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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-tiny-1k-224 |
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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: convnextv2-tiny-1k-224-finetuned-bottomwear |
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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: train |
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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: 0.875 |
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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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# convnextv2-tiny-1k-224-finetuned-bottomwear |
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This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3252 |
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- Accuracy: 0.875 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 150 |
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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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| No log | 0.8889 | 6 | 2.0789 | 0.0938 | |
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| 2.1005 | 1.9259 | 13 | 2.0276 | 0.125 | |
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| 2.0321 | 2.9630 | 20 | 1.9456 | 0.2812 | |
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| 2.0321 | 4.0 | 27 | 1.8393 | 0.4583 | |
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| 1.9151 | 4.8889 | 33 | 1.7343 | 0.5833 | |
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| 1.7396 | 5.9259 | 40 | 1.5972 | 0.6042 | |
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| 1.7396 | 6.9630 | 47 | 1.4546 | 0.6771 | |
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| 1.5392 | 8.0 | 54 | 1.2943 | 0.7396 | |
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| 1.3096 | 8.8889 | 60 | 1.1409 | 0.7396 | |
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| 1.3096 | 9.9259 | 67 | 0.9841 | 0.8229 | |
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| 1.1062 | 10.9630 | 74 | 0.8512 | 0.8229 | |
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| 0.896 | 12.0 | 81 | 0.7128 | 0.8542 | |
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| 0.896 | 12.8889 | 87 | 0.6366 | 0.8333 | |
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| 0.712 | 13.9259 | 94 | 0.5419 | 0.8646 | |
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| 0.6231 | 14.9630 | 101 | 0.5082 | 0.8646 | |
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| 0.6231 | 16.0 | 108 | 0.4674 | 0.875 | |
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| 0.4962 | 16.8889 | 114 | 0.4480 | 0.8542 | |
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| 0.4322 | 17.9259 | 121 | 0.4138 | 0.875 | |
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| 0.4322 | 18.9630 | 128 | 0.3947 | 0.8646 | |
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| 0.3937 | 20.0 | 135 | 0.3827 | 0.8646 | |
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| 0.3377 | 20.8889 | 141 | 0.3626 | 0.8646 | |
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| 0.3377 | 21.9259 | 148 | 0.3579 | 0.8646 | |
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| 0.3099 | 22.9630 | 155 | 0.3558 | 0.8646 | |
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| 0.2895 | 24.0 | 162 | 0.3243 | 0.8646 | |
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| 0.2895 | 24.8889 | 168 | 0.3473 | 0.875 | |
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| 0.2732 | 25.9259 | 175 | 0.3461 | 0.8646 | |
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| 0.2447 | 26.9630 | 182 | 0.3450 | 0.8958 | |
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| 0.2447 | 28.0 | 189 | 0.3603 | 0.8958 | |
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| 0.2009 | 28.8889 | 195 | 0.3214 | 0.8854 | |
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| 0.2064 | 29.9259 | 202 | 0.3043 | 0.875 | |
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| 0.2064 | 30.9630 | 209 | 0.2917 | 0.8958 | |
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| 0.2139 | 32.0 | 216 | 0.2860 | 0.8958 | |
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| 0.1732 | 32.8889 | 222 | 0.3314 | 0.8333 | |
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| 0.1732 | 33.9259 | 229 | 0.3391 | 0.875 | |
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| 0.2009 | 34.9630 | 236 | 0.3118 | 0.8958 | |
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| 0.1683 | 36.0 | 243 | 0.3162 | 0.875 | |
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| 0.1683 | 36.8889 | 249 | 0.3011 | 0.8646 | |
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| 0.16 | 37.9259 | 256 | 0.2981 | 0.8854 | |
