exper6_mesum5 Open-source Image Classification Model - Accurately Identify Images in the herbier

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Exper6 Mesum5

Developed by sudo-s

Image classification model fine-tuned on the herbier_mesuem5 dataset based on google/vit-base-patch16-224-in21k

Image Classification

Transformers

Open Source License:Apache-2.0 #Plant Image Classification #ViT Fine-tuned Model #High-precision Recognition

Downloads 28

Release Time : 7/22/2022

Model Overview

This model is an image classification model based on the Vision Transformer (ViT) architecture, specifically fine-tuned for plant specimen image classification tasks.

Model Features

High Accuracy

Achieves an accuracy of 80.36% on the evaluation set, demonstrating excellent performance

Based on ViT Architecture

Utilizes the Vision Transformer architecture, effectively capturing global image features

Few-shot Fine-tuning

Effectively fine-tuned on a specific domain dataset

Model Capabilities

Image Classification

Plant Specimen Recognition

Visual Feature Extraction

Use Cases

Botanical Research

Automatic Plant Specimen Classification

Automatically identifies and classifies plant specimen images

80.36% accuracy

Museum Digitization

Digital Management of Specimen Collections

Assists museums in the digital classification management of plant specimens

🚀 exper6_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 can be used for image classification tasks and achieves the following results on the evaluation set:

Loss: 0.8241
Accuracy: 0.8036

📦 Model Information

Property	Details
Model Type	exper6_mesum5
License	Apache - 2.0
Tags	image - classification, generated_from_trainer
Metrics	accuracy

🔧 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: 16
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.9276	0.23	100	3.8550	0.2089
3.0853	0.47	200	3.1106	0.3414
2.604	0.7	300	2.5732	0.4379
2.3183	0.93	400	2.2308	0.4882
1.5326	1.16	500	1.7903	0.5828
1.3367	1.4	600	1.5524	0.6349
1.1544	1.63	700	1.3167	0.6645
1.0788	1.86	800	1.3423	0.6385
0.6762	2.09	900	1.0780	0.7124
0.6483	2.33	1000	1.0090	0.7284
0.6321	2.56	1100	1.0861	0.7024
0.5558	2.79	1200	0.9933	0.7183
0.342	3.02	1300	0.8871	0.7462
0.2964	3.26	1400	0.9330	0.7408
0.1959	3.49	1500	0.9367	0.7343
0.368	3.72	1600	0.8472	0.7550
0.1821	3.95	1700	0.8937	0.7568
0.1851	4.19	1800	0.9546	0.7485
0.1648	4.42	1900	0.9790	0.7355
0.172	4.65	2000	0.8947	0.7627
0.0928	4.88	2100	1.0093	0.7462
0.0699	5.12	2200	0.8374	0.7639
0.0988	5.35	2300	0.9189	0.7645
0.0822	5.58	2400	0.9512	0.7580
0.1223	5.81	2500	1.0809	0.7349
0.0509	6.05	2600	0.9297	0.7769
0.0511	6.28	2700	0.8981	0.7822
0.0596	6.51	2800	0.9468	0.7704
0.0494	6.74	2900	0.9045	0.7870
0.0643	6.98	3000	1.1559	0.7391
0.0158	7.21	3100	0.8450	0.7899
0.0129	7.44	3200	0.8241	0.8036
0.0441	7.67	3300	0.9679	0.7751
0.0697	7.91	3400	1.0387	0.7751
0.0084	8.14	3500	0.9441	0.7947
0.0182	8.37	3600	0.8967	0.7994
0.0042	8.6	3700	0.8750	0.8041
0.0028	8.84	3800	0.9349	0.8041
0.0053	9.07	3900	0.9403	0.7982
0.0266	9.3	4000	0.9966	0.7959
0.0022	9.53	4100	0.9472	0.8018
0.0018	9.77	4200	0.8717	0.8136
0.0018	10.0	4300	0.8964	0.8083
0.0046	10.23	4400	0.8623	0.8160
0.0037	10.47	4500	0.8762	0.8172
0.0013	10.7	4600	0.9028	0.8142
0.0013	10.93	4700	0.9084	0.8178
0.0013	11.16	4800	0.8733	0.8213
0.001	11.4	4900	0.8823	0.8207
0.0009	11.63	5000	0.8769	0.8213
0.0282	11.86	5100	0.8791	0.8219
0.001	12.09	5200	0.8673	0.8249
0.0016	12.33	5300	0.8633	0.8225
0.0008	12.56	5400	0.8766	0.8195
0.0008	12.79	5500	0.8743	0.8225
0.0008	13.02	5600	0.8752	0.8231
0.0008	13.26	5700	0.8676	0.8237
0.0007	13.49	5800	0.8677	0.8237
0.0008	13.72	5900	0.8703	0.8237
0.0007	13.95	6000	0.8725	0.8237
0.0006	14.19	6100	0.8741	0.8231
0.0006	14.42	6200	0.8758	0.8237
0.0008	14.65	6300	0.8746	0.8243
0.0007	14.88	6400	0.8759	0.8243
0.0007	15.12	6500	0.8803	0.8231
0.0007	15.35	6600	0.8808	0.8237
0.0007	15.58	6700	0.8798	0.8243
0.0007	15.81	6800	0.8805	0.8243

Framework versions

Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1

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