ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Dowon <ks2515@naver.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher
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---
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comments: true
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description: Learn how to load YOLOv5🚀 from PyTorch Hub at https://pytorch.org/hub/ultralytics_yolov5 and perform image inference. UPDATED 26 March 2023.
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---
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📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5).
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📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5).
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UPDATED 26 March 2023.
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## Before You Start
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@ -20,6 +21,7 @@ pip install -r https://raw.githubusercontent.com/ultralytics/yolov5/master/requi
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### Simple Example
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This example loads a pretrained YOLOv5s model from PyTorch Hub as `model` and passes an image for inference. `'yolov5s'` is the lightest and fastest YOLOv5 model. For details on all available models please see the [README](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
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```python
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import torch
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@ -40,10 +42,10 @@ results.pandas().xyxy[0]
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# 3 986.00 304.00 1028.0 420.0 0.286865 27 tie
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```
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### Detailed Example
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This example shows **batched inference** with **PIL** and **OpenCV** image sources. `results` can be **printed** to console, **saved** to `runs/hub`, **showed** to screen on supported environments, and returned as **tensors** or **pandas** dataframes.
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```python
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import cv2
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import torch
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@ -73,12 +75,15 @@ results.pandas().xyxy[0] # im1 predictions (pandas)
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# 2 114.75 195.75 1095.0 708.0 0.624512 0 person
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# 3 986.00 304.00 1028.0 420.0 0.286865 27 tie
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```
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<img src="https://user-images.githubusercontent.com/26833433/124915064-62a49e00-dff1-11eb-86b3-a85b97061afb.jpg" width="500"> <img src="https://user-images.githubusercontent.com/26833433/124915055-60424400-dff1-11eb-9055-24585b375a29.jpg" width="300">
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For all inference options see YOLOv5 `AutoShape()` forward [method](https://github.com/ultralytics/yolov5/blob/30e4c4f09297b67afedf8b2bcd851833ddc9dead/models/common.py#L243-L252).
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### Inference Settings
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YOLOv5 models contain various inference attributes such as **confidence threshold**, **IoU threshold**, etc. which can be set by:
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```python
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model.conf = 0.25 # NMS confidence threshold
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iou = 0.45 # NMS IoU threshold
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@ -91,9 +96,10 @@ model.conf = 0.25 # NMS confidence threshold
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results = model(im, size=320) # custom inference size
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```
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### Device
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Models can be transferred to any device after creation:
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```python
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model.cpu() # CPU
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model.cuda() # GPU
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@ -101,6 +107,7 @@ model.to(device) # i.e. device=torch.device(0)
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```
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Models can also be created directly on any `device`:
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```python
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s', device='cpu') # load on CPU
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```
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@ -108,33 +115,45 @@ model = torch.hub.load('ultralytics/yolov5', 'yolov5s', device='cpu') # load on
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💡 ProTip: Input images are automatically transferred to the correct model device before inference.
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### Silence Outputs
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Models can be loaded silently with `_verbose=False`:
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```python
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s', _verbose=False) # load silently
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```
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### Input Channels
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To load a pretrained YOLOv5s model with 4 input channels rather than the default 3:
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```python
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s', channels=4)
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```
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In this case the model will be composed of pretrained weights **except for** the very first input layer, which is no longer the same shape as the pretrained input layer. The input layer will remain initialized by random weights.
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### Number of Classes
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To load a pretrained YOLOv5s model with 10 output classes rather than the default 80:
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```python
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s', classes=10)
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```
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In this case the model will be composed of pretrained weights **except for** the output layers, which are no longer the same shape as the pretrained output layers. The output layers will remain initialized by random weights.
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### Force Reload
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If you run into problems with the above steps, setting `force_reload=True` may help by discarding the existing cache and force a fresh download of the latest YOLOv5 version from PyTorch Hub.
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```python
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) # force reload
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```
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### Screenshot Inference
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To run inference on your desktop screen:
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```python
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import torch
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from PIL import ImageGrab
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@ -171,14 +190,18 @@ threading.Thread(target=run, args=[model1, 'https://ultralytics.com/images/bus.j
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```
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### Training
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To load a YOLOv5 model for training rather than inference, set `autoshape=False`. To load a model with randomly initialized weights (to train from scratch) use `pretrained=False`. You must provide your own training script in this case. Alternatively see our YOLOv5 [Train Custom Data Tutorial](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) for model training.
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To load a YOLOv5 model for training rather than inference, set `autoshape=False`. To load a model with randomly initialized weights (to train from scratch) use `pretrained=False`. You must provide your own training script in this case. Alternatively see our YOLOv5 [Train Custom Data Tutorial](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) for model training.
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```python
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) # load pretrained
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False, pretrained=False) # load scratch
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```
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### Base64 Results
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For use with API services. See https://github.com/ultralytics/yolov5/pull/2291 and [Flask REST API](https://github.com/ultralytics/yolov5/tree/master/utils/flask_rest_api) example for details.
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```python
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results = model(im) # inference
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@ -192,18 +215,23 @@ for im in results.ims:
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```
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### Cropped Results
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Results can be returned and saved as detection crops:
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```python
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results = model(im) # inference
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crops = results.crop(save=True) # cropped detections dictionary
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```
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### Pandas Results
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Results can be returned as [Pandas DataFrames](https://pandas.pydata.org/):
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```python
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results = model(im) # inference
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results.pandas().xyxy[0] # Pandas DataFrame
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```
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<details markdown>
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<summary>Pandas Output (click to expand)</summary>
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@ -215,24 +243,31 @@ print(results.pandas().xyxy[0])
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# 2 114.75 195.75 1095.0 708.0 0.624512 0 person
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# 3 986.00 304.00 1028.0 420.0 0.286865 27 tie
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```
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</details>
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### Sorted Results
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Results can be sorted by column, i.e. to sort license plate digit detection left-to-right (x-axis):
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```python
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results = model(im) # inference
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results.pandas().xyxy[0].sort_values('xmin') # sorted left-right
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```
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### Box-Cropped Results
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Results can be returned and saved as detection crops:
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```python
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results = model(im) # inference
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crops = results.crop(save=True) # cropped detections dictionary
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```
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### JSON Results
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Results can be returned in JSON format once converted to `.pandas()` dataframes using the `.to_json()` method. The JSON format can be modified using the `orient` argument. See pandas `.to_json()` [documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) for details.
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```python
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results = model(ims) # inference
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results.pandas().xyxy[0].to_json(orient="records") # JSON img1 predictions
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@ -253,7 +288,9 @@ results.pandas().xyxy[0].to_json(orient="records") # JSON img1 predictions
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</details>
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## Custom Models
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This example loads a custom 20-class [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml)-trained YOLOv5s model `'best.pt'` with PyTorch Hub.
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```python
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model = torch.hub.load('ultralytics/yolov5', 'custom', path='path/to/best.pt') # local model
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model = torch.hub.load('path/to/yolov5', 'custom', path='path/to/best.pt', source='local') # local repo
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@ -286,7 +323,6 @@ YOLOv5 may be run in any of the following up-to-date verified environments (with
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- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
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- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
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## Status
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<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
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