Commit
•
69f744d
1
Parent(s):
eddb68a
End of training
Browse files- README.md +186 -0
- all_results.json +15 -0
- config.json +57 -0
- eval_results.json +10 -0
- model.safetensors +3 -0
- preprocessor_config.json +22 -0
- train_results.json +8 -0
- trainer_state.json +2811 -0
- training_args.bin +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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| 3 |
+
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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| 9 |
+
- accuracy
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| 10 |
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- precision
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| 11 |
+
- recall
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| 12 |
+
model-index:
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- name: convnextv2-tiny-1k-224-finetuned-neck-style
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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: 0.8492753623188406
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- name: Precision
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type: precision
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value: 0.8507158478342087
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- name: Recall
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type: recall
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value: 0.8492753623188406
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---
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+
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| 36 |
+
<!-- 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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| 38 |
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# convnextv2-tiny-1k-224-finetuned-neck-style
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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: 0.6084
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- Accuracy: 0.8493
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- Precision: 0.8507
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- Recall: 0.8493
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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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| 66 |
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- train_batch_size: 32
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| 67 |
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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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| 72 |
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- lr_scheduler_type: linear
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| 73 |
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 100
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### Training results
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| 78 |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall |
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| 79 |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|
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| 80 |
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| 1.613 | 0.9897 | 24 | 1.5833 | 0.2928 | 0.3248 | 0.2928 |
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| 1.5494 | 1.9794 | 48 | 1.4944 | 0.3681 | 0.4410 | 0.3681 |
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| 1.3989 | 2.9691 | 72 | 1.3424 | 0.5159 | 0.5262 | 0.5159 |
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| 1.2238 | 4.0 | 97 | 1.1162 | 0.6261 | 0.6666 | 0.6261 |
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| 0.9585 | 4.9897 | 121 | 0.8966 | 0.6986 | 0.7014 | 0.6986 |
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| 0.8934 | 5.9794 | 145 | 0.7638 | 0.7507 | 0.7490 | 0.7507 |
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| 0.7589 | 6.9691 | 169 | 0.6776 | 0.7652 | 0.7719 | 0.7652 |
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| 0.6746 | 8.0 | 194 | 0.6127 | 0.7623 | 0.7628 | 0.7623 |
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| 0.6048 | 8.9897 | 218 | 0.5221 | 0.8203 | 0.8217 | 0.8203 |
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| 0.531 | 9.9794 | 242 | 0.4931 | 0.8116 | 0.8204 | 0.8116 |
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| 0.57 | 10.9691 | 266 | 0.4480 | 0.8319 | 0.8345 | 0.8319 |
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| 0.4624 | 12.0 | 291 | 0.4214 | 0.8464 | 0.8460 | 0.8464 |
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| 0.417 | 12.9897 | 315 | 0.4439 | 0.8493 | 0.8486 | 0.8493 |
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| 0.3814 | 13.9794 | 339 | 0.4138 | 0.8464 | 0.8478 | 0.8464 |
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| 0.3737 | 14.9691 | 363 | 0.4139 | 0.8464 | 0.8466 | 0.8464 |
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| 0.3971 | 16.0 | 388 | 0.4119 | 0.8638 | 0.8665 | 0.8638 |
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| 0.343 | 16.9897 | 412 | 0.4421 | 0.8609 | 0.8659 | 0.8609 |
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| 0.3311 | 17.9794 | 436 | 0.4581 | 0.8493 | 0.8504 | 0.8493 |
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| 0.2652 | 18.9691 | 460 | 0.4563 | 0.8406 | 0.8441 | 0.8406 |
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| 0.3026 | 20.0 | 485 | 0.4536 | 0.8522 | 0.8549 | 0.8522 |
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| 0.2562 | 20.9897 | 509 | 0.4409 | 0.8464 | 0.8493 | 0.8464 |
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| 0.2282 | 21.9794 | 533 | 0.4389 | 0.8435 | 0.8451 | 0.8435 |
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| 0.2374 | 22.9691 | 557 | 0.4452 | 0.8580 | 0.8589 | 0.8580 |
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| 0.216 | 24.0 | 582 | 0.4375 | 0.8580 | 0.8581 | 0.8580 |
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| 0.2127 | 24.9897 | 606 | 0.4422 | 0.8580 | 0.8588 | 0.8580 |
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| 0.2004 | 25.9794 | 630 | 0.4635 | 0.8522 | 0.8519 | 0.8522 |
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| 0.2029 | 26.9691 | 654 | 0.5215 | 0.8493 | 0.8546 | 0.8493 |
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| 0.1794 | 28.0 | 679 | 0.4756 | 0.8638 | 0.8669 | 0.8638 |
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| 0.1835 | 28.9897 | 703 | 0.4728 | 0.8609 | 0.8650 | 0.8609 |
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| 0.1781 | 29.9794 | 727 | 0.4637 | 0.8551 | 0.8568 | 0.8551 |
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| 0.1671 | 30.9691 | 751 | 0.4856 | 0.8580 | 0.8599 | 0.8580 |
