- High Performance. SotA (or competitive) performance on 160 datasets with only one model.
- Perception in the Wild. Detect and segment everything with thousands of vocabularies or language descriptions all at once.
- Flexible. Support both foreground objects and background stuff for instance segmentation and semantic segmentation.
- Release inference code and demo.
- Release checkpoints.
- Release training codes.
- Add clean docs.
- Clone the APE repository from GitHub:
git clone https://github.com/shenyunhang/APE
cd APE- Install the required dependencies and APE:
pip3 install -r requirements.txt
python3 -m pip install -e .Web UI demo
pip3 install gradio
cd APE/demo
python3 app.py
If you have GPUs, this demo will detect them and use one GPU.
Please feel free to try our Online Demo!
Following here to prepare the following datasets:
| COCO | LVIS | Objects365 | Openimages | VisualGenome | SA-1B | RefCOCO | GQA | PhraseCut | Flickr30k | ODinW | SegInW | Roboflow100 | ADE20k | ADE-full | BDD10k | Cityscapes | PC459 | PC59 | VOC | D3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Train | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Test | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Noted we do not use coco_2017_train for training.
Instead, we augment lvis_v1_train with annotations from coco, and keep the image set unchanged.
And we register it as lvis_v1_train+coco for instance segmentation and lvis_v1_train+coco_panoptic_separated for panoptic segmentation.
We provide several scripts to evaluate all models.
It is necessary to adjust the checkpoint location and GPU number in the scripts before running them.
scripts/eval_all_D.sh
scripts/eval_all_C.sh
scripts/eval_all_B.sh
scripts/eval_all_A.shAPE-D
python3.9 demo/demo_lazy.py \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k.py \
--input image1.jpg image2.jpg image3.jpg \
--output /path/to/output/dir \
--confidence-threshold 0.1 \
--text-prompt 'person,car,chess piece of horse head' \
--with-box \
--with-mask \
--with-sseg \
--opts \
train.init_checkpoint=/path/to/APE-D/checkpoint \
model.model_vision.select_box_nums_for_evaluation=500 \
model.model_vision.text_feature_bank_reset=True \
git lfs install
git clone https://huggingface.co/QuanSun/EVA-CLIP models/QuanSun/EVA-CLIP/
git clone https://huggingface.co/BAAI/EVA models/BAAI/EVA/
git clone https://huggingface.co/Yuxin-CV/EVA-02 models/Yuxin-CV/EVA-02/Resize patch size:
python3.9 tools/eva_interpolate_patch_14to16.py --input models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt --output models/QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14to16_plus_s9B.pt --image_size 224
python3.9 tools/eva_interpolate_patch_14to16.py --input models/QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt --output models/QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14to16_s11B.pt --image_size 224
python3.9 tools/eva_interpolate_patch_14to16.py --input models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt --output models/QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14to16_s6B.pt --image_size 336Single node:
python3.9 tools/train_net.py \
--num-gpus 8 \
--resume \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl.py \
train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_`date +'%Y%m%d_%H%M%S'`Multiple nodes:
python3.9 tools/train_net.py \
--dist-url="tcp:https://${MASTER_IP}:${MASTER_PORT}" \
--num-gpus ${HOST_GPU_NUM} \
--num-machines ${HOST_NUM} \
--machine-rank ${INDEX} \
--resume \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl.py \
train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO_GQA_PhraseCut_Flickr30k/ape_deta/ape_deta_vitl_eva02_clip_vlf_lsj1024_cp_16x4_1080k_mdl_`date +'%Y%m%d_%H'`0000Single node:
python3.9 tools/train_net.py \
--num-gpus 8 \
--resume \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py \
train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k_`date +'%Y%m%d_%H%M%S'`Multiple nodes:
python3.9 tools/train_net.py \
--dist-url="tcp:https://${MASTER_IP}:${MASTER_PORT}" \
--num-gpus ${HOST_GPU_NUM} \
--num-machines ${HOST_NUM} \
--machine-rank ${INDEX} \
--resume \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py \
train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_SA1B_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k_`date +'%Y%m%d_%H'`0000Single node:
python3.9 tools/train_net.py \
--num-gpus 8 \
--resume \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py \
train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k_`date +'%Y%m%d_%H%M%S'`Multiple nodes:
python3.9 tools/train_net.py \
--dist-url="tcp:https://${MASTER_IP}:${MASTER_PORT}" \
--num-gpus ${HOST_GPU_NUM} \
--num-machines ${HOST_NUM} \
--machine-rank ${INDEX} \
--resume \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k.py \
train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VGR_REFCOCO/ape_deta/ape_deta_vitl_eva02_vlf_lsj1024_cp_1080k_`date +'%Y%m%d_%H'`0000Single node:
python3.9 tools/train_net.py \
--num-gpus 8 \
--resume \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py \
train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k_`date +'%Y%m%d_%H%M%S'`Multiple nodes:
python3.9 tools/train_net.py \
--dist-url="tcp:https://${MASTER_IP}:${MASTER_PORT}" \
--num-gpus ${HOST_GPU_NUM} \
--num-machines ${HOST_NUM} \
--machine-rank ${INDEX} \
--resume \
--config-file configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k.py \
train.output_dir=output/APE/configs/LVISCOCOCOCOSTUFF_O365_OID_VG/ape_deta/ape_deta_vitl_eva02_lsj1024_cp_720k_`date +'%Y%m%d_%H'`0000git lfs install
git clone https://huggingface.co/shenyunhang/APE
| name | Checkpoint | Config | |
|---|---|---|---|
| 1 | APE-A | HF link | link |
| 2 | APE-B | HF link | link |
| 3 | APE-C | HF link | link |
| 4 | APE-D | HF link | link |
If you find our work helpful for your research, please consider citing the following BibTeX entry.
@article{shen2023aligning,
title={Aligning and Prompting Everything All at Once for Universal Visual Perception},
author={Yunhang Shen and Chaoyou Fu and Peixian Chen and Mengdan Zhang and Ke Li and Xing Sun and Yunsheng Wu and Shaohui Lin and Rongrong Ji},
journal={arXiv preprint arXiv:2312.02153},
year={2023}
}