Open source of Microsoft ResNet-50 Cartoon Face Recognition Model - Excellent performance in image classification and recognition

Microsoft Resnet 50 Cartoon Face Recognition

Developed by jayanta

A cartoon face recognition model fine-tuned based on microsoft/resnet-50, performing well in image classification tasks.

Face-related

Transformers

Open Source License:Apache-2.0 #Cartoon Face Recognition #ResNet50 Fine-tuning #Image Classification Model

Downloads 63

Release Time : 1/21/2023

Model Overview

This model is a cartoon face recognition model fine-tuned based on the ResNet-50 architecture, primarily used for image classification tasks, capable of recognizing and classifying cartoon-style facial images.

Model Features

High Accuracy

Achieves 77.55% accuracy on the evaluation set, demonstrating good performance.

Fine-tuning Optimization

Fine-tuned based on the ResNet-50 architecture, specifically optimized for cartoon face recognition tasks.

Comprehensive Evaluation Metrics

Provides multiple evaluation metrics including accuracy, precision, recall, and F1 score, comprehensively reflecting model performance.

Model Capabilities

Image Classification

Cartoon Face Recognition

Use Cases

Entertainment

Cartoon Character Recognition

Used to recognize and classify character faces in cartoon works.

77.55% accuracy

Security

Cartoon-style Face Verification

Can be used to verify cartoon-style facial images.

🚀 microsoft-resnet-50-cartoon-face-recognition

This model is a fine - tuned version of microsoft/resnet-50 on the imagefolder dataset. It's designed for cartoon face recognition, offering reliable performance in classifying cartoon faces.

🚀 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: 0.8508
Accuracy: 0.7755
Precision: 0.7715
Recall: 0.7755
F1: 0.7676

📚 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: 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: 120

