`ultralytics 8.0.84` results JSON outputs (#2171)
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Talia Bender <85292283+taliabender@users.noreply.github.com>single_channel
Glenn Jocher
2 years ago
committed by
GitHub
parent
6c082ebd6f
commit
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# YOLO Inference API (UNDER CONSTRUCTION)
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The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
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## API URL
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The API URL is the address used to access the YOLO Inference API. In this case, the base URL is:
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```
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https://api.ultralytics.com/inference/v1
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```
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To access the API with a specific model and your API key, you can include them as query parameters in the API URL. The `model` parameter refers to the `MODEL_ID` you want to use for inference, and the `key` parameter corresponds to your `API_KEY`.
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The complete API URL with the model and API key parameters would be:
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```
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https://api.ultralytics.com/inference/v1?model=MODEL_ID&key=API_KEY
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```
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Replace `MODEL_ID` with the ID of the model you want to use and `API_KEY` with your actual API key from [https://hub.ultralytics.com/settings?tab=api+keys](https://hub.ultralytics.com/settings?tab=api+keys).
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## Example Usage in Python
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To access the YOLO Inference API with the specified model and API key using Python, you can use the following code:
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```python
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import requests
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api_key = "API_KEY"
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model_id = "MODEL_ID"
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url = f"https://api.ultralytics.com/inference/v1?model={model_id}&key={api_key}"
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image_path = "image.jpg"
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with open(image_path, "rb") as image_file:
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files = {"image": image_file}
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response = requests.post(url, files=files)
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print(response.text)
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```
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In this example, replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze.
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## Example Usage with CLI
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You can use the YOLO Inference API with the command-line interface (CLI) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
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```commandline
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curl -X POST -F image=@image.jpg "https://api.ultralytics.com/inference/v1?model=MODEL_ID&key=API_KEY"
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```
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## Passing Arguments
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This command sends a POST request to the YOLO Inference API with the specified `model` and `key` parameters in the URL, along with the image file specified by `@image.jpg`.
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Here's an example of passing the `model`, `key`, and `normalize` arguments via the API URL using the `requests` library in Python:
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```python
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import requests
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api_key = "API_KEY"
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model_id = "MODEL_ID"
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url = "https://api.ultralytics.com/inference/v1"
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# Define your query parameters
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params = {
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"key": api_key,
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"model": model_id,
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"normalize": "True"
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}
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image_path = "image.jpg"
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with open(image_path, "rb") as image_file:
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files = {"image": image_file}
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response = requests.post(url, files=files, params=params)
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print(response.text)
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```
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In this example, the `params` dictionary contains the query parameters `key`, `model`, and `normalize`, which tells the API to return all values in normalized image coordinates from 0 to 1. The `normalize` parameter is set to `"True"` as a string since query parameters should be passed as strings. These query parameters are then passed to the `requests.post()` function.
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This will send the query parameters along with the file in the POST request. Make sure to consult the API documentation for the list of available arguments and their expected values.
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## Return JSON format
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The YOLO Inference API returns a JSON list with the detection results. The format of the JSON list will be the same as the one produced locally by the `results[0].tojson()` command.
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The JSON list contains information about the detected objects, their coordinates, classes, and confidence scores.
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### Detect Model Format
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YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
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!!! example "Detect Model JSON Response"
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=== "Local"
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```python
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from ultralytics import YOLO
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# Load model
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model = YOLO('yolov8n.pt')
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# Run inference
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results = model('image.jpg')
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# Print image.jpg results in JSON format
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print(results[0].tojson())
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```
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=== "CLI API"
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```commandline
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curl -X POST -F image=@image.jpg https://api.ultralytics.com/inference/v1?model=MODEL_ID,key=API_KEY
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```
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=== "Python API"
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```python
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import requests
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api_key = "API_KEY"
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model_id = "MODEL_ID"
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url = "https://api.ultralytics.com/inference/v1"
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# Define your query parameters
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params = {
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"key": api_key,
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"model": model_id,
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}
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image_path = "image.jpg"
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with open(image_path, "rb") as image_file:
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files = {"image": image_file}
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response = requests.post(url, files=files, params=params)
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print(response.text)
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```
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=== "JSON Response"
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```json
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[
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{
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"name": "person",
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"class": 0,
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"confidence": 0.8359682559967041,
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"box": {
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"x1": 0.08974208831787109,
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"y1": 0.27418340047200523,
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"x2": 0.8706787109375,
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"y2": 0.9887352837456598
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}
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},
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{
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"name": "person",
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"class": 0,
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"confidence": 0.8189555406570435,
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"box": {
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"x1": 0.5847355842590332,
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"y1": 0.05813225640190972,
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"x2": 0.8930277824401855,
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"y2": 0.9903111775716146
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}
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},
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{
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"name": "tie",
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"class": 27,
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"confidence": 0.2909725308418274,
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"box": {
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"x1": 0.3433395862579346,
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"y1": 0.6070465511745877,
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"x2": 0.40964522361755373,
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"y2": 0.9849439832899306
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}
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}
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]
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```
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### Segment Model Format
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YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
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!!! example "Segment Model JSON Response"
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=== "Local"
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```python
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from ultralytics import YOLO
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# Load model
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model = YOLO('yolov8n-seg.pt')
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# Run inference
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results = model('image.jpg')
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# Print image.jpg results in JSON format
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print(results[0].tojson())
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```
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=== "CLI API"
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```commandline
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curl -X POST -F image=@image.jpg https://api.ultralytics.com/inference/v1?model=MODEL_ID,key=API_KEY
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```
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=== "Python API"
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```python
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import requests
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api_key = "API_KEY"
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model_id = "MODEL_ID"
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url = "https://api.ultralytics.com/inference/v1"
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# Define your query parameters
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params = {
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"key": api_key,
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"model": model_id,
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}
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image_path = "image.jpg"
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with open(image_path, "rb") as image_file:
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files = {"image": image_file}
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response = requests.post(url, files=files, params=params)
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print(response.text)
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```
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=== "JSON Response"
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Note `segments` `x` and `y` lengths may vary from one object to another. Larger or more complex objects may have more segment points.
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```json
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[
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{
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"name": "person",
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"class": 0,
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"confidence": 0.856913149356842,
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"box": {
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"x1": 0.1064866065979004,
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"y1": 0.2798851860894097,
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"x2": 0.8738358497619629,
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"y2": 0.9894873725043403
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},
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"segments": {
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"x": [
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0.421875,
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0.4203124940395355,
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0.41718751192092896
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...
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],
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"y": [
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0.2888889014720917,
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0.2916666567325592,
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0.2916666567325592
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...
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]
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}
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},
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{
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"name": "person",
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"class": 0,
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"confidence": 0.8512625694274902,
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"box": {
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"x1": 0.5757311820983887,
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"y1": 0.053943040635850696,
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"x2": 0.8960096359252929,
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"y2": 0.985154045952691
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},
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"segments": {
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"x": [
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0.7515624761581421,
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0.75,
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0.7437499761581421
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...
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],
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"y": [
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0.0555555559694767,
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0.05833333358168602,
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0.05833333358168602
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...
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]
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}
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},
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{
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"name": "tie",
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"class": 27,
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"confidence": 0.6485961675643921,
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"box": {
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"x1": 0.33911995887756347,
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"y1": 0.6057066175672743,
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"x2": 0.4081430912017822,
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"y2": 0.9916408962673611
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},
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"segments": {
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"x": [
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0.37187498807907104,
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0.37031251192092896,
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0.3687500059604645
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...
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],
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"y": [
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0.6111111044883728,
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0.6138888597488403,
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0.6138888597488403
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...
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]
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}
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}
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]
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```
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### Pose Model Format
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YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
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!!! example "Pose Model JSON Response"
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=== "Local"
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```python
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from ultralytics import YOLO
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# Load model
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model = YOLO('yolov8n-seg.pt')
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# Run inference
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results = model('image.jpg')
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# Print image.jpg results in JSON format
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print(results[0].tojson())
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```
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=== "CLI API"
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```commandline
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curl -X POST -F image=@image.jpg https://api.ultralytics.com/inference/v1?model=MODEL_ID,key=API_KEY
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```
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=== "Python API"
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```python
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import requests
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api_key = "API_KEY"
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model_id = "MODEL_ID"
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url = "https://api.ultralytics.com/inference/v1"
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# Define your query parameters
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params = {
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"key": api_key,
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"model": model_id,
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}
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image_path = "image.jpg"
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with open(image_path, "rb") as image_file:
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files = {"image": image_file}
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response = requests.post(url, files=files, params=params)
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print(response.text)
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```
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=== "JSON Response"
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Note COCO-keypoints pretrained models will have 17 human keypoints. The `visible` part of the keypoints indicates whether a keypoint is visible or obscured. Obscured keypoints may be outside the image or may not be visible, i.e. a person's eyes facing away from the camera.
