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: 2.7620
Accuracy: 0.3333

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: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1661	1.0	44	1.1896	0.1667
1.032	2.0	88	1.2389	0.25
1.0784	3.0	132	1.0699	0.25
1.0819	4.0	176	1.2184	0.0833
1.0274	5.0	220	1.4634	0.0833
1.106	6.0	264	1.0843	0.4167
1.1051	7.0	308	1.1752	0.25
1.0964	8.0	352	1.3698	0.1667
1.1467	9.0	396	1.3023	0.1667
0.7936	10.0	440	1.4005	0.25
0.8262	11.0	484	1.2786	0.25
1.1219	12.0	528	1.4314	0.25
0.9306	13.0	572	1.9908	0.1667
1.2061	14.0	616	1.4193	0.25
0.7925	15.0	660	2.1698	0.25
0.104	16.0	704	2.2744	0.25
0.2619	17.0	748	1.7998	0.3333
0.3196	18.0	792	2.0410	0.3333
1.2972	19.0	836	1.4657	0.5
0.7459	20.0	880	2.4774	0.3333
0.4766	21.0	924	2.0181	0.3333
0.1264	22.0	968	2.6095	0.25
0.2541	23.0	1012	2.6884	0.1667
0.179	24.0	1056	2.6612	0.25
0.308	25.0	1100	1.5827	0.5
0.0291	26.0	1144	2.4500	0.3333
0.0773	27.0	1188	2.4238	0.4167
0.0235	28.0	1232	1.8948	0.5
0.1782	29.0	1276	1.7855	0.5
0.0597	30.0	1320	2.7620	0.3333

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

talli96123/meat_calssify_fresh_V_0_5_test

meat_calssify_fresh_V_0_5_test

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

talli96123/
meat_calssify_fresh_V_0_5_test