Microsoft ResNet-50 cartoon character emotion detection model - Open source, free, and efficiently complete image classification tasks

Home

Microsoft Resnet 50 Cartoon Emotion Detection

Developed by jayanta

A ResNet-50 fine-tuned model for cartoon character emotion detection, performing well in image classification tasks.

Image Classification

Transformers

Open Source License:Apache-2.0 #Cartoon Emotion Recognition #High-precision Classification #ResNet50 Fine-tuning

Downloads 124

Release Time : 1/21/2023

Model Overview

This model is a fine-tuned version of microsoft/resnet-50 on an image folder dataset, specifically designed for cartoon character emotion detection tasks.

Model Features

High Accuracy

Achieved an accuracy of 81.65% on the evaluation set, demonstrating strong performance.

Balanced Performance

Precision, recall, and F1 scores are all around 81%, showing balanced performance.

Fine-tuning Optimization

Optimized for cartoon character emotion detection tasks through fine-tuning based on the ResNet-50 architecture.

Model Capabilities

Image Classification

Emotion Recognition

Cartoon Character Analysis

Use Cases

Entertainment Applications

Cartoon Expression Analysis

Used to analyze the emotional expressions of cartoon characters or anime roles

Can accurately identify multiple emotional states

Content Moderation

Cartoon Content Emotion Filtering

Used to filter or classify cartoon content containing specific emotions

🚀 microsoft-resnet-50-cartoon-emotion-detection

This model is a fine - tuned version of microsoft/resnet-50 on the imagefolder dataset. It can be used for cartoon emotion detection, achieving high accuracy, precision, recall, and F1 scores 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: 0.4801
Accuracy: 0.8165
Precision: 0.8182
Recall: 0.8165
F1: 0.8173

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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: 80

