meat_calssify_fresh_crop_fixed_V_0_1

talli96123/meat_calssify_fresh_crop_fixed_V_0_1

meat_calssify_fresh_crop_fixed_V_0_1 is a image classification model from talli96123. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Jun 11, 2024
Updated: Jun 11, 2024
SHA: 00bdd726

Base models

google/vit-base-patch16-224-in21k

Datasets

imagefolder

Model card

From the Hugging Face model README

meat_calssify_fresh_crop_fixed_V_0_1

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: 1.8065
Accuracy: 0.7405

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0665	1.0	630	1.0532	0.4051
1.0373	2.0	1260	1.0018	0.5063
1.1547	3.0	1890	1.0149	0.4557
1.2332	4.0	2520	1.0203	0.4937
1.2068	5.0	3150	1.8757	0.4114
1.2576	6.0	3780	1.1679	0.5570
1.2601	7.0	4410	1.0406	0.5886
1.0981	8.0	5040	1.1907	0.5949
1.0283	9.0	5670	1.1140	0.6962
1.0173	10.0	6300	1.3832	0.6582
1.0185	11.0	6930	1.0796	0.6646
0.8782	12.0	7560	1.4764	0.6582
0.8761	13.0	8190	1.5654	0.6076
0.8258	14.0	8820	1.4798	0.6456
0.8745	15.0	9450	2.2686	0.5380
0.6339	16.0	10080	1.5005	0.6835
0.6122	17.0	10710	1.7847	0.6709
0.5812	18.0	11340	1.6494	0.6962
0.54	19.0	11970	1.6806	0.6899
0.4075	20.0	12600	2.0121	0.6709
0.4847	21.0	13230	1.5560	0.7089
0.4862	22.0	13860	1.5739	0.7215
0.5239	23.0	14490	1.5406	0.6835
0.4293	24.0	15120	1.7675	0.6962
0.4592	25.0	15750	1.7242	0.7215
0.366	26.0	16380	1.4541	0.7468
0.3754	27.0	17010	1.8378	0.7089
0.356	28.0	17640	1.6920	0.7152
0.3689	29.0	18270	1.7062	0.7152
0.3305	30.0	18900	1.6425	0.7025
0.2995	31.0	19530	1.9092	0.6835
0.3692	32.0	20160	1.5893	0.7405
0.2386	33.0	20790	1.5449	0.7532
0.2427	34.0	21420	1.7221	0.7532
0.2645	35.0	22050	1.3437	0.7975
0.2673	36.0	22680	1.7081	0.7025
0.1925	37.0	23310	1.4535	0.7911
0.1304	38.0	23940	1.9245	0.7152
0.1797	39.0	24570	1.6966	0.7468
0.2092	40.0	25200	1.3813	0.7911
0.161	41.0	25830	1.4498	0.7975
0.1872	42.0	26460	1.6249	0.7595
0.1308	43.0	27090	1.4823	0.7785
0.1939	44.0	27720	1.4895	0.7848
0.1561	45.0	28350	1.6162	0.7532
0.0929	46.0	28980	1.4918	0.7911
0.0911	47.0	29610	1.5509	0.7722
0.0945	48.0	30240	1.4868	0.7848
0.0985	49.0	30870	1.6767	0.7595
0.1023	50.0	31500	1.8065	0.7405

Framework versions

Transformers 4.41.2
Pytorch 2.3.0
Datasets 2.19.2
Tokenizers 0.19.1

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Jun 11, 2024
Updated: Jun 11, 2024