This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.3049
Accuracy: 0.7194

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.0728	0.0803	200	5.0558	0.0417
4.9729	0.1605	400	4.9402	0.0865
4.8315	0.2408	600	4.8201	0.1738
4.6814	0.3210	800	4.6929	0.1999
4.5936	0.4013	1000	4.5779	0.2296
4.5513	0.4815	1200	4.4604	0.2645
4.3161	0.5618	1400	4.3466	0.2976
4.2724	0.6421	1600	4.2445	0.3472
4.1105	0.7223	1800	4.1402	0.3530
4.076	0.8026	2000	4.0522	0.3703
3.9963	0.8828	2200	3.9616	0.3803
3.9235	0.9631	2400	3.8737	0.4122
3.5191	1.0433	2600	3.7816	0.4142
3.6156	1.1236	2800	3.6908	0.4345
3.4338	1.2039	3000	3.6081	0.4487
3.4952	1.2841	3200	3.5293	0.4792
3.369	1.3644	3400	3.4345	0.4836
3.3625	1.4446	3600	3.3572	0.5065
3.3323	1.5249	3800	3.2832	0.5160
3.2143	1.6051	4000	3.2152	0.5035
3.0538	1.6854	4200	3.1389	0.5212
3.0841	1.7657	4400	3.0654	0.5418
2.9804	1.8459	4600	2.9880	0.5493
3.0014	1.9262	4800	2.9356	0.5449
3.0302	2.0064	5000	2.8573	0.5585
2.714	2.0867	5200	2.7933	0.5667
2.6886	2.1669	5400	2.7398	0.5649
2.4439	2.2472	5600	2.6725	0.5802
2.3172	2.3274	5800	2.6106	0.5836
2.3907	2.4077	6000	2.5523	0.5896
2.617	2.4880	6200	2.4995	0.5942
2.1277	2.5682	6400	2.4401	0.6041
2.2384	2.6485	6600	2.3903	0.6113
2.2872	2.7287	6800	2.3348	0.6163
2.1654	2.8090	7000	2.3031	0.6083
2.1407	2.8892	7200	2.2365	0.6239
2.0487	2.9695	7400	2.2016	0.6179
1.9755	3.0498	7600	2.1587	0.6281
1.7432	3.1300	7800	2.1125	0.6382
1.7321	3.2103	8000	2.0822	0.6438
1.4812	3.2905	8200	2.0482	0.6390
1.637	3.3708	8400	1.9964	0.6556
1.538	3.4510	8600	1.9785	0.6472
1.9383	3.5313	8800	1.9220	0.6602
1.5674	3.6116	9000	1.9167	0.6558
1.6303	3.6918	9200	1.8803	0.6629
1.3882	3.7721	9400	1.8531	0.6619
1.5955	3.8523	9600	1.8092	0.6739
1.5168	3.9326	9800	1.7814	0.6763
1.5228	4.0128	10000	1.7617	0.6697
1.3918	4.0931	10200	1.7434	0.6771
1.3898	4.1734	10400	1.6937	0.6769
1.3597	4.2536	10600	1.6928	0.6801
1.3249	4.3339	10800	1.6636	0.6807
1.361	4.4141	11000	1.6585	0.6829
1.2845	4.4944	11200	1.6296	0.6887
1.2342	4.5746	11400	1.6049	0.6928
1.1281	4.6549	11600	1.5856	0.6948
1.2667	4.7352	11800	1.5775	0.6905
1.3742	4.8154	12000	1.5698	0.6911
1.076	4.8957	12200	1.5423	0.6942
1.2422	4.9759	12400	1.5282	0.6970
0.9078	5.0562	12600	1.5109	0.6992
1.0157	5.1364	12800	1.4908	0.7010
1.1909	5.2167	13000	1.4917	0.7006
1.0085	5.2970	13200	1.4804	0.6996
1.0942	5.3772	13400	1.4662	0.7024
0.9015	5.4575	13600	1.4785	0.6952
0.899	5.5377	13800	1.4409	0.7076
1.1695	5.6180	14000	1.4347	0.7074
0.9743	5.6982	14200	1.4381	0.7084
0.9005	5.7785	14400	1.4145	0.7086
1.0092	5.8587	14600	1.4067	0.7128
0.9859	5.9390	14800	1.3790	0.7186
0.8728	6.0193	15000	1.3951	0.7138
0.8551	6.0995	15200	1.3765	0.7211
0.8369	6.1798	15400	1.3751	0.7154
0.8989	6.2600	15600	1.3641	0.7168
0.7289	6.3403	15800	1.3701	0.7162
0.7181	6.4205	16000	1.3661	0.7088
0.7517	6.5008	16200	1.3528	0.7136
1.0271	6.5811	16400	1.3405	0.7200
0.8599	6.6613	16600	1.3296	0.7215
1.0141	6.7416	16800	1.3379	0.7190
0.6966	6.8218	17000	1.3294	0.7194
0.9327	6.9021	17200	1.3241	0.7198
0.8072	6.9823	17400	1.3226	0.7196
0.9195	7.0626	17600	1.3234	0.7170
0.6585	7.1429	17800	1.3171	0.7207
0.9513	7.2231	18000	1.3064	0.7190
0.7139	7.3034	18200	1.3156	0.7215
0.7199	7.3836	18400	1.3098	0.7249
0.7799	7.4639	18600	1.3210	0.7166
0.7034	7.5441	18800	1.3015	0.7245
0.8172	7.6244	19000	1.2978	0.7289
0.6842	7.7047	19200	1.3084	0.7184
0.8592	7.7849	19400	1.2991	0.7231
0.7255	7.8652	19600	1.2929	0.7257
0.8207	7.9454	19800	1.3049	0.7194

Framework versions

Transformers 4.41.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Invitrace/I-live-well-foodai-freeze-layer11

invitrace-ilivewell-freeze-layer11

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Invitrace/
I-live-well-foodai-freeze-layer11