ResNet-18 Image Classification Model - Open source and free to deploy, performs well on image folder datasets

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Resnet 18 Feature Extraction

Developed by SiddharthaM

An image classification model fine-tuned based on microsoft/resnet-18, performing excellently on image folder datasets.

Image Classification

Transformers

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

Downloads 28

Release Time : 11/7/2022

Model Overview

This model is a pre-trained model based on the ResNet-18 architecture, fine-tuned for image classification tasks. It demonstrates high accuracy and recall on the evaluation set.

Model Features

High accuracy

Achieves 95% accuracy on the evaluation set, demonstrating excellent performance.

Balanced metrics

Precision, recall, and F1 score all perform well, making the model's predictions reliable.

Fine-tuning optimization

Fine-tuned based on a pre-trained model to adapt to specific image classification tasks.

Model Capabilities

Image classification

Feature extraction

Use Cases

Computer vision

General image classification

Can be used to classify various types of images

95% accuracy, F1 score 0.972

🚀 resnet-18-feature-extraction

This model is a fine - tuned version of microsoft/resnet-18 on the imagefolder dataset. It can effectively perform image classification tasks and achieves high accuracy, precision, recall, and F1 scores on the evaluation set.

🚀 Quick Start

This model is ready for use after fine - tuning on the imagefolder dataset. You can directly utilize it for image classification tasks.

📚 Documentation

Model Performance

It achieves the following results on the evaluation set:

Loss: 0.1485
Accuracy: 0.95
Precision: 0.9653
Recall: 0.9789
F1: 0.9720
Roc Auc: 0.8505

Model Index

Property	Details
Model Name	resnet-18-feature-extraction
Task	Image Classification
Dataset	imagefolder
Metrics	Accuracy: 0.95, Precision: 0.9652777777777778, Recall: 0.9788732394366197, F1: 0.972027972027972

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
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: 50

Training Results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Roc Auc
No log	0.8	2	0.6232	0.75	0.9636	0.7465	0.8413	0.7621
No log	1.8	4	0.6971	0.4875	1.0	0.4225	0.5941	0.7113
No log	2.8	6	0.7915	0.2875	1.0	0.1972	0.3294	0.5986
No log	3.8	8	0.8480	0.2875	1.0	0.1972	0.3294	0.5986
0.8651	4.8	10	0.9094	0.2562	1.0	0.1620	0.2788	0.5810
0.8651	5.8	12	0.7470	0.5625	1.0	0.5070	0.6729	0.7535
0.8651	6.8	14	0.5915	0.85	1.0	0.8310	0.9077	0.9155
0.8651	7.8	16	0.4817	0.8875	0.9844	0.8873	0.9333	0.8881
0.8651	8.8	18	0.3455	0.9187	0.9778	0.9296	0.9531	0.8815
0.5349	9.8	20	0.2966	0.9187	0.9708	0.9366	0.9534	0.8572
0.5349	10.8	22	0.2347	0.95	0.9653	0.9789	0.9720	0.8505
0.5349	11.8	24	0.2468	0.9313	0.9645	0.9577	0.9611	0.8400
0.5349	12.8	26	0.2310	0.9563	0.9720	0.9789	0.9754	0.8783
0.5349	13.8	28	0.2083	0.9313	0.9580	0.9648	0.9614	0.8157
0.3593	14.8	30	0.1840	0.9375	0.9521	0.9789	0.9653	0.7950
0.3593	15.8	32	0.1947	0.9375	0.9648	0.9648	0.9648	0.8435
0.3593	16.8	34	0.1837	0.9313	0.9517	0.9718	0.9617	0.7915
0.3593	17.8	36	0.1819	0.9437	0.9524	0.9859	0.9689	0.7985
0.3593	18.8	38	0.1924	0.9437	0.9650	0.9718	0.9684	0.8470
0.2737	19.8	40	0.1990	0.95	0.9653	0.9789	0.9720	0.8505
0.2737	20.8	42	0.1759	0.95	0.9718	0.9718	0.9718	0.8748
0.2737	21.8	44	0.1804	0.9313	0.9517	0.9718	0.9617	0.7915
0.2737	22.8	46	0.1666	0.9313	0.9517	0.9718	0.9617	0.7915
0.2737	23.8	48	0.1534	0.9437	0.9524	0.9859	0.9689	0.7985
0.2278	24.8	50	0.1612	0.9375	0.9521	0.9789	0.9653	0.7950
0.2278	25.8	52	0.1535	0.9437	0.9586	0.9789	0.9686	0.8228
0.2278	26.8	54	0.1568	0.9437	0.9716	0.9648	0.9682	0.8713
0.2278	27.8	56	0.2107	0.9375	0.9714	0.9577	0.9645	0.8678
0.2278	28.8	58	0.1592	0.9313	0.9517	0.9718	0.9617	0.7915
0.2057	29.8	60	0.1557	0.9375	0.9648	0.9648	0.9648	0.8435
0.2057	30.8	62	0.1714	0.9437	0.9650	0.9718	0.9684	0.8470
0.2057	31.8	64	0.1571	0.95	0.9653	0.9789	0.9720	0.8505
0.2057	32.8	66	0.1574	0.9375	0.9583	0.9718	0.9650	0.8192
0.2057	33.8	68	0.1423	0.9563	0.9720	0.9789	0.9754	0.8783
0.2	34.8	70	0.1677	0.9437	0.9650	0.9718	0.9684	0.8470
0.2	35.8	72	0.1560	0.9375	0.9583	0.9718	0.9650	0.8192
0.2	36.8	74	0.1594	0.9375	0.9521	0.9789	0.9653	0.7950
0.2	37.8	76	0.1512	0.9437	0.9586	0.9789	0.9686	0.8228
0.2	38.8	78	0.1396	0.9563	0.9655	0.9859	0.9756	0.8541
0.1838	39.8	80	0.1509	0.9375	0.9583	0.9718	0.9650	0.8192
0.1838	40.8	82	0.1529	0.95	0.9718	0.9718	0.9718	0.8748
0.1838	41.8	84	0.1506	0.95	0.9653	0.9789	0.9720	0.8505
0.1838	42.8	86	0.1549	0.95	0.9653	0.9789	0.9720	0.8505
0.1838	43.8	88	0.1331	0.9563	0.9655	0.9859	0.9756	0.8541
0.1872	44.8	90	0.1409	0.9437	0.9524	0.9859	0.9689	0.7985
0.1872	45.8	92	0.1639	0.9375	0.9583	0.9718	0.9650	0.8192
0.1872	46.8	94	0.1391	0.95	0.9589	0.9859	0.9722	0.8263
0.1872	47.8	96	0.1436	0.9563	0.9655	0.9859	0.9756	0.8541
0.1872	48.8	98	0.1442	0.9437	0.9586	0.9789	0.9686	0.8228
0.185	49.8	100	0.1485	0.95	0.9653	0.9789	0.9720	0.8505

Framework Versions

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

📄 License

This model is licensed under the Apache - 2.0 license.

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