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| sdk: streamlit | |
| sdk_version: 1.10.0 # The latest supported version | |
| app_file: app.py | |
| pinned: false | |
| fullWidth: True | |
| ## <div align="center">Planogram Scoring</div> | |
| <p> | |
| </p> | |
| - 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 | |
| </div> | |
| ## <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> | |
| </div> | |
| ## <div align="center">Documentation</div> | |
| See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. | |
| ## <div align="center">Quick Start Examples</div> | |
| <details open> | |
| <summary>Install</summary> | |
| [**Python>=3.6.0**](https://www.python.org/) is required with all | |
| [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including | |
| [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/): | |
| <!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev --> | |
| ```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](https://github.com/ultralytics/yolov5/issues/36). Models automatically download | |
| from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). | |
| ```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](https://github.com/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` | |
| </details> | |
| ### 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](http://cocodataset.org/#upload) 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](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) 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](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale | |
| augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` | |
| </details> | |
| ## <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](CONTRIBUTING.md) to get started. | |
| ## <div align="center">Contact</div> | |
| For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or | |
| professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact). | |
| <br> | |
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| <a href="https://github.com/ultralytics"> | |
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