Hugging Face's logo

talli96123
/
meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_13

Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_13 / README.md
talli96123's picture
talli96123
End of training
da6efae verified
preview code
|
raw history blame contribute delete
No virus
8.13 kB
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_overlap_epoch100_V_0_13
    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: 0.9595015576323987

meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_13

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: 0.1337
  • Accuracy: 0.9595

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: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0893 1.0 21 1.0721 0.4922
1.057 2.0 42 1.0397 0.5234
1.018 3.0 63 0.9985 0.5265
0.9639 4.0 84 0.9241 0.5794
0.8882 5.0 105 0.8763 0.6231
0.8154 6.0 126 0.8003 0.6542
0.6905 7.0 147 0.8660 0.5981
0.7078 8.0 168 0.7604 0.6729
0.6762 9.0 189 0.7024 0.7134
0.5977 10.0 210 0.7110 0.6854
0.516 11.0 231 0.6112 0.7383
0.4939 12.0 252 0.5301 0.7882
0.4254 13.0 273 0.5863 0.7664
0.4009 14.0 294 0.6802 0.7103
0.4477 15.0 315 0.6327 0.7508
0.3547 16.0 336 0.4456 0.8442
0.3203 17.0 357 0.5052 0.7975
0.3331 18.0 378 0.4561 0.8442
0.3304 19.0 399 0.5010 0.8131
0.3035 20.0 420 0.4363 0.8474
0.2585 21.0 441 0.4671 0.8224
0.2425 22.0 462 0.4404 0.8474
0.2911 23.0 483 0.4463 0.8442
0.2466 24.0 504 0.3739 0.8692
0.2028 25.0 525 0.3317 0.8754
0.1761 26.0 546 0.5032 0.8287
0.2257 27.0 567 0.4841 0.8567
0.2464 28.0 588 0.3266 0.8941
0.1637 29.0 609 0.5122 0.8193
0.2037 30.0 630 0.3683 0.8847
0.1592 31.0 651 0.3185 0.8785
0.1779 32.0 672 0.4130 0.8660
0.1726 33.0 693 0.2861 0.9128
0.1685 34.0 714 0.3174 0.8910
0.1571 35.0 735 0.3252 0.8941
0.1315 36.0 756 0.4721 0.8224
0.2717 37.0 777 0.4957 0.8380
0.1968 38.0 798 0.2139 0.9346
0.1257 39.0 819 0.2550 0.9003
0.1178 40.0 840 0.3248 0.8816
0.1101 41.0 861 0.3600 0.8847
0.117 42.0 882 0.4135 0.8567
0.1339 43.0 903 0.3311 0.8847
0.1098 44.0 924 0.4151 0.8660
0.0872 45.0 945 0.2727 0.9097
0.1106 46.0 966 0.3106 0.9065
0.0955 47.0 987 0.2232 0.9315
0.1308 48.0 1008 0.2594 0.9128
0.0809 49.0 1029 0.2846 0.9065
0.1123 50.0 1050 0.2310 0.9221
0.0971 51.0 1071 0.3536 0.8879
0.1126 52.0 1092 0.3048 0.8972
0.0909 53.0 1113 0.2762 0.9097
0.089 54.0 1134 0.2672 0.9065
0.0881 55.0 1155 0.3479 0.8972
0.0852 56.0 1176 0.3397 0.9003
0.0712 57.0 1197 0.2242 0.9252
0.0844 58.0 1218 0.2430 0.9221
0.0619 59.0 1239 0.3453 0.8785
0.0904 60.0 1260 0.2579 0.9190
0.0704 61.0 1281 0.2337 0.9252
0.0637 62.0 1302 0.2778 0.9128
0.0752 63.0 1323 0.2019 0.9315
0.0759 64.0 1344 0.2226 0.9221
0.048 65.0 1365 0.3095 0.9003
0.0546 66.0 1386 0.3657 0.8972
0.0664 67.0 1407 0.3862 0.8972
0.0584 68.0 1428 0.2183 0.9408
0.0704 69.0 1449 0.2288 0.9283
0.0444 70.0 1470 0.2355 0.9252
0.0475 71.0 1491 0.1171 0.9626
0.0594 72.0 1512 0.2632 0.9252
0.0428 73.0 1533 0.2323 0.9346
0.0501 74.0 1554 0.2586 0.9221
0.0556 75.0 1575 0.2172 0.9252
0.0427 76.0 1596 0.2898 0.9097
0.0572 77.0 1617 0.1617 0.9502
0.038 78.0 1638 0.2294 0.9221
0.0453 79.0 1659 0.1670 0.9502
0.0378 80.0 1680 0.1205 0.9595
0.0444 81.0 1701 0.1833 0.9470
0.065 82.0 1722 0.2581 0.9252
0.0498 83.0 1743 0.2651 0.9315
0.0607 84.0 1764 0.2678 0.9221
0.0554 85.0 1785 0.1547 0.9470
0.0313 86.0 1806 0.1567 0.9533
0.0267 87.0 1827 0.1955 0.9346
0.0377 88.0 1848 0.1900 0.9346
0.0388 89.0 1869 0.1831 0.9377
0.0297 90.0 1890 0.1823 0.9470
0.0424 91.0 1911 0.2606 0.9315
0.0459 92.0 1932 0.1478 0.9502
0.0308 93.0 1953 0.1695 0.9439
0.0415 94.0 1974 0.1325 0.9564
0.0387 95.0 1995 0.0877 0.9751
0.0318 96.0 2016 0.1765 0.9408
0.0317 97.0 2037 0.1650 0.9564
0.0198 98.0 2058 0.2043 0.9439
0.0422 99.0 2079 0.1777 0.9377
0.0335 100.0 2100 0.1337 0.9595

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1
  • Datasets 2.20.0
  • Tokenizers 0.19.1