semvec/pypi-packages huggingface dataset:
vodin 0.0.3
Odin - Pytorch
License: MIT
Requires Python: >=3.6,<4.0
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering

[![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
murmurv3-redux 0.6.1
MurmurV3 for Python 3.7
License: MIT


Murmur Hash Library
===================

Murmur Hash Library is a simple c level implementation developed for
high speed hashing of in memory strings, on disk files, and the contents
of zip files.

As the name implies the hashes are generated via an implementation of
`MurmurHash 2.0`_.

A few quick NOTES and WARNINGS:

The implementation of MurMur that is used in this code makes the
following assumptions about your machine:

    1. A 4-byte value can be read from any address without crashing
    2. sizeof(int) == 4

It will also not produce the same results on little-endian and big-endian
machines.

I believe it would be easily possible to get around these limitations.

.. _MurmurHash 2.0: http://murmurhash.googlepages.com/