Awesome

An oriented object detection framework based on TensorRT

<details open> <summary> Supported network: </summary>

FCOSR (code)

</details> <details open> <summary> TensorRT engine file: </summary>

Model	backbone	Param	Size	Input	GFLOPs	FPS	mAP	TensorRT
FCOSR-lite	Mobilenet v2	6.9M	51.63MB	1024×1024	101.25	7.64(NX)	74.30	code: ABCD
FCOSR-tiny	Mobilenet v2	3.52M	23.2MB	1024×1024	35.89	10.68(NX)	73.93	code: ABCD

</details>

This implement is modified from TensorRT/efficientdet. Support Running Mode: fix, whole, server(base on zeroMQ, wrappers)

The inference framework is shown bellow.

Detection result

Log framework

Recommend system environments:

Jetson Xavier NX / Jetson AGX Xavier
python 3.6
JetPack 4.6
CUDA 10.2 (from JetPack)
cuDNN 8.2.1 (from JetPack)
OpenCV 4.1.1 (from JetPack)
TensorRT 8.0.1.6 (from JetPack)
libzmq

Install

pip install Cython
pip install -r requirements.txt

Note: DOTA_devkit. INSTALL.md

Test result on Jetson AGX Xavier

Dota1.0 test set

name	size	patch size	gap	patches	det objects	det time(s)
P0031.png	5343×3795	1024	200	35	1197	2.75
P0051.png	4672×5430	1024	200	42	309	2.38
P0112.png	6989×4516	1024	200	54	184	3.02
P0137.png	5276×4308	1024	200	35	66	1.95
P1004.png	7001×3907	1024	200	45	183	2.52
P1125.png	7582×4333	1024	200	54	28	2.95
P1129.png	4093×6529	1024	200	40	70	2.23
P1146.png	5231×4616	1024	200	42	64	2.29
P1157.png	7278×5286	1024	200	63	184	3.47
P1378.png	5445×4561	1024	200	42	83	2.32
P1379.png	4426×4182	1024	200	30	686	1.78
P1393.png	6072×6540	1024	200	64	893	3.63
P1400.png	6471×4479	1024	200	48	348	2.63
P1402.png	4112×4793	1024	200	30	293	1.68
P1406.png	6531×4182	1024	200	40	19	2.19
P1415.png	4894x4898	1024	200	36	190	1.99
P1436.png	5136×5156	1024	200	42	39	2.31
P1448.png	7242×5678	1024	200	63	51	3.41
P1457.png	5193×4658	1024	200	42	382	2.33
P1461.png	6661×6308	1024	200	64	27	3.45
P1494.png	4782×6677	1024	200	48	70	2.61
P1500.png	4769×4386	1024	200	36	92	1.96
P1772.png	5963×5553	1024	200	49	28	2.70
P1774.png	5352×4281	1024	200	35	291	1.95
P1796.png	5870×5822	1024	200	49	308	2.74
P1870.png	5942×6059	1024	200	56	135	3.04
P2043.png	4165×3438	1024	200	20	1479	1.49
P2329.png	7950×4334	1024	200	60	83	3.26
P2641.png	7574×5625	1024	200	63	269	3.41
P2642.png	7039×5551	1024	200	63	451	3.50
P2643.png	7568×5619	1024	200	63	249	3.40
P2645.png	4605×3442	1024	200	24	357	1.42
P2762.png	8074×4359	1024	200	60	127	3.23
P2795.png	4495×3981	1024	200	30	65	1.64

How to use

We define configure file (yaml) to replace plenty of args.

# Small pictures inference mode
python infer_multi.py config/fix_mode/fcosr_tiny_nx.yaml
# Big whole picture inference mode
python infer_whole.py config/whole_mode/fcosr_tiny_agx_whole.yaml

server mode (ZeroMQ Wrapper)

# server
python infer_zmq_server.py config/zmq_server/fcosr_tiny_zmq_server.yaml
# client
python infer_zmq_client.py

A client running demo.

video

A configure file demo is:

mode: 'whole'  # support mode: fix, whole, server
port: 10000 # only server mode support this attribution
model:
  engine_file: '/home/nvidia/Desktop/FCOSR/model/epoch_36_16_lite_nx.trt' # TensorRT engine file path
  labels: 'labels.txt' # calss name
io: # only support whole/fix mode.
  input_dir: '/home/nvidia/DOTA_TEST/images/' # image folder path
  output_dir: 'result' # output
preprocess: # preprocess configure
  num_process: 8 # multi process
  queue_length: 40
  normalization: # normalization parameters
    enable: 1 # switch
    mean:
      - 123.675
      - 116.28
      - 103.53
    std:
      - 58.395
      - 57.12
      - 57.375
  split:    # split configure, only support whole/server mode.
    subsize: 1024
    gap: 200
postprocess: # postprocess configure
  num_process: 6 # multi process
  queue_length: 40
  nms_threshold: 0.1 # poly nms threshold 
  score_threshold: 0.1 # poly nms score threshold
  max_det_num: 2000
  draw_image: # visualization configure, only support whole/fix mode.
    enable: 0 # switch
    num: 20 # int or 'all'

Run Mode

Mode	Specified Attributions	Ignored Attributions	Description
fix	io, draw_image	split, port	progress fix size image
whole	io, draw_image, split	port	progress a big image
server	port, split	io, draw_image	use zeromq to get image, then progress it like whole mode