale_internimage_l_1x_coco_0.1x_backbone_lr.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/resolve/main/dino_4scale_internimage_l_1x_coco_0.1x_backbone_lr.log.json) |
</div>
</details>
## Evaluation
To evaluate our `InternImage` on COCO val, run: To evaluate our `InternImage` on COCO val, run:
...@@ -74,16 +124,16 @@ sh dist_test.sh <config-file> <checkpoint> <gpu-num> --eval bbox segm ...@@ -74,16 +124,16 @@ sh dist_test.sh <config-file> <checkpoint> <gpu-num> --eval bbox segm
For example, to evaluate the `InternImage-T` with a single GPU: For example, to evaluate the `InternImage-T` with a single GPU:
```bash ```bash
python test.py configs/coco/mask_rcnn_internimage_t_fpn_1x_coco.py checkpoint_dir/det/mask_rcnn_internimage_t_fpn_1x_coco.pth --eval bbox segm python test.py configs/coco/mask_rcnn_internimage_t_fpn_1x_coco.py pretrained/mask_rcnn_internimage_t_fpn_1x_coco.pth --eval bbox segm
``` ```
For example, to evaluate the `InternImage-B` with a single node with 8 GPUs: For example, to evaluate the `InternImage-B` with a single node with 8 GPUs:
```bash ```bash
sh dist_test.sh configs/coco/mask_rcnn_internimage_b_fpn_1x_coco.py checkpoint_dir/det/mask_rcnn_internimage_b_fpn_1x_coco.py 8 --eval bbox segm sh dist_test.sh configs/coco/mask_rcnn_internimage_b_fpn_1x_coco.py pretrained/mask_rcnn_internimage_b_fpn_1x_coco.py 8 --eval bbox segm
``` ```
### Training on COCO ## Training
To train an `InternImage` on COCO, run: To train an `InternImage` on COCO, run:
...@@ -97,7 +147,7 @@ For example, to train `InternImage-T` with 8 GPU on 1 node, run: ...@@ -97,7 +147,7 @@ For example, to train `InternImage-T` with 8 GPU on 1 node, run:
sh dist_train.sh configs/coco/mask_rcnn_internimage_t_fpn_1x_coco.py 8 sh dist_train.sh configs/coco/mask_rcnn_internimage_t_fpn_1x_coco.py 8
``` ```
### Manage Jobs with Slurm ## Manage Jobs with Slurm
For example, to train `InternImage-L` with 32 GPU on 4 node, run: For example, to train `InternImage-L` with 32 GPU on 4 node, run:
...@@ -105,7 +155,13 @@ For example, to train `InternImage-L` with 32 GPU on 4 node, run: ...@@ -105,7 +155,13 @@ For example, to train `InternImage-L` with 32 GPU on 4 node, run:
GPUS=32 sh slurm_train.sh <partition> <job-name> configs/coco/cascade_internimage_xl_fpn_3x_coco.py work_dirs/cascade_internimage_xl_fpn_3x_coco GPUS=32 sh slurm_train.sh <partition> <job-name> configs/coco/cascade_internimage_xl_fpn_3x_coco.py work_dirs/cascade_internimage_xl_fpn_3x_coco
``` ```
### Export ## Export
Install `mmdeploy` at first:
```shell
pip install mmdeploy==0.14.0
```
To export a detection model from PyTorch to TensorRT, run: To export a detection model from PyTorch to TensorRT, run:
......
segmentation/README.md
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...@@ -151,9 +151,9 @@ Prepare datasets according to the [guidelines](https://github.com/open-mmlab/mms ...@@ -151,9 +151,9 @@ Prepare datasets according to the [guidelines](https://github.com/open-mmlab/mms
<br> <br>
<div> <div>
| method | backbone | resolution | mIoU (ss) | #params | FLOPs | Config | Download | | method | backbone | resolution | mIoU (ss/ms) | #params | FLOPs | Config | Download |
| :---------: | :-----------: | :--------: | :---------: | :-----: | :---: | :-----------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | :---------: | :-----------: | :--------: | :----------: | :-----: | :---: | :-----------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Mask2Former | InternImage-H | 512x512 | 59.2 / 59.6 | 1.28B | 1528G | [config](./configs/coco_stuff10k/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.log.json) | | Mask2Former | InternImage-H | 512x512 | 59.2 / 59.6 | 1.28B | 1528G | [config](./configs/coco_stuff10k/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.py) | [ckpt](https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.pth) \| [log](https://huggingface.co/OpenGVLab/InternImage/raw/main/mask2former_internimage_h_512_40k_cocostuff164k_to_10k.log.json) |
</div> </div>
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