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```json
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[
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{
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"name": "person",
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"class": 0,
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"confidence": 0.8439509868621826,
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"box": {
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"x1": 0.1125,
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"y1": 0.28194444444444444,
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"x2": 0.7953125,
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"y2": 0.9902777777777778
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},
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"keypoints": {
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"x": [
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0.5058594942092896,
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0.5103894472122192,
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0.4920862317085266
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...
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],
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"y": [
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0.48964157700538635,
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0.4643048942089081,
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0.4465252459049225
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...
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],
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"visible": [
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0.8726999163627625,
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0.653947651386261,
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0.9130823612213135
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...
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]
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}
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},
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{
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"name": "person",
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"class": 0,
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"confidence": 0.7474289536476135,
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"box": {
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"x1": 0.58125,
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"y1": 0.0625,
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"x2": 0.8859375,
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"y2": 0.9888888888888889
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},
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"keypoints": {
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"x": [
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0.778544008731842,
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0.7976160049438477,
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0.7530890107154846
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...
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],
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"y": [
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0.27595141530036926,
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0.2378823608160019,
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0.23644638061523438
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...
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],
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"visible": [
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0.8900790810585022,
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0.789978563785553,
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0.8974530100822449
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...
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]
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}
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}
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]
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```
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@ -0,0 +1,82 @@
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# YOLOv5 🚀 on AWS Deep Learning Instance: A Comprehensive Guide
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This guide will help new users run YOLOv5 on an Amazon Web Services (AWS) Deep Learning instance. AWS offers a [Free Tier](https://aws.amazon.com/free/) and a [credit program](https://aws.amazon.com/activate/) for a quick and affordable start.
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Other quickstart options for YOLOv5 include our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>, [GCP Deep Learning VM](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial), and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) <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>. *Updated: 21 April 2023*.
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## 1. AWS Console Sign-in
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Create an account or sign in to the AWS console at [https://aws.amazon.com/console/](https://aws.amazon.com/console/) and select the **EC2** service.
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## 2. Launch Instance
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In the EC2 section of the AWS console, click the **Launch instance** button.
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### Choose an Amazon Machine Image (AMI)
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Enter 'Deep Learning' in the search field and select the most recent Ubuntu Deep Learning AMI (recommended), or an alternative Deep Learning AMI. For more information on selecting an AMI, see [Choosing Your DLAMI](https://docs.aws.amazon.com/dlami/latest/devguide/options.html).
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### Select an Instance Type
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A GPU instance is recommended for most deep learning purposes. Training new models will be faster on a GPU instance than a CPU instance. Multi-GPU instances or distributed training across multiple instances with GPUs can offer sub-linear scaling. To set up distributed training, see [Distributed Training](https://docs.aws.amazon.com/dlami/latest/devguide/distributed-training.html).
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**Note:** The size of your model should be a factor in selecting an instance. If your model exceeds an instance's available RAM, select a different instance type with enough memory for your application.
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Refer to [EC2 Instance Types](https://aws.amazon.com/ec2/instance-types/) and choose Accelerated Computing to see the different GPU instance options.
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For more information on GPU monitoring and optimization, see [GPU Monitoring and Optimization](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-gpu.html). For pricing, see [On-Demand Pricing](https://aws.amazon.com/ec2/pricing/on-demand/) and [Spot Pricing](https://aws.amazon.com/ec2/spot/pricing/).
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### Configure Instance Details
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|
||||
Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 70% discount compared to On-Demand prices. We recommend a persistent spot instance, which will save your data and restart automatically when spot instance availability returns after spot instance termination. For full-price On-Demand instances, leave these settings at their default values.
|
||||
|
||||
|
||||
|
||||
Complete Steps 4-7 to finalize your instance hardware and security settings, and then launch the instance.
|
||||
|
||||
## 3. Connect to Instance
|
||||
|
||||
Select the checkbox next to your running instance, and then click Connect. Copy and paste the SSH terminal command into a terminal of your choice to connect to your instance.
