[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)
# Odin
Super Fast and super cheap object detection at massive scale in less than 10 lines of code!
# Appreciation
* Lucidrains
* Agorians
# Install
`pip install vodin`
# Usage
Here are three examples demonstrating the usage of the `Odin` class from your provided code:
**Example 1: Basic Usage**
```python
# Import the necessary modules and classes
from odin import Odin
# Initialize the Odin object with paths and thresholds
odin = Odin(
source_weights_path="yolo.weights",
source_video_path="input_video.mp4",
target_video_path="output_video.mp4",
confidence_threshold=0.3,
iou_threshold=0.7
)
# Run the object to process the video
odin.run()
```
**Example 2: Custom Parameters**
```python
# Import the necessary modules and classes
from odin import Odin
# Initialize the Odin object with custom parameters
odin = Odin(
source_weights_path="custom_yolo.weights",
source_video_path="input_video.mp4",
target_video_path="output_video.mp4",
confidence_threshold=0.5,
iou_threshold=0.6
)
# Run the object to process the video
odin.run()
```
**Example 3: Advanced Usage**
```python
# Import the necessary modules and classes
from odin import Odin
# Initialize the Odin object with paths and thresholds
odin = Odin(
source_weights_path="yolo.weights",
source_video_path="input_video.mp4",
target_video_path="output_video.mp4",
confidence_threshold=0.3,
iou_threshold=0.7
)
# Customize further configurations if needed
odin.tracker.set_max_distance(50)
odin.box_annotator.set_box_color((0, 255, 0))
odin.model.set_device("cuda")
# Run the object to process the video
odin.run()
```
# Architecture
* [Odin utilizes YoloV7, weights can be downloaded here](https://drive.google.com/file/d/1yEYFq1jCIpklofMMhuqQKwyTfvj1hLQ1/view)
# License
MIT
Raw data
{
"_id": null,
"home_page": "https://github.com/kyegomez/odin",
"name": "vodin",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6,<4.0",
"maintainer_email": "",
"keywords": "artificial intelligence,deep learning,optimizers,Prompt Engineering",
"author": "Kye Gomez",
"author_email": "kye@apac.ai",
"download_url": "https://files.pythonhosted.org/packages/42/84/b67789c59c44c1fcb58656209cc26c386657bc2a9aeeda83dc65ba4777cd/vodin-0.0.3.tar.gz",
"platform": null,
"description": "[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)\n\n# Odin\nSuper Fast and super cheap object detection at massive scale in less than 10 lines of code!\n\n# Appreciation\n* Lucidrains\n* Agorians\n\n\n# Install\n`pip install vodin`\n\n# Usage\n\nHere are three examples demonstrating the usage of the `Odin` class from your provided code:\n\n**Example 1: Basic Usage**\n\n```python\n# Import the necessary modules and classes\nfrom odin import Odin\n\n# Initialize the Odin object with paths and thresholds\nodin = Odin(\n source_weights_path=\"yolo.weights\",\n source_video_path=\"input_video.mp4\",\n target_video_path=\"output_video.mp4\",\n confidence_threshold=0.3,\n iou_threshold=0.7\n)\n\n# Run the object to process the video\nodin.run()\n```\n\n**Example 2: Custom Parameters**\n\n```python\n# Import the necessary modules and classes\nfrom odin import Odin\n\n# Initialize the Odin object with custom parameters\nodin = Odin(\n source_weights_path=\"custom_yolo.weights\",\n source_video_path=\"input_video.mp4\",\n target_video_path=\"output_video.mp4\",\n confidence_threshold=0.5,\n iou_threshold=0.6\n)\n\n# Run the object to process the video\nodin.run()\n```\n\n**Example 3: Advanced Usage**\n\n```python\n# Import the necessary modules and classes\nfrom odin import Odin\n\n# Initialize the Odin object with paths and thresholds\nodin = Odin(\n source_weights_path=\"yolo.weights\",\n source_video_path=\"input_video.mp4\",\n target_video_path=\"output_video.mp4\",\n confidence_threshold=0.3,\n iou_threshold=0.7\n)\n\n# Customize further configurations if needed\nodin.tracker.set_max_distance(50)\nodin.box_annotator.set_box_color((0, 255, 0))\nodin.model.set_device(\"cuda\")\n\n# Run the object to process the video\nodin.run()\n```\n\n# Architecture\n* [Odin utilizes YoloV7, weights can be downloaded here](https://drive.google.com/file/d/1yEYFq1jCIpklofMMhuqQKwyTfvj1hLQ1/view)\n\n# License\nMIT\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Odin - Pytorch",
"version": "0.0.3",
"project_urls": {
"Homepage": "https://github.com/kyegomez/odin",
"Repository": "https://github.com/kyegomez/odin"
},
"split_keywords": [
"artificial intelligence",
"deep learning",
"optimizers",
"prompt engineering"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "a92185d3d5da9008c8c577f6b141273aea4c849fb2df0a69230c83b6da05beda",
"md5": "ef8b6ac9a87239dd5d159aa81acab217",
"sha256": "6db4c23b8d1a098bd1b8db624ed1a36bab290255d37dfebdadc3f37a295f9f0b"
},
"downloads": -1,
"filename": "vodin-0.0.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "ef8b6ac9a87239dd5d159aa81acab217",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6,<4.0",
"size": 7169,
"upload_time": "2023-10-09T18:52:55",
"upload_time_iso_8601": "2023-10-09T18:52:55.088523Z",
"url": "https://files.pythonhosted.org/packages/a9/21/85d3d5da9008c8c577f6b141273aea4c849fb2df0a69230c83b6da05beda/vodin-0.0.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4284b67789c59c44c1fcb58656209cc26c386657bc2a9aeeda83dc65ba4777cd",
"md5": "2e18aaed05a60ebf36aa34d4a7320ca2",
"sha256": "391f047cab668db60713f59619ee9f39830dbeaf4ac390b86c4e24caabe4337c"
},
"downloads": -1,
"filename": "vodin-0.0.3.tar.gz",
"has_sig": false,
"md5_digest": "2e18aaed05a60ebf36aa34d4a7320ca2",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6,<4.0",
"size": 6406,
"upload_time": "2023-10-09T18:52:56",
"upload_time_iso_8601": "2023-10-09T18:52:56.625027Z",
"url": "https://files.pythonhosted.org/packages/42/84/b67789c59c44c1fcb58656209cc26c386657bc2a9aeeda83dc65ba4777cd/vodin-0.0.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-09 18:52:56",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "kyegomez",
"github_project": "odin",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "supervision",
"specs": []
},
{
"name": "tqdm",
"specs": []
},
{
"name": "ultranalytics",
"specs": []
}
],
"lcname": "vodin"
}