resnet-152-fv-finetuned-memess Open Source Image Classification Model - Accuracy of Custom Dataset Reaches 76.74%

Resnet 152 Fv Finetuned Memess

Developed by jayanta

An image classification model fine-tuned based on microsoft/resnet-152, achieving 76.74% accuracy on a custom image dataset

Image Classification

Transformers

Open Source License:Apache-2.0 #Image Classification #High Accuracy #Fine-tuned Model

Downloads 23

Release Time : 10/20/2022

Model Overview

This model is a fine-tuned version based on the ResNet-152 architecture, specifically designed for image classification tasks. By fine-tuning on a specific dataset, it enhances the original model's classification performance in the target domain.

Model Features

High Accuracy

Achieves 76.74% classification accuracy on the evaluation set

Balanced Performance

Balanced performance in precision (76.51%), recall (76.74%), and F1-score (76.47%)

Transfer Learning

Fine-tuned based on the pre-trained ResNet-152 model, effectively utilizing existing feature extraction capabilities

Model Capabilities

Image Classification

Feature Extraction

Transfer Learning

Use Cases

Image Recognition

Content Classification

Classify and identify image content

Accuracy 76.74%

🚀 resnet-152-fv-finetuned-memess

This model is a fine - tuned version of microsoft/resnet-152 on the imagefolder dataset, achieving good results in image classification tasks.

🚀 Quick Start

This model is a fine - tuned version of microsoft/resnet-152 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6281
Accuracy: 0.7674
Precision: 0.7651
Recall: 0.7674
F1: 0.7647

📚 Documentation

Model Information

Property	Details
Model Type	Fine - tuned version of microsoft/resnet-152
Training Data	imagefolder dataset
Metrics	Loss: 0.6281, Accuracy: 0.7674, Precision: 0.7651, Recall: 0.7674, F1: 0.7647

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00012
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.5902	0.99	20	1.5519	0.4938	0.3491	0.4938	0.3529
1.4694	1.99	40	1.3730	0.4892	0.4095	0.4892	0.3222
1.3129	2.99	60	1.2052	0.5301	0.3504	0.5301	0.4005
1.1831	3.99	80	1.1142	0.5587	0.4077	0.5587	0.4444
1.0581	4.99	100	0.9930	0.6012	0.5680	0.6012	0.5108
0.9464	5.99	120	0.9263	0.6507	0.6200	0.6507	0.6029
0.8581	6.99	140	0.8400	0.6917	0.6645	0.6917	0.6638
0.7739	7.99	160	0.7829	0.7087	0.6918	0.7087	0.6845
0.6762	8.99	180	0.7512	0.7318	0.7206	0.7318	0.7189
0.6162	9.99	200	0.7409	0.7264	0.7244	0.7264	0.7241
0.5546	10.99	220	0.6936	0.7465	0.7429	0.7465	0.7395
0.4633	11.99	240	0.6779	0.7473	0.7393	0.7473	0.7412
0.4373	12.99	260	0.6736	0.7573	0.7492	0.7573	0.7523
0.4074	13.99	280	0.6534	0.7566	0.7516	0.7566	0.7528
0.39	14.99	300	0.6521	0.7651	0.7603	0.7651	0.7608
0.3766	15.99	320	0.6499	0.7682	0.7607	0.7682	0.7630
0.3507	16.99	340	0.6497	0.7697	0.7686	0.7697	0.7686
0.3589	17.99	360	0.6519	0.7535	0.7485	0.7535	0.7502
0.3261	18.99	380	0.6449	0.7589	0.7597	0.7589	0.7585
0.3234	19.99	400	0.6281	0.7674	0.7651	0.7674	0.7647

Framework versions

Transformers 4.24.0.dev0
Pytorch 1.11.0+cu102
Datasets 2.6.1.dev0
Tokenizers 0.13.1

📄 License

This model is licensed under the Apache - 2.0 license.

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