Open-source Image Classification Model cvt-13-384-in22k-FV-finetuned-memes

Cvt 13 384 In22k FV Finetuned Memes

Developed by jayanta

An image classification model fine-tuned on the image folder dataset based on microsoft/cvt-13-384-22k, achieving an accuracy of 83.46% on the evaluation set

Image Classification

Transformers

Open Source License:Apache-2.0 #High-precision image classification #Meme recognition #Transfer learning optimization

Downloads 11

Release Time : 10/21/2022

Model Overview

This model is a variant of Microsoft's CVT architecture, specifically fine-tuned for image classification tasks, particularly suitable for meme classification scenarios.

Model Features

High accuracy

Achieves 83.46% accuracy in image classification tasks

Based on CVT architecture

Utilizes Microsoft's CVT vision transformer architecture with powerful feature extraction capabilities

Fine-tuning optimization

Precisely optimized on specific datasets to enhance performance in particular scenarios

Model Capabilities

Image classification

Feature extraction

Visual pattern recognition

Use Cases

Content classification

Meme classification

Automatically classify internet memes

83.46% accuracy

Content moderation

Image content recognition

Identify specific content categories in images

🚀 cvt-13-384-in22k-FV-finetuned-memes

This model is a fine - tuned version of microsoft/cvt-13-384-22k on the imagefolder dataset. It offers high - performance image classification capabilities, achieving excellent results on the evaluation set.

📚 Documentation

Model Information

Property	Details
Model Type	Fine - tuned version of microsoft/cvt-13-384-22k
Training Data	imagefolder dataset
Metrics	Accuracy, Precision, Recall, F1

Model Performance

This model achieves the following results on the evaluation set:

Loss: 0.5595
Accuracy: 0.8346
Precision: 0.8327
Recall: 0.8346
F1: 0.8322

Training and Evaluation

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
1.4066	0.99	20	1.2430	0.5124	0.5141	0.5124	0.4371
1.0813	1.99	40	0.8244	0.6893	0.6834	0.6893	0.6616
0.8392	2.99	60	0.6334	0.7612	0.7670	0.7612	0.7570
0.7065	3.99	80	0.5819	0.7767	0.7799	0.7767	0.7672
0.5751	4.99	100	0.5365	0.8176	0.8216	0.8176	0.8130
0.4896	5.99	120	0.4943	0.8308	0.8257	0.8308	0.8265
0.4487	6.99	140	0.5399	0.8107	0.8069	0.8107	0.8054
0.4349	7.99	160	0.4892	0.8300	0.8285	0.8300	0.8273
0.43	8.99	180	0.4984	0.8454	0.8465	0.8454	0.8426
0.4372	9.99	200	0.5573	0.8192	0.8221	0.8192	0.8157
0.3994	10.99	220	0.5158	0.8300	0.8284	0.8300	0.8281
0.3883	11.99	240	0.5495	0.8354	0.8317	0.8354	0.8314
0.406	12.99	260	0.5298	0.8284	0.8285	0.8284	0.8246
0.3355	13.99	280	0.5401	0.8393	0.8346	0.8393	0.8357
0.395	14.99	300	0.5915	0.8308	0.8278	0.8308	0.8261
0.3612	15.99	320	0.5852	0.8408	0.8378	0.8408	0.8368
0.3765	16.99	340	0.5509	0.8385	0.8351	0.8385	0.8356
0.3688	17.99	360	0.5668	0.8416	0.8398	0.8416	0.8387
0.3503	18.99	380	0.5626	0.8393	0.8371	0.8393	0.8365
0.3611	19.99	400	0.5595	0.8346	0.8327	0.8346	0.8322

Framework versions

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

📄 License

This model is licensed under the Apache 2.0 license.

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