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0.97	8	1.3855	0.2294	0.2697	0.2294	0.2165
1.4222	1.97	16	1.3792	0.2569	0.2808	0.2569	0.2543
1.4183	2.97	24	1.3646	0.3853	0.4102	0.3853	0.3511
1.4097	3.97	32	1.3563	0.4128	0.5062	0.4128	0.3245
1.3944	4.97	40	1.3462	0.4037	0.3927	0.4037	0.2939
1.3944	5.97	48	1.3223	0.4037	0.5152	0.4037	0.2841
1.411	6.97	56	1.3040	0.4128	0.4404	0.4128	0.2985
1.346	7.97	64	1.2700	0.4954	0.4960	0.4954	0.4093
1.3031	8.97	72	1.2150	0.5596	0.5440	0.5596	0.4672
1.2371	9.97	80	1.1580	0.5963	0.5659	0.5963	0.5101
1.2371	10.97	88	1.0670	0.6055	0.7279	0.6055	0.5211
1.1736	11.97	96	0.9856	0.6606	0.5537	0.6606	0.5772
1.0457	12.97	104	0.8963	0.6697	0.7631	0.6697	0.5965
0.953	13.97	112	0.8547	0.6697	0.6885	0.6697	0.6081
0.8579	14.97	120	0.7849	0.7156	0.7396	0.7156	0.6643
0.8579	15.97	128	0.7564	0.7431	0.7372	0.7431	0.7119
0.8167	16.97	136	0.7133	0.7615	0.7507	0.7615	0.7211
0.7273	17.97	144	0.6888	0.7523	0.7379	0.7523	0.7202
0.6547	18.97	152	0.6592	0.7798	0.7773	0.7798	0.7577
0.5963	19.97	160	0.6136	0.7706	0.7642	0.7706	0.7551
0.5963	20.97	168	0.5723	0.7890	0.7802	0.7890	0.7787
0.551	21.97	176	0.5686	0.7890	0.7761	0.7890	0.7781
0.4929	22.97	184	0.5597	0.7706	0.7649	0.7706	0.7651
0.4309	23.97	192	0.5234	0.7890	0.7774	0.7890	0.7810
0.3945	24.97	200	0.5008	0.7890	0.7837	0.7890	0.7813
0.3945	25.97	208	0.5289	0.7523	0.7537	0.7523	0.7529
0.3704	26.97	216	0.4399	0.7982	0.7958	0.7982	0.7963
0.3267	27.97	224	0.4539	0.8073	0.7983	0.8073	0.8005
0.2966	28.97	232	0.4735	0.7798	0.7892	0.7798	0.7837
0.2645	29.97	240	0.4594	0.7706	0.7706	0.7706	0.7706
0.2645	30.97	248	0.4699	0.7523	0.7554	0.7523	0.7533
0.2527	31.97	256	0.4551	0.7890	0.7856	0.7890	0.7857
0.2202	32.97	264	0.4458	0.8165	0.8198	0.8165	0.8170
0.2006	33.97	272	0.4632	0.7798	0.7941	0.7798	0.7850
0.1589	34.97	280	0.4651	0.7890	0.7993	0.7890	0.7925
0.1589	35.97	288	0.4595	0.7798	0.7824	0.7798	0.7804
0.153	36.97	296	0.4584	0.7615	0.7691	0.7615	0.7633
0.1427	37.97	304	0.4608	0.7798	0.7830	0.7798	0.7796
0.113	38.97	312	0.4571	0.7890	0.7922	0.7890	0.7899
0.1146	39.97	320	0.5270	0.7615	0.7651	0.7615	0.7613
0.1146	40.97	328	0.4888	0.7706	0.7782	0.7706	0.7710
0.1275	41.97	336	0.4523	0.7890	0.7809	0.7890	0.7837
0.0959	42.97	344	0.4697	0.7798	0.7753	0.7798	0.7767
0.0882	43.97	352	0.4286	0.7706	0.7686	0.7706	0.7686
0.0847	44.97	360	0.5317	0.7890	0.7993	0.7890	0.7925
0.0847	45.97	368	0.5431	0.7615	0.7700	0.7615	0.7647
0.0813	46.97	376	0.4432	0.8257	0.8435	0.8257	0.8284
0.0768	47.97	384	0.4886	0.7982	0.8005	0.7982	0.7956
0.0627	48.97	392	0.5373	0.7982	0.8072	0.7982	0.8010
0.0688	49.97	400	0.5897	0.7798	0.7892	0.7798	0.7822
0.0688	50.97	408	0.5115	0.7982	0.8015	0.7982	0.7992
0.0676	51.97	416	0.4881	0.7982	0.7998	0.7982	0.7978
0.0539	52.97	424	0.4820	0.8073	0.8139	0.8073	0.8077
0.0596	53.97	432	0.4450	0.8257	0.8246	0.8257	0.8244
0.0611	54.97	440	0.5057	0.7890	0.8008	0.7890	0.7924
0.0611	55.97	448	0.4918	0.7982	0.8056	0.7982	0.8008
0.0643	56.97	456	0.5946	0.7523	0.7587	0.7523	0.7545
0.0605	57.97	464	0.4888	0.8073	0.8239	0.8073	0.8121
0.063	58.97	472	0.5917	0.7890	0.8051	0.7890	0.7937
0.0595	59.97	480	0.5117	0.7890	0.7904	0.7890	0.7894
0.0595	60.97	488	0.5497	0.7615	0.7692	0.7615	0.7635
0.0554	61.97	496	0.4742	0.8165	0.8101	0.8165	0.8126
0.0557	62.97	504	0.5369	0.7890	0.7886	0.7890	0.7886
0.0539	63.97	512	0.5440	0.7890	0.7922	0.7890	0.7899
0.048	64.97	520	0.5924	0.7890	0.7878	0.7890	0.7883
0.048	65.97	528	0.4863	0.8440	0.8440	0.8440	0.8440
0.045	66.97	536	0.5850	0.8073	0.8076	0.8073	0.8047
0.047	67.97	544	0.4939	0.8257	0.8212	0.8257	0.8227
0.0412	68.97	552	0.4850	0.7890	0.7912	0.7890	0.7900
0.0392	69.97	560	0.5066	0.8257	0.8265	0.8257	0.8258
0.0392	70.97	568	0.4965	0.8073	0.8053	0.8073	0.8058
0.0423	71.97	576	0.4717	0.8349	0.8376	0.8349	0.8351
0.0471	72.97	584	0.4845	0.8257	0.8378	0.8257	0.8296
0.0322	73.97	592	0.5188	0.7706	0.7689	0.7706	0.7693
0.042	74.97	600	0.5242	0.7706	0.7699	0.7706	0.7701
0.042	75.97	608	0.5945	0.7798	0.7824	0.7798	0.7804
0.0416	76.97	616	0.5432	0.7982	0.8038	0.7982	0.7993
0.0399	77.97	624	0.5381	0.7982	0.8072	0.7982	0.7994
0.0439	78.97	632	0.6181	0.7798	0.7878	0.7798	0.7827
0.0462	79.97	640	0.4801	0.8165	0.8182	0.8165	0.8173

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu117
Datasets 2.8.0
Tokenizers 0.11.0

📄 License

This project is licensed under the Apache - 2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご