--- license: apache-2.0 base_model: facebook/convnextv2-tiny-1k-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall model-index: - name: convnextv2-tiny-1k-224-finetuned-neck-style 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: 0.8492753623188406 - name: Precision type: precision value: 0.8507158478342087 --- # convnextv2-tiny-1k-224-finetuned-neck-style 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. It achieves the following results on the evaluation set: - Loss: 0.6084 - Accuracy: 0.8493 - Precision: 0.8507 - Recall: 0.8493 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 | Precision | Recall | |:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:| | 1.613 | 0.9897 | 24 | 1.5833 | 0.2928 | 0.3248 | 0.2928 | | 1.5494 | 1.9794 | 48 | 1.4944 | 0.3681 | 0.4410 | 0.3681 | | 1.3989 | 2.9691 | 72 | 1.3424 | 0.5159 | 0.5262 | 0.5159 | | 1.2238 | 4.0 | 97 | 1.1162 | 0.6261 | 0.6666 | 0.6261 | | 0.9585 | 4.9897 | 121 | 0.8966 | 0.6986 | 0.7014 | 0.6986 | | 0.8934 | 5.9794 | 145 | 0.7638 | 0.7507 | 0.7490 | 0.7507 | | 0.7589 | 6.9691 | 169 | 0.6776 | 0.7652 | 0.7719 | 0.7652 | | 0.6746 | 8.0 | 194 | 0.6127 | 0.7623 | 0.7628 | 0.7623 | | 0.6048 | 8.9897 | 218 | 0.5221 | 0.8203 | 0.8217 | 0.8203 | | 0.531 | 9.9794 | 242 | 0.4931 | 0.8116 | 0.8204 | 0.8116 | | 0.57 | 10.9691 | 266 | 0.4480 | 0.8319 | 0.8345 | 0.8319 | | 0.4624 | 12.0 | 291 | 0.4214 | 0.8464 | 0.8460 | 0.8464 | | 0.417 | 12.9897 | 315 | 0.4439 | 0.8493 | 0.8486 | 0.8493 | | 0.3814 | 13.9794 | 339 | 0.4138 | 0.8464 | 0.8478 | 0.8464 | | 0.3737 | 14.9691 | 363 | 0.4139 | 0.8464 | 0.8466 | 0.8464 | | 0.3971 | 16.0 | 388 | 0.4119 | 0.8638 | 0.8665 | 0.8638 | | 0.343 | 16.9897 | 412 | 0.4421 | 0.8609 | 0.8659 | 0.8609 | | 0.3311 | 17.9794 | 436 | 0.4581 | 0.8493 | 0.8504 | 0.8493 | | 0.2652 | 18.9691 | 460 | 0.4563 | 0.8406 | 0.8441 | 0.8406 | | 0.3026 | 20.0 | 485 | 0.4536 | 0.8522 | 0.8549 | 0.8522 | | 0.2562 | 20.9897 | 509 | 0.4409 | 0.8464 | 0.8493 | 0.8464 | | 0.2282 | 21.9794 | 533 | 0.4389 | 0.8435 | 0.8451 | 0.8435 | | 0.2374 | 22.9691 | 557 | 0.4452 | 0.8580 | 0.8589 | 0.8580 | | 0.216 | 24.0 | 582 | 0.4375 | 0.8580 | 0.8581 | 0.8580 | | 0.2127 | 24.9897 | 606 | 0.4422 | 0.8580 | 0.8588 | 0.8580 | | 0.2004 | 25.9794 | 630 | 0.4635 | 0.8522 | 0.8519 | 0.8522 | | 0.2029 | 26.9691 | 654 | 0.5215 | 0.8493 | 0.8546 | 0.8493 | | 0.1794 | 28.0 | 679 | 0.4756 | 0.8638 | 0.8669 | 0.8638 | | 0.1835 | 28.9897 | 703 | 0.4728 | 0.8609 | 0.8650 | 0.8609 | | 0.1781 | 29.9794 | 727 | 0.4637 | 0.8551 | 0.8568 | 0.8551 | | 0.1671 | 30.9691 | 751 | 0.4856 | 0.8580 | 0.8599 | 0.8580 | | 0.1762 | 32.0 | 776 | 0.5008 | 0.8667 | 0.8684 | 0.8667 | | 0.1867 | 32.9897 | 800 | 0.5058 | 0.8580 | 0.8585 | 0.8580 | | 0.1409 | 33.9794 | 824 | 0.5490 | 0.8406 | 0.8409 | 0.8406 | | 0.1315 | 34.9691 | 848 | 0.5284 | 0.8348 | 0.8356 | 0.8348 | | 0.1315 | 36.0 | 873 | 0.5415 | 0.8464 | 0.8488 | 0.8464 | | 0.1974 | 36.9897 | 897 | 0.5194 | 0.8493 | 0.8536 | 0.8493 | | 0.1337 | 37.9794 | 921 | 0.5088 | 0.8609 | 0.8603 | 0.8609 | | 0.173 | 38.9691 | 945 | 0.4912 | 0.8667 | 0.8680 | 0.8667 | | 0.1409 | 40.0 | 970 | 0.5223 | 0.8493 | 0.8502 | 0.8493 | | 0.1379 | 40.9897 | 994 | 0.5204 | 0.8493 | 0.8487 | 0.8493 | | 0.1437 | 41.9794 | 1018 | 0.5860 | 0.8522 | 0.8551 | 0.8522 | | 0.1022 | 42.9691 | 1042 | 0.5461 | 0.8464 | 0.8492 | 0.8464 | | 0.1181 | 44.0 | 1067 | 0.5411 | 0.8551 | 0.8566 | 0.8551 | | 0.1212 | 44.9897 | 1091 | 0.5294 | 0.8580 | 0.8580 | 0.8580 | | 0.1049 | 45.9794 | 1115 | 0.5667 | 0.8493 | 0.8492 | 0.8493 | | 0.1132 | 46.9691 | 1139 | 0.5908 | 0.8464 | 0.8491 | 0.8464 | | 0.1313 | 48.0 | 1164 | 0.5996 | 0.8522 | 0.8582 | 0.8522 | | 0.1312 | 48.9897 | 1188 | 0.5430 | 0.8580 | 0.8607 | 0.8580 | | 0.0996 | 49.9794 | 1212 | 0.5777 | 0.8522 | 0.8561 | 0.8522 | | 0.1389 | 50.9691 | 1236 | 0.5758 | 0.8435 | 0.8486 | 0.8435 | | 0.1079 | 52.0 | 1261 | 0.5540 | 0.8580 | 0.8611 | 0.8580 | | 0.0972 | 52.9897 | 1285 | 0.5600 | 0.8551 | 0.8559 | 0.8551 | | 0.0985 | 53.9794 | 1309 | 0.5392 | 0.8638 | 0.8656 | 0.8638 | | 0.1112 | 54.9691 | 1333 | 0.5411 | 0.8638 | 0.8656 | 0.8638 | | 0.1308 | 56.0 | 1358 | 0.5445 | 0.8638 | 0.8654 | 0.8638 | | 0.1005 | 56.9897 | 1382 | 0.5554 | 0.8551 | 0.8551 | 0.8551 | | 0.0871 | 57.9794 | 1406 | 0.5966 | 0.8406 | 0.8441 | 0.8406 | | 0.1102 | 58.9691 | 1430 | 0.5807 | 0.8522 | 0.8543 | 0.8522 | | 0.1028 | 60.0 | 1455 | 0.5654 | 0.8435 | 0.8491 | 0.8435 | | 0.107 | 60.9897 | 1479 | 0.5779 | 0.8435 | 0.8461 | 0.8435 | | 0.0848 | 61.9794 | 1503 | 0.5843 | 0.8551 | 0.8569 | 0.8551 | | 0.0976 | 62.9691 | 1527 | 0.6162 | 0.8435 | 0.8454 | 0.8435 | | 0.0977 | 64.0 | 1552 | 0.5822 | 0.8464 | 0.8469 | 0.8464 | | 0.1256 | 64.9897 | 1576 | 0.5757 | 0.8493 | 0.8514 | 0.8493 | | 0.0883 | 65.9794 | 1600 | 0.5716 | 0.8464 | 0.8467 | 0.8464 | | 0.0808 | 66.9691 | 1624 | 0.5726 | 0.8551 | 0.8562 | 0.8551 | | 0.1034 | 68.0 | 1649 | 0.5413 | 0.8551 | 0.8549 | 0.8551 | | 0.0845 | 68.9897 | 1673 | 0.5826 | 0.8435 | 0.8477 | 0.8435 | | 0.0916 | 69.9794 | 1697 | 0.5661 | 0.8522 | 0.8522 | 0.8522 | | 0.0912 | 70.9691 | 1721 | 0.5771 | 0.8493 | 0.8498 | 0.8493 | | 0.0863 | 72.0 | 1746 | 0.5769 | 0.8551 | 0.8550 | 0.8551 | | 0.083 | 72.9897 | 1770 | 0.5860 | 0.8493 | 0.8486 | 0.8493 | | 0.0839 | 73.9794 | 1794 | 0.5647 | 0.8551 | 0.8551 | 0.8551 | | 0.0903 | 74.9691 | 1818 | 0.6012 | 0.8551 | 0.8535 | 0.8551 | | 0.074 | 76.0 | 1843 | 0.6048 | 0.8464 | 0.8461 | 0.8464 | | 0.0907 | 76.9897 | 1867 | 0.5807 | 0.8493 | 0.8495 | 0.8493 | | 0.0613 | 77.9794 | 1891 | 0.5775 | 0.8377 | 0.8382 | 0.8377 | | 0.0964 | 78.9691 | 1915 | 0.5759 | 0.8667 | 0.8676 | 0.8667 | | 0.0735 | 80.0 | 1940 | 0.5962 | 0.8551 | 0.8566 | 0.8551 | | 0.0663 | 80.9897 | 1964 | 0.5769 | 0.8435 | 0.8441 | 0.8435 | | 0.0719 | 81.9794 | 1988 | 0.5826 | 0.8493 | 0.8507 | 0.8493 | | 0.0718 | 82.9691 | 2012 | 0.5880 | 0.8580 | 0.8590 | 0.8580 | | 0.0925 | 84.0 | 2037 | 0.5986 | 0.8493 | 0.8513 | 0.8493 | | 0.0621 | 84.9897 | 2061 | 0.5915 | 0.8493 | 0.8497 | 0.8493 | | 0.059 | 85.9794 | 2085 | 0.5779 | 0.8580 | 0.8577 | 0.8580 | | 0.0806 | 86.9691 | 2109 | 0.5928 | 0.8493 | 0.8501 | 0.8493 | | 0.0617 | 88.0 | 2134 | 0.6062 | 0.8522 | 0.8520 | 0.8522 | | 0.0651 | 88.9897 | 2158 | 0.6067 | 0.8522 | 0.8519 | 0.8522 | | 0.0754 | 89.9794 | 2182 | 0.6108 | 0.8551 | 0.8553 | 0.8551 | | 0.0682 | 90.9691 | 2206 | 0.6185 | 0.8493 | 0.8489 | 0.8493 | | 0.0763 | 92.0 | 2231 | 0.6168 | 0.8580 | 0.8575 | 0.8580 | | 0.0703 | 92.9897 | 2255 | 0.6259 | 0.8522 | 0.8521 | 0.8522 | | 0.0861 | 93.9794 | 2279 | 0.6128 | 0.8551 | 0.8553 | 0.8551 | | 0.0807 | 94.9691 | 2303 | 0.6140 | 0.8551 | 0.8547 | 0.8551 | | 0.0621 | 96.0 | 2328 | 0.6133 | 0.8522 | 0.8532 | 0.8522 | | 0.0831 | 96.9897 | 2352 | 0.6101 | 0.8493 | 0.8507 | 0.8493 | | 0.0625 | 97.9794 | 2376 | 0.6097 | 0.8493 | 0.8507 | 0.8493 | | 0.0571 | 98.9691 | 2400 | 0.6084 | 0.8493 | 0.8507 | 0.8493 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1