cifar10_mobilenet-v2

jialicheng/cifar10_mobilenet-v2

cifar10_mobilenet-v2 is a image classification model from jialicheng. Use it when you need a label for an image. The card lists the license as other.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

6% of all-time downloads

All-time downloads

207

Public

Parameters

2.3M

9.1 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors9.1 MB · 99%

At a glance

Task: Image Classification
License: other
Model type: mobilenet_v2
Access: Public
Created: Sep 21, 2025
Updated: Sep 21, 2025
SHA: 049ea1df

Base models

google/mobilenet_v2_1.0_224

Model card

From the Hugging Face model README

mobilenet_v2

This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the cifar10 dataset. It achieves the following results on the evaluation set:

Loss: 0.4749
Accuracy: 0.8446

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: 1e-05
train_batch_size: 128
eval_batch_size: 256
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	333	1.4842	0.5297
1.7515	2.0	666	1.6557	0.4453
1.7515	3.0	999	0.8495	0.7062
1.1908	4.0	1332	0.7553	0.747
1.0051	5.0	1665	0.7284	0.7479
1.0051	6.0	1998	0.8906	0.6977
0.9089	7.0	2331	1.0051	0.6587
0.8441	8.0	2664	0.5889	0.8025
0.8441	9.0	2997	0.6794	0.7749
0.7937	10.0	3330	0.9055	0.7074
0.7578	11.0	3663	0.7539	0.7619
0.7578	12.0	3996	0.6955	0.7708
0.7315	13.0	4329	1.1638	0.6383
0.7048	14.0	4662	0.6883	0.7777
0.7048	15.0	4995	0.8076	0.7407
0.6901	16.0	5328	0.7501	0.759
0.6627	17.0	5661	0.6667	0.7834
0.6627	18.0	5994	0.8337	0.7508
0.6457	19.0	6327	0.8104	0.7488
0.6365	20.0	6660	0.6201	0.793
0.6365	21.0	6993	0.6534	0.794
0.6244	22.0	7326	0.4883	0.835
0.6092	23.0	7659	0.6647	0.7898
0.6092	24.0	7992	0.6831	0.777
0.5978	25.0	8325	0.7547	0.7608
0.5838	26.0	8658	0.5030	0.8356
0.5838	27.0	8991	0.4207	0.8573
0.5828	28.0	9324	0.7332	0.7726
0.5716	29.0	9657	0.3767	0.8721
0.5716	30.0	9990	0.5153	0.8394
0.565	31.0	10323	0.5992	0.8111
0.5496	32.0	10656	0.6761	0.7903
0.5496	33.0	10989	0.6412	0.7951
0.5482	34.0	11322	0.7193	0.7872
0.5346	35.0	11655	0.5146	0.8348
0.5346	36.0	11988	0.9719	0.7291
0.5336	37.0	12321	0.6971	0.7816
0.5381	38.0	12654	0.6219	0.8095
0.5381	39.0	12987	0.8059	0.7571
0.5205	40.0	13320	0.5201	0.8323
0.5182	41.0	13653	0.7611	0.7731
0.5182	42.0	13986	0.4614	0.8502
0.5105	43.0	14319	0.7823	0.7874
0.5051	44.0	14652	0.5006	0.8431
0.5051	45.0	14985	0.4780	0.8436
0.5033	46.0	15318	0.7846	0.7505
0.4989	47.0	15651	0.7369	0.7783
0.4989	48.0	15984	0.6269	0.8136
0.4902	49.0	16317	0.6005	0.8187
0.4899	50.0	16650	0.7436	0.7906
0.4899	51.0	16983	0.8028	0.777
0.4837	52.0	17316	0.4615	0.8515
0.481	53.0	17649	0.7034	0.7907
0.481	54.0	17982	0.5976	0.8075
0.481	55.0	18315	0.5986	0.8119
0.4831	56.0	18648	0.5826	0.8211
0.4831	57.0	18981	1.2071	0.6883
0.4844	58.0	19314	0.5116	0.8411
0.4715	59.0	19647	0.3828	0.8749
0.4715	60.0	19980	0.5963	0.8205
0.4689	61.0	20313	0.5510	0.8319
0.472	62.0	20646	0.7266	0.79
0.472	63.0	20979	0.4501	0.8508
0.4668	64.0	21312	0.9535	0.7623
0.4627	65.0	21645	0.7841	0.7753
0.4627	66.0	21978	0.8179	0.7753
0.4549	67.0	22311	0.4133	0.8672
0.4578	68.0	22644	0.7689	0.7905
0.4578	69.0	22977	0.4337	0.8656
0.4581	70.0	23310	0.3573	0.8812
0.4544	71.0	23643	0.4087	0.8698
0.4544	72.0	23976	0.4307	0.8599
0.4547	73.0	24309	0.8750	0.7509
0.4536	74.0	24642	0.5887	0.8163
0.4536	75.0	24975	0.3848	0.8718
0.4573	76.0	25308	0.8057	0.7881
0.4492	77.0	25641	0.8340	0.7727
0.4492	78.0	25974	0.4320	0.8619
0.4437	79.0	26307	0.6830	0.7969
0.4462	80.0	26640	0.6303	0.8152
0.4462	81.0	26973	0.5285	0.8282
0.4419	82.0	27306	0.3664	0.8871
0.449	83.0	27639	0.9199	0.7549
0.449	84.0	27972	0.4462	0.8568
0.4373	85.0	28305	0.4055	0.8645
0.4454	86.0	28638	0.8410	0.7686
0.4454	87.0	28971	0.3777	0.8811
0.4459	88.0	29304	1.0111	0.7445
0.441	89.0	29637	0.9389	0.7426
0.441	90.0	29970	1.0830	0.7328
0.4396	91.0	30303	0.4384	0.8569
0.4381	92.0	30636	0.7627	0.795
0.4381	93.0	30969	0.8045	0.7615
0.439	94.0	31302	0.6230	0.8071
0.4435	95.0	31635	0.6560	0.8117
0.4435	96.0	31968	0.4749	0.8503
0.4428	97.0	32301	0.4037	0.8691
0.4353	98.0	32634	0.7115	0.7903
0.4353	99.0	32967	0.6069	0.8124
0.4433	100.0	33300	0.4749	0.8446

Framework versions

Transformers 4.37.2
Pytorch 2.3.0+cu121
Datasets 2.19.0
Tokenizers 0.15.2

Task: Image Classification
Type: mobilenet_v2
License: other
Created: Sep 21, 2025
Updated: Sep 21, 2025