# vc-bantai-vit-withoutAMBI-adunest-v1 Open-source Image Classification Model

Vc Bantai Vit Withoutambi Adunest V1

Developed by AykeeSalazar

High-precision image classification model fine-tuned based on Google's ViT-base model, achieving 91.81% accuracy on the evaluation set

Image Classification

Transformers

Open Source License:Apache-2.0 #High-precision image classification #ViT fine-tuned model #Inappropriate content detection

Downloads 28

Release Time : 7/28/2022

Model Overview

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an image folder dataset, primarily used for image classification tasks.

Model Features

High accuracy

Achieved 91.81% accuracy on the evaluation set

Based on ViT architecture

Utilizes Vision Transformer architecture with powerful image feature extraction capabilities

Fine-tuned optimization

Trained for 200 epochs using Adam optimizer with linear learning rate scheduling

Model Capabilities

Image classification

Visual feature extraction

Use Cases

Image recognition

Inappropriate content classification

Can be used to identify and classify inappropriate image content

Performs well on the Violation-Classification dataset

🚀 vc-bantai-vit-withoutAMBI-adunest-v1

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It offers high - performance image classification capabilities, achieving remarkable results on the evaluation set.

📚 Documentation

Model Information

Property	Details
Model Type	Fine - tuned version of google/vit-base-patch16-224-in21k
Training Data	imagefolder dataset
Metrics	Loss: 0.3318; Accuracy: 0.9181

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	0.23	100	0.3365	0.8581
No log	0.45	200	0.3552	0.8472
No log	0.68	300	0.3165	0.8581
No log	0.91	400	0.2882	0.8690
0.3813	1.13	500	0.2825	0.8745
0.3813	1.36	600	0.2686	0.9007
0.3813	1.59	700	0.2381	0.9017
0.3813	1.81	800	0.3643	0.8734
0.3813	2.04	900	0.2873	0.8930
0.2736	2.27	1000	0.2236	0.9039
0.2736	2.49	1100	0.2652	0.8723
0.2736	2.72	1200	0.2793	0.8952
0.2736	2.95	1300	0.2158	0.8974
0.2736	3.17	1400	0.2410	0.8886
0.2093	3.4	1500	0.2262	0.9017
0.2093	3.63	1600	0.2110	0.9214
0.2093	3.85	1700	0.2048	0.9138
0.2093	4.08	1800	0.2044	0.9127
0.2093	4.31	1900	0.2591	0.9007
0.1764	4.54	2000	0.2466	0.8952
0.1764	4.76	2100	0.2554	0.9017
0.1764	4.99	2200	0.2145	0.9203
0.1764	5.22	2300	0.3187	0.9039
0.1764	5.44	2400	0.3336	0.9050
0.1454	5.67	2500	0.2542	0.9127
0.1454	5.9	2600	0.2796	0.8952
0.1454	6.12	2700	0.2410	0.9181
0.1454	6.35	2800	0.2503	0.9148
0.1454	6.58	2900	0.2966	0.8996
0.1216	6.8	3000	0.1978	0.9312
0.1216	7.03	3100	0.2297	0.9214
0.1216	7.26	3200	0.2768	0.9203
0.1216	7.48	3300	0.3356	0.9083
0.1216	7.71	3400	0.3415	0.9138
0.1038	7.94	3500	0.2398	0.9061
0.1038	8.16	3600	0.3347	0.8963
0.1038	8.39	3700	0.2199	0.9203
0.1038	8.62	3800	0.2943	0.9061
0.1038	8.84	3900	0.2561	0.9181
0.0925	9.07	4000	0.4170	0.8777
0.0925	9.3	4100	0.3638	0.8974
0.0925	9.52	4200	0.3233	0.9094
0.0925	9.75	4300	0.3496	0.9203
0.0925	9.98	4400	0.3621	0.8996
0.0788	10.2	4500	0.3260	0.9116
0.0788	10.43	4600	0.3979	0.9061
0.0788	10.66	4700	0.3301	0.8974
0.0788	10.88	4800	0.2197	0.9105
0.0788	11.11	4900	0.3306	0.9148
0.0708	11.34	5000	0.3318	0.9181

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.

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