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Abhilashvj/planogram-compliance

sourceHugging Faceupdated 4y agoView on Hugging Face

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<div align="center">Planogram Scoring</div>

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—Train a Yolo Model on the available products in our data base to detect them on a shelf
—https://wandb.ai/abhilash001vj/YOLOv5/runs/1v6yh7nk?workspace=user-abhilash001vj
—Have the master planogram data captured as a matrix of products encoded as numbers (label encoding by looking the products names saved in a list of all - the available product names )
—Detect the products on real images from stores.
—Arrange the detected products in the captured photograph to rows and columns
—Compare the product arrangement of captured photograph to the existing master planogram and produce the compliance score for correctly placed products

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<div align="center">YOLOv5</div>

<p> YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. </p>

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<div align="center">Documentation</div>

See the YOLOv5 Docs for full documentation on training, testing and deployment.

<div align="center">Quick Start Examples</div>

<details open> <summary>Install</summary>

**Python>=3.6.0** is required with all requirements.txt installed including **PyTorch>=1.7**:

bash

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

</details>

<details open> <summary>Inference</summary>

Inference with YOLOv5 and PyTorch Hub. Models automatically download from the latest YOLOv5 release.

python

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

</details>

<div align="center">Why YOLOv5</div>

<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p> <details> <summary>YOLOv5-P5 640 Figure (click to expand)</summary>

<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p> </details> <details> <summary>Figure Notes (click to expand)</summary>

—GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
—EfficientDet data from google/automl at batch size 8.
—Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

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Pretrained Checkpoints

[assets]: https://github.com/ultralytics/yolov5/releases

Model	size<br><sup>(pixels)	mAP<sup>val<br>0.5:0.95	mAP<sup>test<br>0.5:0.95	mAP<sup>val<br>0.5	Speed<br><sup>V100 (ms)	params<br><sup>(M)	FLOPs<br><sup>640 (B)
[YOLOv5s][assets]	640	36.7	36.7	55.4	2.0	7.3	17.0
[YOLOv5m][assets]	640	44.5	44.5	63.1	2.7	21.4	51.3
[YOLOv5l][assets]	640	48.2	48.2	66.9	3.8	47.0	115.4
[YOLOv5x][assets]	640	50.4	50.4	68.8	6.1	87.7	218.8

[YOLOv5s6][assets]	1280	43.3	43.3	61.9	4.3	12.7	17.4
[YOLOv5m6][assets]	1280	50.5	50.5	68.7	8.4	35.9	52.4
[YOLOv5l6][assets]	1280	53.4	53.4	71.1	12.3	77.2	117.7
[YOLOv5x6][assets]	1280	54.4	54.4	72.0	22.4	141.8	222.9

[YOLOv5x6][assets] TTA	1280	55.0	55.0	72.0	70.8	-	-

<details> <summary>Table Notes (click to expand)</summary>

—AP<sup>test</sup> denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
—AP values are for single-model single-scale unless otherwise noted. Reproduce mAP by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
—Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes FP16 inference, postprocessing and NMS. Reproduce speed by python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half
—All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
—Test Time Augmentation (TTA) includes reflection and scale augmentation. Reproduce TTA by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

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<div align="center">Contribute</div>

We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started.

<div align="center">Contact</div>

For issues running YOLOv5 please visit GitHub Issues. For business or professional support requests please visit https://ultralytics.com/contact.

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