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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: gockle_v2 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7843691148775894 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# gockle_v2 |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9618 |
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- Accuracy: 0.7844 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-06 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 11 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.7231 | 0.64 | 100 | 2.6467 | 0.2279 | |
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| 2.3217 | 1.28 | 200 | 2.4386 | 0.2288 | |
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| 2.0819 | 1.92 | 300 | 2.2887 | 0.2815 | |
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| 1.9583 | 2.56 | 400 | 2.1686 | 0.4501 | |
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| 1.8098 | 3.21 | 500 | 2.0731 | 0.5085 | |
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| 1.7511 | 3.85 | 600 | 1.9978 | 0.5320 | |
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| 1.6581 | 4.49 | 700 | 1.9233 | 0.5584 | |
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| 1.6094 | 5.13 | 800 | 1.8703 | 0.5706 | |
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| 1.5241 | 5.77 | 900 | 1.8192 | 0.6017 | |
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| 1.501 | 6.41 | 1000 | 1.7757 | 0.6111 | |
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| 1.4308 | 7.05 | 1100 | 1.7415 | 0.6281 | |
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| 1.3985 | 7.69 | 1200 | 1.7015 | 0.6375 | |
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| 1.3559 | 8.33 | 1300 | 1.6652 | 0.6403 | |
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| 1.3092 | 8.97 | 1400 | 1.6290 | 0.6488 | |
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| 1.3059 | 9.62 | 1500 | 1.6142 | 0.6620 | |
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| 1.2597 | 10.26 | 1600 | 1.5771 | 0.6704 | |
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| 1.2147 | 10.9 | 1700 | 1.5501 | 0.6902 | |
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| 1.1942 | 11.54 | 1800 | 1.5288 | 0.6911 | |
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| 1.1668 | 12.18 | 1900 | 1.5081 | 0.6902 | |
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| 1.1371 | 12.82 | 2000 | 1.4883 | 0.6949 | |
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| 1.1256 | 13.46 | 2100 | 1.4770 | 0.6930 | |
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| 1.0922 | 14.1 | 2200 | 1.4500 | 0.7081 | |
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| 1.0559 | 14.74 | 2300 | 1.4369 | 0.7072 | |
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| 1.054 | 15.38 | 2400 | 1.4157 | 0.7128 | |
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| 1.0465 | 16.03 | 2500 | 1.3899 | 0.7279 | |
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| 0.9965 | 16.67 | 2600 | 1.3734 | 0.7194 | |
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| 0.9876 | 17.31 | 2700 | 1.3603 | 0.7298 | |
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| 0.9791 | 17.95 | 2800 | 1.3422 | 0.7298 | |
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| 0.9551 | 18.59 | 2900 | 1.3309 | 0.7373 | |
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| 0.9313 | 19.23 | 3000 | 1.3223 | 0.7335 | |
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| 0.9211 | 19.87 | 3100 | 1.3052 | 0.7345 | |
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| 0.9071 | 20.51 | 3200 | 1.2897 | 0.7420 | |
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| 0.875 | 21.15 | 3300 | 1.2762 | 0.7561 | |
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| 0.8676 | 21.79 | 3400 | 1.2657 | 0.7542 | |
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| 0.8498 | 22.44 | 3500 | 1.2575 | 0.7580 | |
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| 0.8529 | 23.08 | 3600 | 1.2435 | 0.7542 | |
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| 0.8341 | 23.72 | 3700 | 1.2369 | 0.7561 | |
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| 0.8056 | 24.36 | 3800 | 1.2306 | 0.7533 | |
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| 0.8038 | 25.0 | 3900 | 1.2181 | 0.7665 | |
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| 0.7733 | 25.64 | 4000 | 1.2031 | 0.7655 | |
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| 0.7834 | 26.28 | 4100 | 1.2015 | 0.7637 | |
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| 0.7697 | 26.92 | 4200 | 1.1887 | 0.7637 | |
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| 0.7438 | 27.56 | 4300 | 1.1788 | 0.7674 | |
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| 0.733 | 28.21 | 4400 | 1.1740 | 0.7637 | |
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| 0.7244 | 28.85 | 4500 | 1.1671 | 0.7674 | |
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| 0.7091 | 29.49 | 4600 | 1.1563 | 0.7693 | |
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| 0.7138 | 30.13 | 4700 | 1.1543 | 0.7665 | |
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| 0.693 | 30.77 | 4800 | 1.1445 | 0.7665 | |
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| 0.6837 | 31.41 | 4900 | 1.1348 | 0.7731 | |
