Swin-Tiny-Patch4-Window7-224-Finetuned-Fluro_Cls Open-Source Image Classification Model

Swin Tiny Patch4 Window7 224 Finetuned Fluro Cls

Developed by zlgao

Fine-tuned model based on Swin Transformer Tiny architecture for image classification tasks

Open Source License:Apache-2.0 #High-precision image classification #Small window attention mechanism #Transfer learning optimization

Downloads 19

Release Time : 1/6/2023

Model Overview

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224, specifically designed for particular image classification tasks. It achieved an accuracy of 1.0 on the evaluation set.

Model Features

High Accuracy

Achieved 100% classification accuracy on the evaluation set

Based on Swin Transformer

Utilizes the advanced Swin Transformer architecture with excellent image feature extraction capabilities

Efficient Fine-tuning

Achieved outstanding performance with just 30 training epochs, featuring well-optimized learning rate scheduling

Model Capabilities

Image Classification

Visual Feature Extraction

Use Cases

Image Recognition

Domain-specific Image Classification

Can be used for domain-specific image classification tasks

Evaluation accuracy 1.0

🚀 swin-tiny-patch4-window7-224-finetuned-fluro_cls

This model is a fine - tuned version of microsoft/swin-tiny-patch4-window7-224 on the None dataset. It can achieve high accuracy in relevant tasks, with a loss of 0.0000 and an accuracy of 1.0 on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of microsoft/swin-tiny-patch4-window7-224 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0000
Accuracy: 1.0

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.67	1	0.7112	0.5238
No log	1.67	2	0.5591	0.8571
0.811	2.67	3	0.3781	0.9524
0.811	3.67	4	0.1995	1.0
0.811	4.67	5	0.1215	1.0
0.3531	5.67	6	0.0578	1.0
0.3531	6.67	7	0.0195	1.0
0.3531	7.67	8	0.0072	1.0
0.0618	8.67	9	0.0030	1.0
0.0618	9.67	10	0.0012	1.0
0.0618	10.67	11	0.0005	1.0
0.0079	11.67	12	0.0003	1.0
0.0079	12.67	13	0.0001	1.0
0.0079	13.67	14	0.0001	1.0
0.0051	14.67	15	0.0001	1.0
0.0051	15.67	16	0.0000	1.0
0.0051	16.67	17	0.0000	1.0
0.0017	17.67	18	0.0000	1.0
0.0017	18.67	19	0.0000	1.0
0.0017	19.67	20	0.0000	1.0
0.0004	20.67	21	0.0000	1.0
0.0004	21.67	22	0.0000	1.0
0.0004	22.67	23	0.0000	1.0
0.0022	23.67	24	0.0000	1.0
0.0022	24.67	25	0.0000	1.0
0.0022	25.67	26	0.0000	1.0
0.001	26.67	27	0.0000	1.0
0.001	27.67	28	0.0000	1.0
0.001	28.67	29	0.0000	1.0
0.0013	29.67	30	0.0000	1.0

Framework versions

Transformers 4.25.1
Pytorch 1.10.2+cu113
Datasets 2.7.1
Tokenizers 0.13.2

📄 License

This model is licensed under the Apache - 2.0 license.

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