Cat and Dog Image Classification Model - Open-source Deployment Based on a Small Dataset, with a Cat and Dog Recognition Accuracy of 95%

Cat Dog Classifier With Small Datasest

Developed by MoGHenry

A cat-dog image classification model fine-tuned based on microsoft/resnet-50, achieving 95% accuracy on the evaluation set

Image Classification

Transformers

Open Source License:Apache-2.0 #ResNet50 fine-tuning #High-precision image classification #Few-shot learning

Downloads 33

Release Time : 1/6/2025

Model Overview

This is a cat-dog image classification model optimized for small-scale datasets, suitable for simple binary classification tasks.

Model Features

High Accuracy

Achieves 95% classification accuracy on the evaluation set

Small Dataset Optimization

Designed for small-scale image datasets with high training efficiency

Transfer Learning

Fine-tuned based on the mature ResNet-50 architecture for stable performance

Model Capabilities

Image classification

Cat-dog recognition

Binary classification task processing

Use Cases

Pet Recognition

Pet Photo Classification

Automatically identifies cats or dogs in photos

Classification accuracy of 95%

Educational Demonstration

Machine Learning Teaching

Used as an example model for introductory image classification teaching

🚀 cat_dog_classifier_with_small_datasest

This model is a fine - tuned image classifier that can distinguish between cats and dogs. It is based on the microsoft/resnet - 50 model, trained on the imagefolder dataset, and achieves high accuracy on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1369
Accuracy: 0.95

✨ Features

Fine - tuned Model: Based on the powerful microsoft/resnet-50 model, fine - tuned for cat - dog classification.
High Accuracy: Achieves an accuracy of 0.95 on the evaluation set.

📚 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: 4e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	70	0.5422	0.8571
No log	2.0	140	0.5221	0.8786
No log	3.0	210	0.4977	0.8571
No log	4.0	280	0.4617	0.8786
No log	5.0	350	0.3932	0.9143
No log	6.0	420	0.3411	0.9143
No log	7.0	490	0.2884	0.9143
0.4971	8.0	560	0.2429	0.9286
0.4971	9.0	630	0.2151	0.9429
0.4971	10.0	700	0.1962	0.9286
0.4971	11.0	770	0.1727	0.9357
0.4971	12.0	840	0.1676	0.95
0.4971	13.0	910	0.1764	0.9286
0.4971	14.0	980	0.1565	0.9429
0.2878	15.0	1050	0.1578	0.9429
0.2878	16.0	1120	0.1577	0.9429
0.2878	17.0	1190	0.1393	0.9429
0.2878	18.0	1260	0.1472	0.9429
0.2878	19.0	1330	0.1315	0.95
0.2878	20.0	1400	0.1369	0.95

Framework versions

Transformers 4.47.1
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

📄 License

This project is licensed under the apache - 2.0 license.

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