exper3_mesum5 Open-Source Image Classification Model - Fine-tuned on a Specific Dataset, Evaluation Accuracy Exceeds 83%

Exper3 Mesum5

Developed by sudo-s

This model is an image classification model fine-tuned on the sudo-s/herbier_mesuem5 dataset based on google/vit-base-patch16-224-in21k, achieving an evaluation accuracy of 83.67%.

Image Classification

Transformers

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

Downloads 29

Release Time : 7/22/2022

Model Overview

An image classification model based on the ViT architecture, fine-tuned on a specific dataset, primarily used for image classification tasks.

Model Features

High Accuracy

Achieves 83.67% accuracy on the evaluation set.

Based on ViT Architecture

Utilizes the Vision Transformer architecture, which excels in image feature extraction.

Fine-tuned

Underwent 8 epochs of fine-tuning on a specific dataset.

Model Capabilities

Image Classification

Feature Extraction

Use Cases

Image Recognition

Plant Image Classification

Can be used for classifying and identifying plant specimen images.

Accuracy: 83.67%

🚀 exper3_mesum5

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on the sudo - s/herbier_mesuem5 dataset. It offers a solution for image classification tasks and achieves high accuracy on the evaluation set, providing reliable performance for related applications.

🚀 Quick Start

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on the sudo - s/herbier_mesuem5 dataset. It achieves the following results on the evaluation set:

Loss: 0.6366
Accuracy: 0.8367

📚 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:

Property	Details
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	8
mixed_precision_training	Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.895	0.23	100	3.8276	0.1935
3.1174	0.47	200	3.1217	0.3107
2.6	0.7	300	2.5399	0.4207
2.256	0.93	400	2.1767	0.5160
1.5441	1.16	500	1.8086	0.5852
1.3834	1.4	600	1.5565	0.6325
1.1995	1.63	700	1.3339	0.6763
1.0845	1.86	800	1.3299	0.6533
0.6472	2.09	900	1.0679	0.7219
0.5948	2.33	1000	1.0286	0.7124
0.5565	2.56	1100	0.9595	0.7284
0.4879	2.79	1200	0.8915	0.7420
0.2816	3.02	1300	0.8159	0.7763
0.2412	3.26	1400	0.7766	0.7911
0.2015	3.49	1500	0.7850	0.7828
0.274	3.72	1600	0.7361	0.7935
0.1244	3.95	1700	0.7299	0.7911
0.0794	4.19	1800	0.7441	0.7846
0.0915	4.42	1900	0.7614	0.7941
0.0817	4.65	2000	0.7310	0.8012
0.0561	4.88	2100	0.7222	0.8065
0.0165	5.12	2200	0.7515	0.8059
0.0168	5.35	2300	0.6687	0.8213
0.0212	5.58	2400	0.6671	0.8249
0.0389	5.81	2500	0.6893	0.8278
0.0087	6.05	2600	0.6839	0.8260
0.0087	6.28	2700	0.6412	0.8320
0.0077	6.51	2800	0.6366	0.8367
0.0065	6.74	2900	0.6697	0.8272
0.0061	6.98	3000	0.6510	0.8349
0.0185	7.21	3100	0.6452	0.8367
0.0059	7.44	3200	0.6426	0.8379
0.0062	7.67	3300	0.6398	0.8379
0.0315	7.91	3400	0.6397	0.8385

Framework versions

Property	Details
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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