Delivery Truck Classification Open-Source Image Classification Model - Accurately Identify Delivery Trucks with a 97.33% Accuracy Rate

Delivery Truck Classification

Developed by JEdward7777

An image classification model fine-tuned based on Swin-Tiny architecture, specifically designed for express truck recognition with an accuracy rate of 97.33%

Image Classification

Transformers

Open Source License:Apache-2.0 #Express Vehicle Recognition #High-precision Classification #Swin-Transformer

Downloads 21

Release Time : 8/28/2022

Model Overview

This model is an image classification model fine-tuned based on microsoft/swin-tiny-patch4-window7-224, primarily used for express truck recognition tasks. It performs excellently on the evaluation set, achieving an accuracy rate of 97.33%.

Model Features

High Accuracy

Achieves a classification accuracy of 97.33% on the test set

Based on Swin Transformer

Utilizes the advanced Swin Transformer architecture with excellent visual feature extraction capabilities

Lightweight Model

Based on the Tiny version architecture, suitable for deployment in resource-constrained environments

Model Capabilities

Image Classification

Express Truck Recognition

Visual Feature Extraction

Use Cases

Logistics Management

Automatic Express Vehicle Recognition

Used in automatic vehicle recognition systems at logistics parks or warehouse entrances

Achieves an accurate recognition rate of 97.33%

Transportation Monitoring

Automatic classification and statistics of express vehicles in monitoring systems

🚀 delivery_truck_classification

This model is a fine - tuned version of [microsoft/swin - tiny - patch4 - window7 - 224](https://huggingface.co/microsoft/swin - tiny - patch4 - window7 - 224) on the imagefolder dataset. It can accurately classify delivery trucks in images, achieving high accuracy on the evaluation set.

🚀 Quick Start

This section provides basic information about the model. For more detailed usage, please refer to relevant documentation.

📚 Documentation

Model description

This model is a fine - tuned version of [microsoft/swin - tiny - patch4 - window7 - 224](https://huggingface.co/microsoft/swin - tiny - patch4 - window7 - 224) on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1787
Accuracy: 0.9733

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.91	5	0.1787	0.9733
No log	1.91	10	0.1787	0.9733
No log	2.91	15	0.1787	0.9733
0.3799	3.91	20	0.1787	0.9733
0.3799	4.91	25	0.1787	0.9733
0.3799	5.91	30	0.1787	0.9733
0.3799	6.91	35	0.1787	0.9733
0.3648	7.91	40	0.1787	0.9733
0.3648	8.91	45	0.1787	0.9733
0.3648	9.91	50	0.1787	0.9733
0.3648	10.91	55	0.1787	0.9733
0.3954	11.91	60	0.1787	0.9733
0.3954	12.91	65	0.1787	0.9733
0.3954	13.91	70	0.1787	0.9733
0.3954	14.91	75	0.1787	0.9733
0.3926	15.91	80	0.1787	0.9733
0.3926	16.91	85	0.1787	0.9733
0.3926	17.91	90	0.1787	0.9733
0.3926	18.91	95	0.1787	0.9733
0.3801	19.91	100	0.1787	0.9733
0.3801	20.91	105	0.1787	0.9733
0.3801	21.91	110	0.1787	0.9733
0.3801	22.91	115	0.1787	0.9733
0.3815	23.91	120	0.1787	0.9733
0.3815	24.91	125	0.1787	0.9733
0.3815	25.91	130	0.1787	0.9733
0.3815	26.91	135	0.1787	0.9733
0.3955	27.91	140	0.1787	0.9733
0.3955	28.91	145	0.1787	0.9733
0.3955	29.91	150	0.1787	0.9733
0.3955	30.91	155	0.1787	0.9733
0.3854	31.91	160	0.1787	0.9733
0.3854	32.91	165	0.1787	0.9733
0.3854	33.91	170	0.1787	0.9733
0.3854	34.91	175	0.1787	0.9733
0.3949	35.91	180	0.1787	0.9733
0.3949	36.91	185	0.1787	0.9733
0.3949	37.91	190	0.1787	0.9733
0.3949	38.91	195	0.1787	0.9733
0.423	39.91	200	0.1787	0.9733
0.423	40.91	205	0.1787	0.9733
0.423	41.91	210	0.1787	0.9733
0.423	42.91	215	0.1787	0.9733
0.3761	43.91	220	0.1787	0.9733
0.3761	44.91	225	0.1787	0.9733
0.3761	45.91	230	0.1787	0.9733
0.3761	46.91	235	0.1787	0.9733
0.3673	47.91	240	0.1787	0.9733
0.3673	48.91	245	0.1787	0.9733
0.3673	49.91	250	0.1787	0.9733
0.3673	50.91	255	0.1787	0.9733
0.3639	51.91	260	0.1787	0.9733
0.3639	52.91	265	0.1787	0.9733
0.3639	53.91	270	0.1787	0.9733
0.3639	54.91	275	0.1787	0.9733
0.4031	55.91	280	0.1787	0.9733
0.4031	56.91	285	0.1787	0.9733
0.4031	57.91	290	0.1787	0.9733
0.4031	58.91	295	0.1787	0.9733
0.3787	59.91	300	0.1787	0.9733

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2

📄 License

This model is licensed under the Apache - 2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご