README.md

metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: meat_calssify_fresh_crop_fixed_V_0_2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7884615384615384

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