Unofficial-Pix2seq: A Language Modeling Framework for Object Detection

Unofficial implementation of Pix2SEQ. Please use this code with causion. Many implemtation details are not following original paper and significantly simplified.

Please check new codebase Stable-Pix2Seq

Aim

This project aims for a step by step replication of Pix2Seq starting from DETR codebase.

Step 1

Starting from DETR, we add bounding box quantization over normalized coordinate, sequence generator from normalized coordinate, auto-regressive decoder and training code for Pix2SEQ.

How to use?

Install packages following original DETR and command line is same as DETR.

By setting image size to 512, each epoch takes 3 minutes on 8 A100 GPU.

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --coco_path ../../data/coco/

Released at 8pm, 26th, Seq

Problem to be solved : 1) better logging 2) correct padding, end of sentence, start of sentence token 3) efficient padding 4) better organization of code 5) fixed order of bounding box 6) shared dictionary between position and category

Released at 10pm, 26th, Seq

Problem to be solved: 1) better organization of code 2) fixed order of bounding box

Step 2

Finish inference code of pix2seq and report performance on object detection benchmark. Note that we are going to write an inefficent greedy decoding. The progress can be significantly accelerated by following cache previous state in Fairseq. The quality can be improved by nucleus sampling and beam search. We leave these complex but engineering tricks for future implementation and keep the project as simple as possible for understanding language modeling object detection.

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --coco_path ../../data/coco/  --eval --resume checkpoint.pth --batch_size 4

After 30 epoches training, our replication of pix2seq can achieve 12.1 mAP on MSCOCO. Image resolution 512 for fast training.

COCO bbox detection val5k evaluation with maximum 25 boundingx box predictions (Original paper 100 bounding box):

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.121
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.239
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.107
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.007
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.091
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.267
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.144
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.166
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.166
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.011
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.128
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.350

After 107 epoches training, our replication of pix2seq can achieve 17.9 mAP on MSCOCO. Image resolution 512 for fast training. Checkpoint can be downloaded at here.

COCO bbox detection val5k evaluation with maximum 25 boundingx box predictions (Original paper 100 bounding box):

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.179
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.314
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.177
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.157
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.375
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.191
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.233
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.233
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.028
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.210
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.469

Vislizization of Pix2Seq detection results(mAP 17.9) on MSCOCO.

python  predict.py --resume checkpoint.pth --img_path ./data/coco/val2017/000000157098.jpg

After 213 epoches training, our replication of pix2seq can achieve 26.4 mAP on MSCOCO. Image resolution 1333 after 150 epochs for better training. Checkpoint can be downloaded at here.

COCO bbox detection val5k evaluation with maximum 25 boundingx box predictions (Original paper 100 bounding box):

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.264
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.423
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.273
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.080
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.287
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.254
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.334
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.334
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.102
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.356
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556

Observation

(1). The sequence is tend to generate End of Sentence(EOS) early. After generating EOS token, langauge modeling will still genrate boudning box. (2). Repeatable sequence which is a common problem in seq2seq modeling. (3) end prediction when meet with 'EOS' token will generate high Precision but low recall.

High Precision, Low Recall when use EOS, 512 resolution, maxmium 20 boudning box

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.199
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.346
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.197
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.032
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.189
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.401
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.208
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.265
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.265
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.044
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.251
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.519

Low Precision, High Recall when ignore EOS, 512 resolution, maxmium 20 boudning box

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.193
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.345
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.184
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.025
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.191
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.386
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.217
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.296
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.301
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.057
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.301
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.566

Low Precision, High Recall when ignore EOS, 512 resolution, maxmium 40 boudning box

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.192
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.341
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.187
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.027
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.192
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.386
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.215
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.298
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.306
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.064
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.311
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.562

Low Precision, High Recall when ignore EOS, 512 resolution, maxmium 60 boudning box

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.192
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.340
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.187
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.027
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.192
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.386
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.215
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.299
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.307
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.065
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.313
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.563

Position Distribution (Multiple plausable position):

Released at 10am, 28th, Seq

Please refer to Stable-Pix2Seq for a full-fledged Pix2Seq. Problem to be solved : 1). Add sequence likelihood evaluationn on validation dataset. 2) Better organization of code. 3) FP16 support. 4) Beam Search

Step 3

Add tricks proposed in Pix2SEQ like droplayer, bounding box augmentation, multiple crop augmentation and so on.

To be released at 10th, Oct

We add sequence augmentation, token dropout, warmup, linear learning rate decay, drop layer and instance Repetition. We observer signifcnat improvement over original version. We follow the original schedule to train the model. We expect the training can be finished with 10 days on 1333 resolution with 300 epoches training.

Contributor

Peng Gao, Jingqiu Zhou

Acknowledegement

This codebase heavily borrow from DETR, CART, minGPT and Fairseq and motivated by the method explained in Pix2Seq

Name		Name	Last commit message	Last commit date
Latest commit History 68 Commits
datasets		datasets
models		models
util		util
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
demo1.PNG		demo1.PNG
demo2.PNG		demo2.PNG
demo3.PNG		demo3.PNG
distribution.jpg		distribution.jpg
engine.py		engine.py
hubconf.py		hubconf.py
main.py		main.py
predict.py		predict.py
requirements.txt		requirements.txt
run_with_submitit.py		run_with_submitit.py
test_all.py		test_all.py
tox.ini		tox.ini

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Unofficial-Pix2seq: A Language Modeling Framework for Object Detection

Please check new codebase Stable-Pix2Seq

Aim

Step 1

How to use?

Released at 8pm, 26th, Seq

Released at 10pm, 26th, Seq

Step 2

Observation

Released at 10am, 28th, Seq

Step 3

To be released at 10th, Oct

Contributor

Acknowledegement

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