|
||||
|
||||
|
||||
|
||||
## 4. Run YOLOv5
|
||||
|
||||
Once you have logged in to your instance, clone the repository and install the dependencies in a [**Python>=3.7.0**](https://www.python.org/) environment, including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ultralytics/yolov5 # clone
|
||||
cd yolov5
|
||||
pip install -r requirements.txt # install
|
||||
```
|
||||
|
||||
Then, start training, testing, detecting, and exporting YOLOv5 models:
|
||||
|
||||
```bash
|
||||
python train.py # train a model
|
||||
python val.py --weights yolov5s.pt # validate a model for Precision, Recall, and mAP
|
||||
python detect.py --weights yolov5s.pt --source path/to/images # run inference on images and videos
|
||||
python export.py --weights yolov5s.pt --include onnx coreml tflite # export models to other formats
|
||||
```
|
||||
|
||||
## Optional Extras
|
||||
|
||||
Add 64GB of swap memory (to `--cache` large datasets):
|
||||
|
||||
```bash
|
||||
sudo fallocate -l 64G /swapfile
|
||||
sudo chmod 600 /swapfile
|
||||
sudo mkswap /swapfile
|
||||
sudo swapon /swapfile
|
||||
free -h # check memory
|
||||
```
|
||||
|
||||
Now you have successfully set up and run YOLOv5 on an AWS Deep Learning instance. Enjoy training, testing, and deploying your object detection models!
|
||||
@ -0,0 +1,58 @@
|
||||
# Get Started with YOLOv5 🚀 in Docker
|
||||
|
||||
This tutorial will guide you through the process of setting up and running YOLOv5 in a Docker container.
|
||||
|
||||
You can also explore other quickstart options for YOLOv5, such as our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>, [GCP Deep Learning VM](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial), and [Amazon AWS](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial). *Updated: 21 April 2023*.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. **Nvidia Driver**: Version 455.23 or higher. Download from [Nvidia's website](https://www.nvidia.com/Download/index.aspx).
|
||||
2. **Nvidia-Docker**: Allows Docker to interact with your local GPU. Installation instructions are available on the [Nvidia-Docker GitHub repository](https://github.com/NVIDIA/nvidia-docker).
|
||||
3. **Docker Engine - CE**: Version 19.03 or higher. Download and installation instructions can be found on the [Docker website](https://docs.docker.com/install/).
|
||||
|
||||
## Step 1: Pull the YOLOv5 Docker Image
|
||||
|
||||
The Ultralytics YOLOv5 DockerHub repository is available at [https://hub.docker.com/r/ultralytics/yolov5](https://hub.docker.com/r/ultralytics/yolov5). Docker Autobuild ensures that the `ultralytics/yolov5:latest` image is always in sync with the most recent repository commit. To pull the latest image, run the following command:
|
||||
|
||||
```bash
|
||||
sudo docker pull ultralytics/yolov5:latest
|
||||
```
|
||||
|
||||
## Step 2: Run the Docker Container
|
||||
|
||||
### Basic container:
|
||||
|
||||
Run an interactive instance of the YOLOv5 Docker image (called a "container") using the `-it` flag:
|
||||
|
||||
```bash
|
||||
sudo docker run --ipc=host -it ultralytics/yolov5:latest
|
||||
```
|
||||
|
||||
### Container with local file access:
|
||||
|
||||
To run a container with access to local files (e.g., COCO training data in `/datasets`), use the `-v` flag:
|
||||
|
||||
```bash
|
||||
sudo docker run --ipc=host -it -v "$(pwd)"/datasets:/usr/src/datasets ultralytics/yolov5:latest
|
||||
```
|
||||
|
||||
### Container with GPU access:
|
||||
|
||||
To run a container with GPU access, use the `--gpus all` flag:
|
||||
|
||||
```bash
|
||||
sudo docker run --ipc=host -it --gpus all ultralytics/yolov5:latest
|
||||
```
|
||||
|
||||
## Step 3: Use YOLOv5 🚀 within the Docker Container
|
||||
|
||||
Now you can train, test, detect, and export YOLOv5 models within the running Docker container:
|
||||
|
||||
```bash
|
||||
python train.py # train a model
|
||||
python val.py --weights yolov5s.pt # validate a model for Precision, Recall, and mAP
|
||||
python detect.py --weights yolov5s.pt --source path/to/images # run inference on images and videos
|
||||
python export.py --weights yolov5s.pt --include onnx coreml tflite # export models to other formats
|
||||
```
|
||||
|
||||
<p align="center"><img width="1000" src="https://user-images.githubusercontent.com/26833433/142224770-6e57caaf-ac01-4719-987f-c37d1b6f401f.png"></p>
|
||||
@ -0,0 +1,43 @@
|
||||
# Run YOLOv5 🚀 on Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) ⭐
|
||||
|
||||
This tutorial will guide you through the process of setting up and running YOLOv5 on a GCP Deep Learning VM. New GCP users are eligible for a [$300 free credit offer](https://cloud.google.com/free/docs/gcp-free-tier#free-trial).
|
||||
|
||||
You can also explore other quickstart options for YOLOv5, such as our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>, [Amazon AWS](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial) and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) <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>. *Updated: 21 April 2023*.
|
||||
|
||||
**Last Updated**: 6 May 2022
|
||||
|
||||
## Step 1: Create a Deep Learning VM
|
||||
|
||||
1. Go to the [GCP marketplace](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning) and select a **Deep Learning VM**.
|
||||
2. Choose an **n1-standard-8** instance (with 8 vCPUs and 30 GB memory).
|
||||
3. Add a GPU of your choice.
|
||||
4. Check 'Install NVIDIA GPU driver automatically on first startup?'
|
||||
5. Select a 300 GB SSD Persistent Disk for sufficient I/O speed.
|
||||
6. Click 'Deploy'.
|
||||
|
||||
The preinstalled [Anaconda](https://docs.anaconda.com/anaconda/packages/pkg-docs/) Python environment includes all dependencies.
|
||||
|
||||
<img width="1000" alt="GCP Marketplace" src="https://user-images.githubusercontent.com/26833433/105811495-95863880-5f61-11eb-841d-c2f2a5aa0ffe.png">
|
||||
|
||||
## Step 2: Set Up the VM
|
||||
|
||||
Clone the YOLOv5 repository and install the [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.7.0**](https://www.python.org/) environment, including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) will be downloaded automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ultralytics/yolov5 # clone
|
||||
cd yolov5
|
||||
pip install -r requirements.txt # install
|
||||
```
|
||||
|
||||
## Step 3: Run YOLOv5 🚀 on the VM
|
||||
|
||||
You can now train, test, detect, and export YOLOv5 models on your VM:
|
||||
|
||||
```bash
|
||||
python train.py # train a model
|
||||
python val.py --weights yolov5s.pt # validate a model for Precision, Recall, and mAP
|
||||
python detect.py --weights yolov5s.pt --source path/to/images # run inference on images and videos
|
||||
python export.py --weights yolov5s.pt --include onnx coreml tflite # export models to other formats
|
||||
```
|
||||
|
||||
<img width="1000" alt="GCP terminal" src="https://user-images.githubusercontent.com/26833433/142223900-275e5c9e-e2b5-43f7-a21c-35c4ca7de87c.png">
|
||||
@ -0,0 +1,76 @@
|
||||
# YOLOv5 Quickstart
|
||||
|
||||
See below for quickstart examples.
|
||||
|
||||
## Install
|
||||
|
||||
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
|
||||
[**Python>=3.7.0**](https://www.python.org/) environment, including
|
||||
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ultralytics/yolov5 # clone
|
||||
cd yolov5
|
||||
pip install -r requirements.txt # install
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Inference
|
||||
|
||||
YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
|
||||
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
|
||||
|
||||
```python
|
||||
import torch
|
||||
|
||||
# Model
|
||||
model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, 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.
|
||||
```
|
||||
|
||||
## Inference with detect.py
|
||||
|
||||
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
|
||||
the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
|
||||
|
||||
```bash
|
||||
python detect.py --weights yolov5s.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
screen # screenshot
|
||||
path/ # directory
|
||||
list.txt # list of images
|
||||
list.streams # list of streams
|
||||
'path/*.jpg' # glob
|
||||
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
```
|
||||
|
||||
## Training
|
||||
|
||||
The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
|
||||
results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
|
||||
and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
|
||||
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
|
||||
1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
|
||||
largest `--batch-size` possible, or pass `--batch-size -1` for
|
||||
YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
|
||||
|
||||
```bash
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
|
||||
yolov5s 64
|
||||
yolov5m 40
|
||||
yolov5l 24
|
||||
yolov5x 16
|
||||
```
|
||||
|
||||
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
|
||||
Loading…
Reference in new issue