---
sdk: streamlit
sdk_version: 1.10.0 # The latest supported version
app_file: app.py
pinned: false
fullWidth: True
---
## Planogram Scoring
- 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
## YOLOv5
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
## Documentation
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
## Quick Start Examples
Install
[**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/):
```bash
$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt
```
Inference
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.
```
## Why YOLOv5
YOLOv5-P5 640 Figure (click to expand)
Figure Notes (click to expand)
* 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`
### Pretrained Checkpoints
[assets]: https://github.com/ultralytics/yolov5/releases
|Model |size
(pixels) |mAPval
0.5:0.95 |mAPtest
0.5:0.95 |mAPval
0.5 |Speed
V100 (ms) | |params
(M) |FLOPs
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 | |- |-
Table Notes (click to expand)
* APtest 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`
* SpeedGPU 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`
## Contribute
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.
## Contact
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).