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| 0.6706 | 32.05 | 5000 | 1.1282 | 0.7702 | |
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| 0.6514 | 32.69 | 5100 | 1.1222 | 0.7712 | |
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| 0.6513 | 33.33 | 5200 | 1.1323 | 0.7665 | |
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| 0.6517 | 33.97 | 5300 | 1.1138 | 0.7693 | |
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| 0.637 | 34.62 | 5400 | 1.1014 | 0.7712 | |
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| 0.6277 | 35.26 | 5500 | 1.0949 | 0.7759 | |
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| 0.6103 | 35.9 | 5600 | 1.0882 | 0.7759 | |
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| 0.5916 | 36.54 | 5700 | 1.0888 | 0.7693 | |
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| 0.6101 | 37.18 | 5800 | 1.0890 | 0.7721 | |
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| 0.6042 | 37.82 | 5900 | 1.0779 | 0.7750 | |
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| 0.5618 | 38.46 | 6000 | 1.0769 | 0.7750 | |
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| 0.5878 | 39.1 | 6100 | 1.0638 | 0.7787 | |
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| 0.5522 | 39.74 | 6200 | 1.0611 | 0.7731 | |
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| 0.557 | 40.38 | 6300 | 1.0639 | 0.7768 | |
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| 0.5665 | 41.03 | 6400 | 1.0668 | 0.7740 | |
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| 0.5269 | 41.67 | 6500 | 1.0531 | 0.7759 | |
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| 0.5672 | 42.31 | 6600 | 1.0493 | 0.7759 | |
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| 0.5197 | 42.95 | 6700 | 1.0469 | 0.7759 | |
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| 0.5273 | 43.59 | 6800 | 1.0481 | 0.7740 | |
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| 0.5149 | 44.23 | 6900 | 1.0434 | 0.7712 | |
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| 0.5146 | 44.87 | 7000 | 1.0462 | 0.7787 | |
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| 0.5033 | 45.51 | 7100 | 1.0358 | 0.7759 | |
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| 0.5073 | 46.15 | 7200 | 1.0322 | 0.7806 | |
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| 0.4964 | 46.79 | 7300 | 1.0313 | 0.7815 | |
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| 0.4832 | 47.44 | 7400 | 1.0238 | 0.7797 | |
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| 0.484 | 48.08 | 7500 | 1.0355 | 0.7768 | |
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| 0.4856 | 48.72 | 7600 | 1.0263 | 0.7834 | |
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| 0.4688 | 49.36 | 7700 | 1.0178 | 0.7815 | |
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| 0.4628 | 50.0 | 7800 | 1.0161 | 0.7787 | |
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| 0.457 | 50.64 | 7900 | 1.0195 | 0.7768 | |
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| 0.4547 | 51.28 | 8000 | 1.0064 | 0.7825 | |
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| 0.4551 | 51.92 | 8100 | 1.0108 | 0.7806 | |
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| 0.4408 | 52.56 | 8200 | 1.0136 | 0.7768 | |
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| 0.4471 | 53.21 | 8300 | 1.0016 | 0.7834 | |
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| 0.4431 | 53.85 | 8400 | 1.0038 | 0.7863 | |
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| 0.4393 | 54.49 | 8500 | 1.0057 | 0.7815 | |
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| 0.4246 | 55.13 | 8600 | 0.9961 | 0.7797 | |
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| 0.4237 | 55.77 | 8700 | 1.0019 | 0.7806 | |
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| 0.4128 | 56.41 | 8800 | 0.9941 | 0.7806 | |
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| 0.4285 | 57.05 | 8900 | 0.9946 | 0.7815 | |
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| 0.4121 | 57.69 | 9000 | 0.9932 | 0.7806 | |
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| 0.4167 | 58.33 | 9100 | 0.9916 | 0.7825 | |
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| 0.4001 | 58.97 | 9200 | 0.9915 | 0.7825 | |
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| 0.4053 | 59.62 | 9300 | 0.9886 | 0.7815 | |
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| 0.3993 | 60.26 | 9400 | 0.9910 | 0.7844 | |
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| 0.3881 | 60.9 | 9500 | 0.9856 | 0.7863 | |
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| 0.3846 | 61.54 | 9600 | 0.9917 | 0.7806 | |
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| 0.3913 | 62.18 | 9700 | 0.9820 | 0.7834 | |
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| 0.3897 | 62.82 | 9800 | 0.9806 | 0.7844 | |
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| 0.3821 | 63.46 | 9900 | 0.9804 | 0.7825 | |
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| 0.3742 | 64.1 | 10000 | 0.9873 | 0.7844 | |
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| 0.3835 | 64.74 | 10100 | 0.9807 | 0.7834 | |
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| 0.3571 | 65.38 | 10200 | 0.9792 | 0.7844 | |
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| 0.38 | 66.03 | 10300 | 0.9786 | 0.7844 | |
