ONNX Model Zoo

ottoykh/Smart-Traffic

By ottoykh · ↓ 10 · ♥ 3 · Updated 2024-02-25

What's in this repo

This page lists the files in ottoykh/Smart-Traffic, including best.onnx.

  • 1 ONNX models (.onnx files)
  • 46 files, including 14 .jpg, 12 .png, 3 .json, 2 .ipynb, 2 .pt, 2 .yaml
  • Last updated 2024-02-25

ONNX files

File Size Download
best.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

Model Card for Smart-Traffic

Sample of the segmented CCTV Sample of the segmentation preformace This is a machine learning model designed for the Real-Time CCTV road traffic monitoring, use for the road traffic estimation and indexing.

Table of Contents

Model Details

Model Description

This is a machine learning model designed for the Real-Time CCTV road traffic monitoring, use for the road traffic estimation and indexing.

  • Developed by: Yu Kai Him Otto
  • Shared by [Optional]: Yu Kai Him Otto
  • Model type: Language model
  • Language(s) (NLP): en
  • License: mit
  • Parent Model: More information needed
  • Resources for more information: More information needed

Model Card Authors [optional]

Yu Kai Him Otto, Chan Ka Hin and Leung Yat Long

Model Card Contact

More information needed

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

More information needed

Model info

image-segmentation transformers
transformers pytorch tensorboard onnx yolov8-seg image-segmentation en license:mit endpoints_compatible region:us
↓ 10 ♥ 3

Created: 2024-02-23

Updated: 2024-02-25

View on Hugging Face

Files (46)

.gitattributes
README.md
Real-time time series.mp4
Real_Time_Fetching_and_instance_segmentation.ipynb
Real_Time_Traffic_CCTV_Instance_Segmentation.ipynb
Road-Traffic-4.zip
__init__.py
best.onnx ONNX
best.pt
config.json
data.yaml
map.html
preprocessor_config.json
pytorch_model.bin
result/BoxF1_curve.png
result/BoxPR_curve.png
result/BoxP_curve.png
result/BoxR_curve.png
result/MaskF1_curve.png
result/MaskPR_curve.png
result/MaskP_curve.png
result/MaskR_curve.png
result/args.yaml
result/confusion_matrix.png
result/confusion_matrix_normalized.png
result/events.out.tfevents.1708667748.a7a67d10a0c5.875.0
result/labels.jpg
result/labels_correlogram.jpg
result/model_artifacts.json
result/results.csv
result/results.png
result/state_dict.pt
result/train_batch0.jpg
result/train_batch1.jpg
result/train_batch10620.jpg
result/train_batch10621.jpg
result/train_batch10622.jpg
result/train_batch2.jpg
result/val_batch0_labels.jpg
result/val_batch0_pred.jpg
result/val_batch1_labels.jpg
result/val_batch1_pred.jpg
result/val_batch2_labels.jpg
result/val_batch2_pred.jpg
sample_seg.png
sample_site3.gif

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