birth

detection/README.md

@@ -0,0 +1,93 @@
+# FlashInternImage for Object Detection
+
+This folder contains the implementation of the FlashInternImage 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).
+
+
+## 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:
+```bash
+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`:
+
+```bash
+pip install -U openmim
+mim install mmcv-full==1.5.0
+pip install timm==0.6.11 mmdet==2.28.1
+```
+
+- Install other requirements:
+
+```bash
+pip install opencv-python termcolor yacs pyyaml scipy
+```
+
+- Install DCNv4
+```bash
+pip install DCNv4==latest
+```
+
+
+### Data Preparation
+
+Prepare COCO according to the guidelines in [MMDetection v2.28.1](https://github.com/open-mmlab/mmdetection/resolve/master/docs/en/1_exist_data_model.md).
+
+
+### Evaluation
+
+To evaluate our `FlashInternImage` on COCO val, run:
+
+```bash
+sh dist_test.sh <config-file> <checkpoint> <gpu-num> --eval bbox segm
+```
+
+For example, to evaluate the `FlashInternImage-T` with a single GPU:
+
+```bash
+python test.py configs/coco/mask_rcnn_flash_intern_image_t_fpn_1x_coco.py checkpoint_dir/det/mask_rcnn_flash_internimage_t_fpn_1x_coco.pth --eval bbox segm
+```
+
+For example, to evaluate the `FlashInternImage-B` with a single node with 8 GPUs:
+
+```bash
+sh dist_test.sh configs/coco/mask_rcnn_flash_intern_image_b_fpn_1x_coco.py checkpoint_dir/det/mask_rcnn_flash_internimage_b_fpn_1x_coco.py 8 --eval bbox segm
+```
+
+### Training on COCO
+
+To train an `FlashInternImage` on COCO, run:
+
+```bash
+sh dist_train.sh <config-file> <gpu-num>
+```
+
+For example, to train `FlashInternImage-T` with 8 GPU on 1 node, run:
+
+```bash
+sh dist_train.sh configs/coco/mask_rcnn_flash_intern_image_t_fpn_1x_coco.py 8
+```
+