meat_calssify_fresh_crop_fixed_V_0_2

talli96123/meat_calssify_fresh_crop_fixed_V_0_2

meat_calssify_fresh_crop_fixed_V_0_2 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: dc3587af

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_2

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: 0.5986
Accuracy: 0.7885

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: 64
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
1.0979	1.0	10	1.0867	0.3910
1.0723	2.0	20	1.0606	0.4487
1.0368	3.0	30	1.0202	0.4936
0.968	4.0	40	0.9396	0.5449
0.8927	5.0	50	0.8491	0.6410
0.8256	6.0	60	0.8543	0.6282
0.7477	7.0	70	0.8216	0.6410
0.6567	8.0	80	0.7805	0.6282
0.6121	9.0	90	0.7005	0.7308
0.6303	10.0	100	0.7170	0.6923
0.5335	11.0	110	0.7192	0.7051
0.5375	12.0	120	0.6438	0.7436
0.4651	13.0	130	0.7292	0.7115
0.5207	14.0	140	0.6449	0.7244
0.4692	15.0	150	0.6545	0.7244
0.4146	16.0	160	0.6789	0.7372
0.383	17.0	170	0.6214	0.7564
0.3612	18.0	180	0.6287	0.7372
0.3444	19.0	190	0.7465	0.6987
0.3562	20.0	200	0.6255	0.7756
0.3149	21.0	210	0.5088	0.8141
0.2883	22.0	220	0.6508	0.7179
0.2829	23.0	230	0.7362	0.7179
0.2713	24.0	240	0.5616	0.7692
0.2562	25.0	250	0.7014	0.7244
0.2819	26.0	260	0.6033	0.7628
0.2237	27.0	270	0.5719	0.7885
0.2486	28.0	280	0.7404	0.7179
0.2049	29.0	290	0.6897	0.75
0.2185	30.0	300	0.6415	0.7564
0.239	31.0	310	0.6182	0.7821
0.2315	32.0	320	0.7067	0.75
0.1775	33.0	330	0.6307	0.7628
0.1829	34.0	340	0.5605	0.8205
0.1712	35.0	350	0.6619	0.7692
0.1896	36.0	360	0.5419	0.7949
0.1961	37.0	370	0.6204	0.7885
0.1825	38.0	380	0.5401	0.8013
0.1986	39.0	390	0.5964	0.7821
0.1623	40.0	400	0.5319	0.8269
0.1356	41.0	410	0.6096	0.7821
0.1615	42.0	420	0.6163	0.7692
0.1515	43.0	430	0.5757	0.7821
0.1655	44.0	440	0.6040	0.7756
0.1353	45.0	450	0.6121	0.7564
0.1133	46.0	460	0.4764	0.8141
0.1073	47.0	470	0.6337	0.7821
0.1266	48.0	480	0.5615	0.8077
0.1156	49.0	490	0.5092	0.8205
0.1344	50.0	500	0.5986	0.7885

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
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