APE: Aligning and Prompting Everything All at Once for Universal Visual Perception

🍇 [Read our arXiv Paper] 🍎 [Try our Online Demo]

💡 Highlight

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.

🏷️ TODO

Release inference code and demo.
Release checkpoints.
Release training codes.
Add clean docs.

🛠️ Install

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 .

▶️ Demo Localy

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!

📚 Data Prepare

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	✓	✓	✓	✓	✗	✗	✓	✗	✗	✗	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓

🧪 Inference

Infer on 160+ dataset

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.sh

Infer on images or videos

APE-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 \

🚋 Training

Prepare backbone and language models

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 336

Train APE-D

Single 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://${MASTER_IP}:${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'`0000

Train APE-C

Single 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://${MASTER_IP}:${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'`0000

Train APE-B

Single 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://${MASTER_IP}:${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'`0000

Train APE-A

Single 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://${MASTER_IP}:${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'`0000

🧳 Checkpoints

git 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

🎖️ Results

✒️ Citation

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}
}

junbaoZHUO/APE

APE: Aligning and Prompting Everything All at Once for Universal Visual Perception

💡 Highlight

🏷️ TODO

🛠️ Install

▶️ Demo Localy

📚 Data Prepare

🧪 Inference

Infer on 160+ dataset

Infer on images or videos

🚋 Training

Prepare backbone and language models

Train APE-D

Train APE-C

Train APE-B

Train APE-A

🧳 Checkpoints

🎖️ Results

✒️ Citation