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| 0.3612 | 66.67 | 10400 | 0.9769 | 0.7844 | |
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| 0.3628 | 67.31 | 10500 | 0.9991 | 0.7740 | |
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| 0.3655 | 67.95 | 10600 | 0.9737 | 0.7806 | |
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| 0.3489 | 68.59 | 10700 | 0.9745 | 0.7853 | |
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| 0.371 | 69.23 | 10800 | 0.9853 | 0.7787 | |
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| 0.3454 | 69.87 | 10900 | 0.9676 | 0.7825 | |
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| 0.3457 | 70.51 | 11000 | 0.9708 | 0.7853 | |
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| 0.3559 | 71.15 | 11100 | 0.9691 | 0.7863 | |
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| 0.3523 | 71.79 | 11200 | 0.9690 | 0.7872 | |
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| 0.3357 | 72.44 | 11300 | 0.9707 | 0.7815 | |
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| 0.344 | 73.08 | 11400 | 0.9690 | 0.7863 | |
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| 0.3527 | 73.72 | 11500 | 0.9788 | 0.7825 | |
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| 0.327 | 74.36 | 11600 | 0.9703 | 0.7825 | |
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| 0.3376 | 75.0 | 11700 | 0.9770 | 0.7787 | |
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| 0.3518 | 75.64 | 11800 | 0.9718 | 0.7834 | |
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| 0.3031 | 76.28 | 11900 | 0.9736 | 0.7863 | |
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| 0.3404 | 76.92 | 12000 | 0.9661 | 0.7825 | |
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| 0.3243 | 77.56 | 12100 | 0.9731 | 0.7853 | |
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| 0.3381 | 78.21 | 12200 | 0.9685 | 0.7900 | |
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| 0.3258 | 78.85 | 12300 | 0.9691 | 0.7844 | |
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| 0.3149 | 79.49 | 12400 | 0.9615 | 0.7844 | |
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| 0.3234 | 80.13 | 12500 | 0.9661 | 0.7853 | |
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| 0.3296 | 80.77 | 12600 | 0.9722 | 0.7815 | |
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| 0.3215 | 81.41 | 12700 | 0.9672 | 0.7834 | |
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| 0.3121 | 82.05 | 12800 | 0.9641 | 0.7834 | |
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| 0.3163 | 82.69 | 12900 | 0.9636 | 0.7834 | |
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| 0.3225 | 83.33 | 13000 | 0.9649 | 0.7853 | |
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| 0.3136 | 83.97 | 13100 | 0.9652 | 0.7825 | |
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| 0.3172 | 84.62 | 13200 | 0.9639 | 0.7853 | |
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| 0.3098 | 85.26 | 13300 | 0.9671 | 0.7834 | |
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| 0.3081 | 85.9 | 13400 | 0.9627 | 0.7806 | |
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| 0.3099 | 86.54 | 13500 | 0.9626 | 0.7815 | |
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| 0.3144 | 87.18 | 13600 | 0.9612 | 0.7815 | |
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| 0.2952 | 87.82 | 13700 | 0.9645 | 0.7863 | |
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| 0.3092 | 88.46 | 13800 | 0.9604 | 0.7853 | |
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| 0.3193 | 89.1 | 13900 | 0.9630 | 0.7844 | |
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| 0.3005 | 89.74 | 14000 | 0.9667 | 0.7815 | |
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| 0.2928 | 90.38 | 14100 | 0.9638 | 0.7844 | |
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| 0.315 | 91.03 | 14200 | 0.9644 | 0.7844 | |
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| 0.3095 | 91.67 | 14300 | 0.9637 | 0.7834 | |
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| 0.3036 | 92.31 | 14400 | 0.9615 | 0.7834 | |
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| 0.298 | 92.95 | 14500 | 0.9617 | 0.7844 | |
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| 0.2944 | 93.59 | 14600 | 0.9658 | 0.7834 | |
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| 0.3065 | 94.23 | 14700 | 0.9625 | 0.7834 | |
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| 0.2983 | 94.87 | 14800 | 0.9622 | 0.7844 | |
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| 0.2953 | 95.51 | 14900 | 0.9626 | 0.7834 | |
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| 0.3063 | 96.15 | 15000 | 0.9608 | 0.7853 | |
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| 0.3058 | 96.79 | 15100 | 0.9631 | 0.7853 | |
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| 0.2974 | 97.44 | 15200 | 0.9614 | 0.7844 | |
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| 0.3004 | 98.08 | 15300 | 0.9608 | 0.7844 | |
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| 0.3001 | 98.72 | 15400 | 0.9613 | 0.7853 | |
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| 0.2968 | 99.36 | 15500 | 0.9623 | 0.7853 | |
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| 0.2985 | 100.0 | 15600 | 0.9618 | 0.7844 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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