Google-ViT-Base-Patch16-224 Open-Source Cartoon Face Recognition Model - Excellently Completes Image Classification Tasks

Google Vit Base Patch16 224 Cartoon Face Recognition

Developed by jayanta

A cartoon face recognition model fine-tuned based on the Google Vision Transformer (ViT) architecture, excelling in image classification tasks

Face-related

Transformers

Open Source License:Apache-2.0 #Cartoon Face Recognition #High-precision Classification #ViT Fine-tuning

Downloads 62

Release Time : 1/18/2023

Model Overview

This model is a fine-tuned version of google/vit-base-patch16-224 on an image folder dataset, specifically designed for cartoon face recognition tasks. It achieved over 90% accuracy on the evaluation set.

Model Features

High-precision Recognition

Achieves 90.05% accuracy and 90.66% precision in cartoon face recognition tasks

Based on ViT Architecture

Utilizes the Vision Transformer architecture with powerful image feature extraction capabilities

Efficient Fine-tuning

Fine-tuned on the base model with high training efficiency and relatively low resource consumption

Model Capabilities

Cartoon Face Recognition

Image Classification

Feature Extraction

Use Cases

Entertainment Applications

Cartoon Character Recognition

Identify characters in animations or comics

90.05% accuracy

Expression Classification

Classify and recognize expressions of cartoon characters

Security Verification

Cartoon Avatar Verification

Verify if user-uploaded cartoon avatars meet requirements

🚀 google-vit-base-patch16-224-cartoon-face-recognition

This model is a fine - tuned version of google/vit-base-patch16-224 on the imagefolder dataset, which can be used for cartoon face recognition tasks and achieves high accuracy.

🚀 Quick Start

This model is a fine - tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3707
Accuracy: 0.9005
Precision: 0.9066
Recall: 0.9005
F1: 0.8984

📚 Documentation

Model description

More information needed

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: 0.00012
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0.89	6	0.5459	0.8611	0.8683	0.8611	0.8577
0.0812	1.89	12	0.4703	0.8796	0.8833	0.8796	0.8764
0.0812	2.89	18	0.4430	0.8935	0.8969	0.8935	0.8906
0.0307	3.89	24	0.4045	0.8819	0.8849	0.8819	0.8767
0.0091	4.89	30	0.3672	0.9005	0.9025	0.9005	0.8980
0.0091	5.89	36	0.3841	0.9028	0.9125	0.9028	0.9011
0.0043	6.89	42	0.3926	0.9005	0.9073	0.9005	0.8972
0.0043	7.89	48	0.3786	0.8958	0.9005	0.8958	0.8931
0.0031	8.89	54	0.3791	0.9028	0.9091	0.9028	0.9007
0.002	9.89	60	0.3677	0.9028	0.9106	0.9028	0.9001
0.002	10.89	66	0.3740	0.9028	0.9099	0.9028	0.9007
0.0027	11.89	72	0.3869	0.8981	0.9043	0.8981	0.8956
0.0027	12.89	78	0.3801	0.8981	0.9021	0.8981	0.8954
0.004	13.89	84	0.3674	0.9051	0.9113	0.9051	0.9028
0.0024	14.89	90	0.3620	0.9051	0.9096	0.9051	0.9027
0.0024	15.89	96	0.3670	0.9028	0.9089	0.9028	0.9006
0.0021	16.89	102	0.3827	0.9005	0.9065	0.9005	0.8980
0.0021	17.89	108	0.3748	0.8981	0.9049	0.8981	0.8958
0.0022	18.89	114	0.3825	0.9028	0.9101	0.9028	0.9006
0.0019	19.89	120	0.3707	0.9005	0.9066	0.9005	0.8984

Framework versions

Transformers 4.24.0.dev0
Pytorch 1.11.0+cu102
Datasets 2.6.1
Tokenizers 0.13.1

📄 License

This model is licensed under the Apache - 2.0 license.

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