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| 0.1448 | 38.9630 | 263 | 0.3417 | 0.9062 | |
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| 0.1272 | 40.0 | 270 | 0.3558 | 0.8646 | |
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| 0.1272 | 40.8889 | 276 | 0.3948 | 0.8542 | |
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| 0.1578 | 41.9259 | 283 | 0.3668 | 0.8646 | |
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| 0.1604 | 42.9630 | 290 | 0.3342 | 0.8958 | |
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| 0.1604 | 44.0 | 297 | 0.3141 | 0.9167 | |
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| 0.1251 | 44.8889 | 303 | 0.3266 | 0.8854 | |
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| 0.1449 | 45.9259 | 310 | 0.3438 | 0.8854 | |
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| 0.1449 | 46.9630 | 317 | 0.3383 | 0.875 | |
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| 0.1134 | 48.0 | 324 | 0.3341 | 0.8958 | |
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| 0.1558 | 48.8889 | 330 | 0.2855 | 0.8958 | |
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| 0.1558 | 49.9259 | 337 | 0.2843 | 0.8958 | |
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| 0.1433 | 50.9630 | 344 | 0.2879 | 0.8438 | |
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| 0.1207 | 52.0 | 351 | 0.2887 | 0.8854 | |
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| 0.1207 | 52.8889 | 357 | 0.3173 | 0.8958 | |
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| 0.1006 | 53.9259 | 364 | 0.2926 | 0.8854 | |
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| 0.1053 | 54.9630 | 371 | 0.2791 | 0.9062 | |
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| 0.1053 | 56.0 | 378 | 0.3276 | 0.875 | |
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| 0.106 | 56.8889 | 384 | 0.3224 | 0.875 | |
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| 0.1058 | 57.9259 | 391 | 0.3385 | 0.8854 | |
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| 0.1058 | 58.9630 | 398 | 0.3494 | 0.8958 | |
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| 0.0962 | 60.0 | 405 | 0.2798 | 0.8854 | |
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| 0.0883 | 60.8889 | 411 | 0.2934 | 0.8854 | |
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| 0.0883 | 61.9259 | 418 | 0.2956 | 0.875 | |
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| 0.084 | 62.9630 | 425 | 0.2918 | 0.8958 | |
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| 0.0808 | 64.0 | 432 | 0.3416 | 0.8854 | |
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| 0.0808 | 64.8889 | 438 | 0.3502 | 0.8854 | |
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| 0.0804 | 65.9259 | 445 | 0.2985 | 0.8958 | |
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| 0.0854 | 66.9630 | 452 | 0.2792 | 0.9062 | |
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| 0.0854 | 68.0 | 459 | 0.3644 | 0.8958 | |
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| 0.0887 | 68.8889 | 465 | 0.2684 | 0.9062 | |
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| 0.0671 | 69.9259 | 472 | 0.2802 | 0.8958 | |
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| 0.0671 | 70.9630 | 479 | 0.2901 | 0.9062 | |
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| 0.0704 | 72.0 | 486 | 0.3098 | 0.8854 | |
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| 0.0802 | 72.8889 | 492 | 0.2960 | 0.8854 | |
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| 0.0802 | 73.9259 | 499 | 0.2757 | 0.875 | |
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| 0.09 | 74.9630 | 506 | 0.3104 | 0.8646 | |
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| 0.0772 | 76.0 | 513 | 0.3120 | 0.8958 | |
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| 0.0772 | 76.8889 | 519 | 0.2803 | 0.9167 | |
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| 0.0725 | 77.9259 | 526 | 0.2825 | 0.8958 | |
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| 0.0684 | 78.9630 | 533 | 0.3255 | 0.875 | |
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| 0.0732 | 80.0 | 540 | 0.3091 | 0.9062 | |
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| 0.0732 | 80.8889 | 546 | 0.2876 | 0.9167 | |
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| 0.0743 | 81.9259 | 553 | 0.3035 | 0.8646 | |
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| 0.0807 | 82.9630 | 560 | 0.2751 | 0.9271 | |
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| 0.0807 | 84.0 | 567 | 0.2657 | 0.9167 | |
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| 0.0799 | 84.8889 | 573 | 0.2810 | 0.9062 | |
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| 0.0632 | 85.9259 | 580 | 0.3037 | 0.9062 | |
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| 0.0632 | 86.9630 | 587 | 0.3357 | 0.9062 | |
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| 0.0579 | 88.0 | 594 | 0.3171 | 0.8646 | |
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| 0.0593 | 88.8889 | 600 | 0.3223 | 0.8854 | |
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| 0.0593 | 89.9259 | 607 | 0.2977 | 0.8958 | |
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| 0.0418 | 90.9630 | 614 | 0.3380 | 0.9062 | |
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| 0.0647 | 92.0 | 621 | 0.2863 | 0.875 | |
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| 0.0647 | 92.8889 | 627 | 0.2899 | 0.9167 | |
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| 0.0649 | 93.9259 | 634 | 0.2853 | 0.8958 | |
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| 0.0538 | 94.9630 | 641 | 0.2452 | 0.8854 | |
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| 0.0538 | 96.0 | 648 | 0.2569 | 0.8958 | |
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| 0.0483 | 96.8889 | 654 | 0.2687 | 0.9062 | |
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| 0.0597 | 97.9259 | 661 | 0.3083 | 0.875 | |
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| 0.0597 | 98.9630 | 668 | 0.2929 | 0.8646 | |
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| 0.0544 | 100.0 | 675 | 0.3253 | 0.875 | |
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| 0.0585 | 100.8889 | 681 | 0.3394 | 0.8646 | |
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| 0.0585 | 101.9259 | 688 | 0.3748 | 0.8542 | |
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| 0.0563 | 102.9630 | 695 | 0.3890 | 0.8646 | |
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| 0.059 | 104.0 | 702 | 0.3460 | 0.8854 | |
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| 0.059 | 104.8889 | 708 | 0.3308 | 0.875 | |
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| 0.0601 | 105.9259 | 715 | 0.3228 | 0.875 | |
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| 0.0512 | 106.9630 | 722 | 0.3190 | 0.8854 | |
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| 0.0512 | 108.0 | 729 | 0.3028 | 0.875 | |
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| 0.0346 | 108.8889 | 735 | 0.3066 | 0.9062 | |
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| 0.0434 | 109.9259 | 742 | 0.2952 | 0.9062 | |
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| 0.0434 | 110.9630 | 749 | 0.3054 | 0.9062 | |
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| 0.0466 | 112.0 | 756 | 0.3087 | 0.8958 | |
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| 0.0402 | 112.8889 | 762 | 0.3212 | 0.875 | |
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| 0.0402 | 113.9259 | 769 | 0.3235 | 0.8854 | |
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| 0.0491 | 114.9630 | 776 | 0.3135 | 0.9062 | |
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| 0.0495 | 116.0 | 783 | 0.2991 | 0.8958 | |
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| 0.0495 | 116.8889 | 789 | 0.3051 | 0.8854 | |
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| 0.0536 | 117.9259 | 796 | 0.3339 | 0.875 | |
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| 0.0419 | 118.9630 | 803 | 0.3371 | 0.8646 | |
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| 0.0333 | 120.0 | 810 | 0.3376 | 0.8646 | |
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| 0.0333 | 120.8889 | 816 | 0.3379 | 0.8646 | |
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| 0.0376 | 121.9259 | 823 | 0.3373 | 0.8542 | |
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| 0.0397 | 122.9630 | 830 | 0.3437 | 0.8646 | |
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| 0.0397 | 124.0 | 837 | 0.3585 | 0.8646 | |
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| 0.0299 | 124.8889 | 843 | 0.3514 | 0.8646 | |
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| 0.0468 | 125.9259 | 850 | 0.3397 | 0.8646 | |
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| 0.0468 | 126.9630 | 857 | 0.3316 | 0.8542 | |
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| 0.0351 | 128.0 | 864 | 0.3334 | 0.8646 | |
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| 0.0439 | 128.8889 | 870 | 0.3324 | 0.8646 | |
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| 0.0439 | 129.9259 | 877 | 0.3290 | 0.8646 | |
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| 0.0478 | 130.9630 | 884 | 0.3256 | 0.875 | |
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| 0.0434 | 132.0 | 891 | 0.3253 | 0.875 | |
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| 0.0434 | 132.8889 | 897 | 0.3251 | 0.875 | |
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| 0.0374 | 133.3333 | 900 | 0.3252 | 0.875 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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