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✭ MAGNETRON ™ ✭: YOLOv7 is a platform for PROXIA that can do POSE ESTIMATION, object detection, segmentation and other visual recognition tasks.

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THE ABC 123 GROUP ™:

🤖 THE ABC 123 GROUP ™ 🤖

🌐 GENERAL CONSULTING ABC 123 BY OSAROPRIME ™.

🌐 ABC 123 USA ™

🌐 ABC 123 DESYGN ™

🌐 ABC 123 FILMS ™

=============================================================

             🌐 MAGENTRON ™ 🌐

🌐 ARTIFICIAL INTELLIGENCE 2.0 ™ : OBJECT DETECTION PROXIA

*️⃣📶🤖

🌐 PHYSICAL WORLD SENSE: SIGHT ✅

🌐 PHYSICAL WORLD SENSE: SMELL

🌐 PHYSICAL WORLD SENSE: HEARING

🌐 PHYSICAL WORLD SENSE: TASTE

🌐 PHYSICAL WORLD SENSE: TOUCH

+++++++++++++++++++++++++++++++++++++

🌐 ASTRAL BODY MINDCLOUD: NO

🌐 PRANIC BODY MINDCLOUD: NO

🌐 INSTINCTIVE MIND MINDCLOUD: ✅

🌐 ASTRAL MIND MINDCLOUD: NO

🌐 PRANIC MIND MINDCLOUD: NO

REQUIREMENTS:

[*] Software Requirements: Google Colab/Jupyter Notebook, Python

[*] HARDWARE REQUIREMENTS: fast TPU/GPU.

[*] DEPENDENCIES: INCLUDED (ALSO EXPAND INSTALLATION SECTION IN README BELOW FOR DOCKER COMMANDS IF NEEDED)

🌐 NOTE: THIS NOTEBOOK GUIDES YOU THROUGH OBJECT DETECTION USING YOLOv7. CLICK HERE FOR A COLAB/JUPYTER NOTEBOOK ON OBJECT DETECTION WITH YOLO3: https://github.com/GCABC123/magnetron.artificial-intelligence-2.0.mincloud.proxia--INSTINCTIVE-MIND-1

=============================================================

This Google Colab NOTEBOOK will guide you on one possible scheme to create an OBJECT DETECTION PROXIA in the ARTIFICIAL INTELLIGENCE 2.0™ FRAMEWORK/DOCUMENTATION. This notebook will guide you on using OBJECT DETECTION on IMAGES for making PROXIA to be used on MINDCLOUDS (IT CAN ALSO BE USED ON VIDEO AND FRAMES OF VIDEO FOOTGAGE). OBJECT DETECTION on IMAGES will be reffered to as OBJECT DETECTION B. This is useful in ARTIFICIAL INTELLIGENCE 2.0 ™ as far as helping robots in understanding IMAGES encountered on the internet. This will be on the INSTINCTIVE MIND MINDCLOUD and INFORMATION from this PROXIA must go to other PROXIA for processing e.g LOGIC/ASTRAL MIND MINDCLOUD PROXIA.

NOTE: YOLOv7 ALSO DOES POSE ESTIMATION, INSTANCE SEGMENTATION AND VARIOUS OTHER VISUAL IDENTIFICATION TASKS.

🌐 NOTE: ARTIFICIAL INTELLIGENCE 2.0™ IS PART OF MAGNETRONTECHNOLOGY.

🌐 NOTE : REMEMBER A PROXIA IS THE EQUIVALENT OF AN APP IN iOS APP DEVELOPMENT (THE iPHONE ITSELD WOULD BE A MINDCLOUD with the PROXIA stored on it (refer to ARTIFICIAL INTELLIGENCE 2.0 ™ DOCUMENTATION ONLINE)

CLICK ON ONE OF THE FOLLOWING LINKS FOR ARTIFICIAL INTELLIGENCE 2.0 ™ DOCUMENTATION ON FACEBOOK:

🌐 ARTIFICIAL INTELLIGENCE PRIMER ™ DOCUMENTATION

🌐 ARTIFICIAL INTELLIGENCE 2.0 ™ DOCUMENTATION

🌐 MEMBER'S CLUB ™ DOCUMENTATION

Official YOLOv7

PWC Hugging Face Spaces Open In Colab arxiv.org

Web Demo

Performance

MS COCO

Model Test Size APtest AP50test AP75test batch 1 fps batch 32 average time
YOLOv7 640 51.4% 69.7% 55.9% 161 fps 2.8 ms
YOLOv7-X 640 53.1% 71.2% 57.8% 114 fps 4.3 ms
YOLOv7-W6 1280 54.9% 72.6% 60.1% 84 fps 7.6 ms
YOLOv7-E6 1280 56.0% 73.5% 61.2% 56 fps 12.3 ms
YOLOv7-D6 1280 56.6% 74.0% 61.8% 44 fps 15.0 ms
YOLOv7-E6E 1280 56.8% 74.4% 62.1% 36 fps 18.7 ms

Installation

Docker environment (recommended)

Expand
# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3

# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx

# pip install required packages
pip install seaborn thop

# go to code folder
cd /yolov7

Testing

yolov7.pt yolov7x.pt yolov7-w6.pt yolov7-e6.pt yolov7-d6.pt yolov7-e6e.pt

python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val

You will get the results:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.51206
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.69730
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.55521
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.38453
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.63765
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.68772
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868

To measure accuracy, download COCO-annotations for Pycocotools to the ./coco/annotations/instances_val2017.json

Training

Data preparation

bash scripts/get_coco.sh
  • Download MS COCO dataset images (train, val, test) and labels. If you have previously used a different version of YOLO, we strongly recommend that you delete train2017.cache and val2017.cache files, and redownload labels

Single GPU training

# train p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml

# train p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml

Multiple GPU training

# train p5 models
python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml

# train p6 models
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml

Transfer learning

yolov7_training.pt yolov7x_training.pt yolov7-w6_training.pt yolov7-e6_training.pt yolov7-d6_training.pt yolov7-e6e_training.pt

Single GPU finetuning for custom dataset

# finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml

# finetune p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml

Re-parameterization

See reparameterization.ipynb

Inference

On video:

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4

On image:

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg

Export

Pytorch to CoreML (and inference on MacOS/iOS) Open In Colab

Pytorch to ONNX with NMS (and inference) Open In Colab

python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \
        --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640

Pytorch to TensorRT with NMS (and inference) Open In Colab

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16

Pytorch to TensorRT another way Open In Colab

Expand

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights yolov7-tiny.pt --grid --include-nms
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16

# Or use trtexec to convert ONNX to TensorRT engine
/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16

Tested with: Python 3.7.13, Pytorch 1.12.0+cu113

Pose estimation

code yolov7-w6-pose.pt

Instance segmentation

code yolov7-mask.pt

Instance segmentation

code yolov7-seg.pt

YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)

Model Test Size APbox AP50box AP75box APmask AP50mask AP75mask
YOLOv7-seg 640 51.4% 69.4% 55.8% 41.5% 65.5% 43.7%

Anchor free detection head

code yolov7-u6.pt

YOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)

Model Test Size APval AP50val AP75val
YOLOv7-u6 640 52.6% 69.7% 57.3%

Citation

@article{wang2022yolov7,
  title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2207.02696},
  year={2022}
}
@article{wang2022designing,
  title={Designing Network Design Strategies Through Gradient Path Analysis},
  author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},
  journal={arXiv preprint arXiv:2211.04800},
  year={2022}
}

Teaser

Yolov7-semantic & YOLOv7-panoptic & YOLOv7-caption

Acknowledgements

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