Open-source finetuned-ViT-Indian-Food-Classification-v3 Model - Achieve High-precision Image Classification

Finetuned ViT Indian Food Classification V3

Developed by DrishtiSharma

This model is a fine-tuned image classification model based on google/vit-base-patch16-224-in21k on the Human_Action_Recognition dataset, achieving an accuracy of 93.84%.

Image Classification

Transformers

Open Source License:Apache-2.0 #High-precision image classification #Fine-tuned ViT model #Action recognition

Downloads 60

Release Time : 9/3/2022

Model Overview

This is an image classification model based on the Vision Transformer (ViT) architecture, specifically designed for recognizing Indian food categories. The model has been fine-tuned on the Human_Action_Recognition dataset and performs excellently.

Model Features

High accuracy

Achieves 93.84% accuracy on the evaluation set, demonstrating excellent performance.

Based on ViT architecture

Utilizes the advanced Vision Transformer architecture, effectively capturing global image features.

Efficient fine-tuning

Efficiently fine-tuned on a pre-trained model, saving training resources.

Model Capabilities

Image classification

Food recognition

Visual feature extraction

Use Cases

Food and beverage industry

Automatic dish recognition

Used in automatic dish classification systems for restaurants or food delivery platforms

Accurately identifies various Indian food items

Health applications

Diet recording assistance

Mobile applications that help users automatically record their dietary intake

Automatically identifies food types, simplifying the recording process

🚀 finetuned-ViT-Indian-Food-Classification-v3

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the Human_Action_Recognition dataset, achieving high accuracy in image classification tasks.

🚀 Quick Start

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

Loss: 0.2878
Accuracy: 0.9384

📚 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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1913	0.3	100	0.9307	0.8395
0.6846	0.6	200	0.5650	0.8852
0.5783	0.9	300	0.5147	0.8895
0.5635	1.2	400	0.5310	0.8650
0.4487	1.5	500	0.4155	0.8980
0.2803	1.8	600	0.3848	0.9012
0.4496	2.1	700	0.4308	0.8852
0.4071	2.4	800	0.4004	0.8905
0.3747	2.7	900	0.3795	0.8927
0.2665	3.0	1000	0.3618	0.8927
0.3696	3.3	1100	0.3588	0.8990
0.2808	3.6	1200	0.3794	0.8884
0.158	3.9	1300	0.3416	0.9054
0.2062	4.2	1400	0.3686	0.8916
0.2039	4.5	1500	0.3219	0.9118
0.2392	4.8	1600	0.3392	0.9086
0.1276	5.11	1700	0.3249	0.9192
0.1812	5.41	1800	0.2970	0.9245
0.1352	5.71	1900	0.3366	0.9118
0.1333	6.01	2000	0.3111	0.9203
0.189	6.31	2100	0.3604	0.9139
0.1048	6.61	2200	0.3496	0.9171
0.0913	6.91	2300	0.3046	0.9224
0.1678	7.21	2400	0.3154	0.9288
0.0705	7.51	2500	0.3229	0.9235
0.1057	7.81	2600	0.2895	0.9330
0.1219	8.11	2700	0.2984	0.9299
0.0521	8.41	2800	0.3083	0.9288
0.1181	8.71	2900	0.3020	0.9288
0.1339	9.01	3000	0.2885	0.9373
0.2393	9.31	3100	0.2895	0.9277
0.1044	9.61	3200	0.2912	0.9362
0.096	9.91	3300	0.2878	0.9384

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1

📄 License

This model is licensed under the Apache 2.0 license.

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