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0.89	6	3.1774	0.0370	0.0069	0.0370	0.0098
3.4185	1.89	12	3.1739	0.0301	0.0100	0.0301	0.0126
3.4185	2.89	18	3.1668	0.0440	0.0805	0.0440	0.0340
3.6463	3.89	24	3.1583	0.0370	0.0180	0.0370	0.0151
3.3899	4.89	30	3.1425	0.0741	0.0610	0.0741	0.0453
3.3899	5.89	36	3.1262	0.0856	0.0334	0.0856	0.0405
3.5947	6.89	42	3.1055	0.1019	0.0784	0.1019	0.0481
3.5947	7.89	48	3.0841	0.1181	0.1071	0.1181	0.0500
3.5553	8.89	54	3.0650	0.1065	0.0216	0.1065	0.0343
3.2713	9.89	60	3.0351	0.1273	0.0323	0.1273	0.0418
3.2713	10.89	66	3.0069	0.1227	0.0311	0.1227	0.0390
3.4382	11.89	72	2.9754	0.1204	0.0353	0.1204	0.0366
3.4382	12.89	78	2.9455	0.1227	0.0224	0.1227	0.0338
3.3573	13.89	84	2.9167	0.1204	0.0213	0.1204	0.0332
3.0549	14.89	90	2.8841	0.1227	0.0474	0.1227	0.0408
3.0549	15.89	96	2.8534	0.1412	0.1174	0.1412	0.0540
3.1853	16.89	102	2.8143	0.1505	0.1595	0.1505	0.0667
3.1853	17.89	108	2.7771	0.1667	0.1693	0.1667	0.0719
3.0871	18.89	114	2.7400	0.1759	0.1454	0.1759	0.0896
2.7666	19.89	120	2.7048	0.2014	0.0927	0.2014	0.1051
2.7666	20.89	126	2.6458	0.2315	0.1622	0.2315	0.1250
2.846	21.89	132	2.5803	0.2569	0.2386	0.2569	0.1470
2.846	22.89	138	2.5291	0.2639	0.2725	0.2639	0.1523
2.7428	23.89	144	2.4916	0.2870	0.2114	0.2870	0.1811
2.4183	24.89	150	2.4273	0.3079	0.2322	0.3079	0.2048
2.4183	25.89	156	2.3923	0.3194	0.2937	0.3194	0.2238
2.5064	26.89	162	2.3349	0.3403	0.3183	0.3403	0.2494
2.5064	27.89	168	2.2977	0.3542	0.3554	0.3542	0.2663
2.4046	28.89	174	2.2363	0.3773	0.3214	0.3773	0.2981
2.1201	29.89	180	2.1791	0.3889	0.4024	0.3889	0.3179
2.1201	30.89	186	2.1448	0.4144	0.4079	0.4144	0.3455
2.1705	31.89	192	2.0969	0.4306	0.4214	0.4306	0.3583
2.1705	32.89	198	2.0535	0.4468	0.4448	0.4468	0.3797
2.0295	33.89	204	1.9940	0.4745	0.4877	0.4745	0.4133
1.8114	34.89	210	1.9467	0.4861	0.4952	0.4861	0.4261
1.8114	35.89	216	1.8896	0.4931	0.4510	0.4931	0.4321
1.8048	36.89	222	1.8404	0.5046	0.4859	0.5046	0.4507
1.8048	37.89	228	1.7999	0.5278	0.5142	0.5278	0.4816
1.6862	38.89	234	1.7363	0.5324	0.5169	0.5324	0.4844
1.4545	39.89	240	1.7104	0.5440	0.5100	0.5440	0.4971
1.4545	40.89	246	1.6492	0.5648	0.5239	0.5648	0.5138
1.4444	41.89	252	1.6076	0.5671	0.5329	0.5671	0.5260
1.4444	42.89	258	1.5784	0.5741	0.5708	0.5741	0.5424
1.3124	43.89	264	1.5259	0.6019	0.5977	0.6019	0.5619
1.1645	44.89	270	1.4814	0.6181	0.6033	0.6181	0.5880
1.1645	45.89	276	1.4697	0.6088	0.6033	0.6088	0.5803
1.1307	46.89	282	1.4380	0.6088	0.6015	0.6088	0.5769
1.1307	47.89	288	1.3872	0.6227	0.6085	0.6227	0.5917
1.0347	48.89	294	1.3709	0.6157	0.6039	0.6157	0.5880
0.8962	49.89	300	1.3415	0.6296	0.6120	0.6296	0.6057
0.8962	50.89	306	1.3290	0.6389	0.6327	0.6389	0.6134
0.8898	51.89	312	1.2836	0.6389	0.6192	0.6389	0.6119
0.8898	52.89	318	1.2665	0.6412	0.6186	0.6412	0.6162
0.7886	53.89	324	1.2272	0.6551	0.6431	0.6551	0.6319
0.6794	54.89	330	1.2144	0.6806	0.6643	0.6806	0.6629
0.6794	55.89	336	1.1817	0.6806	0.6666	0.6806	0.6642
0.6459	56.89	342	1.1702	0.6782	0.6591	0.6782	0.6574
0.6459	57.89	348	1.0947	0.7037	0.6863	0.7037	0.6883
0.6075	58.89	354	1.1227	0.7037	0.6874	0.7037	0.6867
0.4979	59.89	360	1.0849	0.7083	0.6813	0.7083	0.6895
0.4979	60.89	366	1.0742	0.7153	0.6924	0.7153	0.6976
0.4895	61.89	372	1.0452	0.7245	0.7020	0.7245	0.7057
0.4895	62.89	378	1.0435	0.7361	0.7316	0.7361	0.7235
0.456	63.89	384	1.0698	0.6921	0.6835	0.6921	0.6783
0.3816	64.89	390	1.0126	0.7222	0.7064	0.7222	0.7091
0.3816	65.89	396	0.9934	0.7361	0.7247	0.7361	0.7205
0.3599	66.89	402	0.9960	0.7292	0.7213	0.7292	0.7170
0.3599	67.89	408	1.0141	0.7222	0.7148	0.7222	0.7087
0.3484	68.89	414	0.9934	0.7222	0.7125	0.7222	0.7107
0.2939	69.89	420	0.9835	0.7431	0.7417	0.7431	0.7349
0.2939	70.89	426	0.9870	0.7315	0.7275	0.7315	0.7217
0.285	71.89	432	0.9656	0.7431	0.7411	0.7431	0.7340
0.285	72.89	438	0.9462	0.7338	0.7320	0.7338	0.7267
0.2463	73.89	444	0.9513	0.7454	0.7467	0.7454	0.7384
0.2328	74.89	450	0.9334	0.7361	0.7389	0.7361	0.7286
0.2328	75.89	456	0.9375	0.7384	0.7278	0.7384	0.7291
0.2208	76.89	462	0.9332	0.7407	0.7357	0.7407	0.7322
0.2208	77.89	468	0.9408	0.7384	0.7406	0.7384	0.7346
0.2177	78.89	474	0.9059	0.7222	0.7183	0.7222	0.7136
0.1734	79.89	480	0.9517	0.7315	0.7371	0.7315	0.7257
0.1734	80.89	486	0.9063	0.7523	0.7462	0.7523	0.7424
0.1791	81.89	492	0.9171	0.7454	0.7461	0.7454	0.7386
0.1791	82.89	498	0.8846	0.7523	0.7561	0.7523	0.7485
0.1681	83.89	504	0.8871	0.7384	0.7431	0.7384	0.7320
0.1573	84.89	510	0.9118	0.7454	0.7474	0.7454	0.7395
0.1573	85.89	516	0.9006	0.7407	0.7432	0.7407	0.7366
0.1439	86.89	522	0.8703	0.7616	0.7693	0.7616	0.7579
0.1439	87.89	528	0.8988	0.7454	0.7570	0.7454	0.7401
0.1362	88.89	534	0.9234	0.7454	0.7477	0.7454	0.7396
0.1249	89.89	540	0.8860	0.75	0.7473	0.75	0.7425
0.1249	90.89	546	0.8608	0.7546	0.7601	0.7546	0.7513
0.1264	91.89	552	0.8871	0.7593	0.7640	0.7593	0.7560
0.1264	92.89	558	0.8432	0.7639	0.7727	0.7639	0.7599
0.1201	93.89	564	0.8654	0.7639	0.7698	0.7639	0.7569
0.1117	94.89	570	0.8856	0.7454	0.7569	0.7454	0.7415
0.1117	95.89	576	0.8668	0.7546	0.7686	0.7546	0.7535
0.1128	96.89	582	0.8630	0.7662	0.7698	0.7662	0.7619
0.1128	97.89	588	0.8551	0.7731	0.7826	0.7731	0.7696
0.1155	98.89	594	0.8697	0.7708	0.7738	0.7708	0.7643
0.0987	99.89	600	0.8613	0.7546	0.7518	0.7546	0.7484
0.0987	100.89	606	0.8742	0.7569	0.7597	0.7569	0.7524
0.1063	101.89	612	0.8498	0.7755	0.7807	0.7755	0.7712
0.1063	102.89	618	0.8557	0.7708	0.7749	0.7708	0.7655
0.097	103.89	624	0.8764	0.7546	0.7634	0.7546	0.7527
0.0947	104.89	630	0.8677	0.7616	0.7628	0.7616	0.7572
0.0947	105.89	636	0.8909	0.75	0.7614	0.75	0.7469
0.1013	106.89	642	0.8283	0.7639	0.7621	0.7639	0.7580
0.1013	107.89	648	0.8471	0.7662	0.7864	0.7662	0.7651
0.0963	108.89	654	0.8653	0.7593	0.7701	0.7593	0.7558
0.0874	109.89	660	0.8479	0.7731	0.7834	0.7731	0.7692
0.0874	110.89	666	0.8584	0.7639	0.7719	0.7639	0.7620
0.0876	111.89	672	0.8714	0.7616	0.7600	0.7616	0.7550
0.0876	112.89	678	0.8509	0.7731	0.7847	0.7731	0.7727
0.0974	113.89	684	0.8688	0.7685	0.7741	0.7685	0.7648
0.0869	114.89	690	0.8590	0.7847	0.7932	0.7847	0.7794
0.0869	115.89	696	0.8687	0.7593	0.7703	0.7593	0.7579
0.0877	116.89	702	0.8735	0.7593	0.7698	0.7593	0.7554
0.0877	117.89	708	0.8566	0.7546	0.7732	0.7546	0.7518
0.0883	118.89	714	0.8681	0.7708	0.7750	0.7708	0.7656
0.0883	119.89	720	0.8508	0.7755	0.7715	0.7755	0.7676

📄 License

This model is released under the Apache 2.0 license.

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