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Merge remote-tracking branch 'origin/feature/from-roboflow-base-model' into feature/from-roboflow-base-model

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SkalskiP 2024-01-04 15:09:29 +01:00
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examples/traffic_analysis/README.md
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@ -41,40 +41,40 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-
- ultralytics
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
file, which is essential for the object detection process. This file contains the
file, which is essential for the object detection process. This file contains the
data that the model uses to identify objects in the video.
- `--source_video_path`: Required. The path to the source video file that will be
analyzed. This is the input video on which traffic flow analysis will be performed.
- `--target_video_path` (optional): The path to save the output video with
annotations. If not specified, the processed video will be displayed in real-time
- `--target_video_path` (optional): The path to save the output video with
annotations. If not specified, the processed video will be displayed in real-time
without being saved.
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
model to filter detections. Default is `0.3`. This determines how confident the
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
model to filter detections. Default is `0.3`. This determines how confident the
model should be to recognize an object in the video.
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
for the model. Default is 0.7. This value is used to manage object detection
for the model. Default is 0.7. This value is used to manage object detection
accuracy, particularly in distinguishing between different objects.
- inference
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
- `--roboflow_api_key` (optional): The API key for Roboflow services. If not provided
directly, the script tries to fetch it from the `ROBOFLOW_API_KEY` environment
variable. Follow [this guide](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
to acquire your `API KEY`.
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
value is `"vehicle-count-in-drone-video/6"`.
- `--model_id` (optional): Designates the Roboflow model ID to be used. The default
value is `"vehicle-count-in-drone-video/6"`.
- `--source_video_path`: Required. The path to the source video file that will be
analyzed. This is the input video on which traffic flow analysis will be performed.
- `--target_video_path` (optional): The path to save the output video with
annotations. If not specified, the processed video will be displayed in real-time
- `--target_video_path` (optional): The path to save the output video with
annotations. If not specified, the processed video will be displayed in real-time
without being saved.
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
model to filter detections. Default is `0.3`. This determines how confident the
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO
model to filter detections. Default is `0.3`. This determines how confident the
model should be to recognize an object in the video.
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
for the model. Default is 0.7. This value is used to manage object detection
for the model. Default is 0.7. This value is used to manage object detection
accuracy, particularly in distinguishing between different objects.
## ⚙️ run
@ -105,12 +105,12 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-
This demo integrates two main components, each with its own licensing:
- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
- ultralytics: The object detection model used in this demo, YOLOv8, is distributed
under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
You can find more details about this license here.
- supervision: The analytics code that powers the zone-based analysis in this demo is
based on the Supervision library, which is licensed under the
- supervision: The analytics code that powers the zone-based analysis in this demo is
based on the Supervision library, which is licensed under the
[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
makes the Supervision part of the code fully open source and freely usable in your
projects.