vc-bantai-vit-withoutAMBI-adunest-v3 Open-source Image Classification Model - Custom Fine-tuning Accuracy Reaches 82.18%

Vc Bantai Vit Withoutambi Adunest V3

Developed by AykeeSalazar

An image classification model based on Google Vision Transformer (ViT) architecture, fine-tuned on a custom dataset with an accuracy of 82.18%

Image Classification

Transformers

Open Source License:Apache-2.0 #Image Classification #ViT Fine-tuning #High Accuracy

Downloads 30

Release Time : 8/3/2022

Model Overview

This model is a fine-tuned version of the google/vit-base-patch16-224-in21k pre-trained model for image classification tasks, primarily used for image classification.

Model Features

High Accuracy

Achieves an accuracy of 82.18% on the evaluation set, demonstrating excellent performance

Based on ViT Architecture

Utilizes the Vision Transformer architecture, effectively capturing global features in images

Fine-tuned Optimization

Trained for 200 epochs using linear learning rate scheduling and the Adam optimizer

Model Capabilities

Image Classification

Visual Feature Extraction

Use Cases

Image Recognition

Violation Content Classification

Can be used to identify and classify images with prohibited content

Performs well on the Violation-Classification dataset

🚀 vc-bantai-vit-withoutAMBI-adunest-v3

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It can be used for image classification tasks and achieves certain performance on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.8889
Accuracy: 0.8218

📚 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: 0.0005
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 200
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.38	100	0.8208	0.7147
No log	0.76	200	0.8861	0.7595
No log	1.14	300	0.4306	0.7910
No log	1.52	400	0.5222	0.8245
0.3448	1.9	500	0.8621	0.7602
0.3448	2.28	600	0.2902	0.8801
0.3448	2.66	700	0.3687	0.8426
0.3448	3.04	800	0.3585	0.8694
0.3448	3.42	900	0.6546	0.7897
0.2183	3.8	1000	0.3881	0.8272
0.2183	4.18	1100	0.9650	0.7709
0.2183	4.56	1200	0.6444	0.7917
0.2183	4.94	1300	0.4685	0.8707
0.2183	5.32	1400	0.4972	0.8506
0.157	5.7	1500	0.4010	0.8513
0.157	6.08	1600	0.4629	0.8419
0.157	6.46	1700	0.4258	0.8714
0.157	6.84	1800	0.4383	0.8573
0.157	7.22	1900	0.5324	0.8493
0.113	7.6	2000	0.3212	0.8942
0.113	7.98	2100	0.8621	0.8326
0.113	8.37	2200	0.6050	0.8131
0.113	8.75	2300	0.7173	0.7991
0.113	9.13	2400	0.5313	0.8125
0.0921	9.51	2500	0.6584	0.8158
0.0921	9.89	2600	0.8727	0.7930
0.0921	10.27	2700	0.4222	0.8922
0.0921	10.65	2800	0.5811	0.8265
0.0921	11.03	2900	0.6175	0.8372
0.0701	11.41	3000	0.3914	0.8835
0.0701	11.79	3100	0.3364	0.8654
0.0701	12.17	3200	0.6223	0.8359
0.0701	12.55	3300	0.7830	0.8125
0.0701	12.93	3400	0.4356	0.8942
0.0552	13.31	3500	0.7553	0.8232
0.0552	13.69	3600	0.9107	0.8292
0.0552	14.07	3700	0.6108	0.8580
0.0552	14.45	3800	0.5732	0.8567
0.0552	14.83	3900	0.5087	0.8614
0.0482	15.21	4000	0.8889	0.8218

Framework versions

Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1

📄 License

This model is licensed under the Apache - 2.0 license.

Model Index

Property	Details
Model Name	vc - bantai - vit - withoutAMBI - adunest - v3
Task	Image Classification
Dataset	imagefolder (args: Violation - Classification---Raw - 10)
Metrics	Accuracy: 0.8218352310783658

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