a library for meter detection
Project description
Detection model for Reading Area Detection
Detection models for meter reading area.
We use stanford-extra dataset as the basic dataset.
Install
pip install meter_detection
Install keras and torch by yourself.
Features
- To train with stanford-extra dataset, using
examples/notebooks/keypoint_detection.ipynb. - train with meter dataset, just run
examples/notebooks/meter_detection.ipynb. - I want to use keras as the deep learning framework to build the detection model.
The detection model is planed to be based on
mobilenetv2with pretrained weights.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
meter_detection-0.2.0.tar.gz
(6.9 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file meter_detection-0.2.0.tar.gz.
File metadata
- Download URL: meter_detection-0.2.0.tar.gz
- Upload date:
- Size: 6.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.9.21
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
0da390ca2c73b43b9afaf67e78a28f398ffebd0bb8988e6deeea4fabbe87e4f1
|
|
| MD5 |
9a9522875d6d9dd98ae7faae8d35f3ce
|
|
| BLAKE2b-256 |
525339990258f26ed19e2a4e9011571cb72877ab573ad75fa0f96e64bf41d7ae
|
File details
Details for the file meter_detection-0.2.0-py3-none-any.whl.
File metadata
- Download URL: meter_detection-0.2.0-py3-none-any.whl
- Upload date:
- Size: 11.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.0.1 CPython/3.9.21
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
215aee354e1f4309cb257d594f4b4fbcbb1507c7eb2e8435830ef2a8b0520c04
|
|
| MD5 |
ff6fd89f2b5183d3090c3d7bc3993acb
|
|
| BLAKE2b-256 |
a69a1a2bd66bf1b3269823c4a920aae03d46b2ca0477de42fbb36636450549d9
|