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| 111 |
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| 0.1762 | 32.0 | 776 | 0.5008 | 0.8667 | 0.8684 | 0.8667 |
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| 0.1867 | 32.9897 | 800 | 0.5058 | 0.8580 | 0.8585 | 0.8580 |
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| 0.1409 | 33.9794 | 824 | 0.5490 | 0.8406 | 0.8409 | 0.8406 |
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| 0.1315 | 34.9691 | 848 | 0.5284 | 0.8348 | 0.8356 | 0.8348 |
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| 115 |
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| 0.1315 | 36.0 | 873 | 0.5415 | 0.8464 | 0.8488 | 0.8464 |
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| 0.1974 | 36.9897 | 897 | 0.5194 | 0.8493 | 0.8536 | 0.8493 |
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| 117 |
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| 0.1337 | 37.9794 | 921 | 0.5088 | 0.8609 | 0.8603 | 0.8609 |
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| 118 |
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| 0.173 | 38.9691 | 945 | 0.4912 | 0.8667 | 0.8680 | 0.8667 |
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| 119 |
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| 0.1409 | 40.0 | 970 | 0.5223 | 0.8493 | 0.8502 | 0.8493 |
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| 0.1379 | 40.9897 | 994 | 0.5204 | 0.8493 | 0.8487 | 0.8493 |
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| 0.1437 | 41.9794 | 1018 | 0.5860 | 0.8522 | 0.8551 | 0.8522 |
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| 0.1022 | 42.9691 | 1042 | 0.5461 | 0.8464 | 0.8492 | 0.8464 |
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| 0.1181 | 44.0 | 1067 | 0.5411 | 0.8551 | 0.8566 | 0.8551 |
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| 124 |
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| 0.1212 | 44.9897 | 1091 | 0.5294 | 0.8580 | 0.8580 | 0.8580 |
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| 125 |
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| 0.1049 | 45.9794 | 1115 | 0.5667 | 0.8493 | 0.8492 | 0.8493 |
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| 126 |
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| 0.1132 | 46.9691 | 1139 | 0.5908 | 0.8464 | 0.8491 | 0.8464 |
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| 127 |
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| 0.1313 | 48.0 | 1164 | 0.5996 | 0.8522 | 0.8582 | 0.8522 |
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| 128 |
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| 0.1312 | 48.9897 | 1188 | 0.5430 | 0.8580 | 0.8607 | 0.8580 |
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| 129 |
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| 0.0996 | 49.9794 | 1212 | 0.5777 | 0.8522 | 0.8561 | 0.8522 |
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| 130 |
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| 0.1389 | 50.9691 | 1236 | 0.5758 | 0.8435 | 0.8486 | 0.8435 |
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| 131 |
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| 0.1079 | 52.0 | 1261 | 0.5540 | 0.8580 | 0.8611 | 0.8580 |
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| 132 |
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| 0.0972 | 52.9897 | 1285 | 0.5600 | 0.8551 | 0.8559 | 0.8551 |
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| 133 |
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| 0.0985 | 53.9794 | 1309 | 0.5392 | 0.8638 | 0.8656 | 0.8638 |
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| 134 |
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| 0.1112 | 54.9691 | 1333 | 0.5411 | 0.8638 | 0.8656 | 0.8638 |
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| 135 |
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| 0.1308 | 56.0 | 1358 | 0.5445 | 0.8638 | 0.8654 | 0.8638 |
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| 136 |
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| 0.1005 | 56.9897 | 1382 | 0.5554 | 0.8551 | 0.8551 | 0.8551 |
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| 137 |
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| 0.0871 | 57.9794 | 1406 | 0.5966 | 0.8406 | 0.8441 | 0.8406 |
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| 0.1102 | 58.9691 | 1430 | 0.5807 | 0.8522 | 0.8543 | 0.8522 |
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| 139 |
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| 0.1028 | 60.0 | 1455 | 0.5654 | 0.8435 | 0.8491 | 0.8435 |
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| 0.107 | 60.9897 | 1479 | 0.5779 | 0.8435 | 0.8461 | 0.8435 |
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| 0.0848 | 61.9794 | 1503 | 0.5843 | 0.8551 | 0.8569 | 0.8551 |
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| 0.0976 | 62.9691 | 1527 | 0.6162 | 0.8435 | 0.8454 | 0.8435 |
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| 143 |
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| 0.0977 | 64.0 | 1552 | 0.5822 | 0.8464 | 0.8469 | 0.8464 |
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| 144 |
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| 0.1256 | 64.9897 | 1576 | 0.5757 | 0.8493 | 0.8514 | 0.8493 |
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| 0.0883 | 65.9794 | 1600 | 0.5716 | 0.8464 | 0.8467 | 0.8464 |
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| 146 |
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| 0.0808 | 66.9691 | 1624 | 0.5726 | 0.8551 | 0.8562 | 0.8551 |
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| 147 |
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| 0.1034 | 68.0 | 1649 | 0.5413 | 0.8551 | 0.8549 | 0.8551 |
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| 148 |
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| 0.0845 | 68.9897 | 1673 | 0.5826 | 0.8435 | 0.8477 | 0.8435 |
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| 149 |
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| 0.0916 | 69.9794 | 1697 | 0.5661 | 0.8522 | 0.8522 | 0.8522 |
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| 150 |
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| 0.0912 | 70.9691 | 1721 | 0.5771 | 0.8493 | 0.8498 | 0.8493 |
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| 151 |
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| 0.0863 | 72.0 | 1746 | 0.5769 | 0.8551 | 0.8550 | 0.8551 |
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| 152 |
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| 0.083 | 72.9897 | 1770 | 0.5860 | 0.8493 | 0.8486 | 0.8493 |
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| 153 |
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| 0.0839 | 73.9794 | 1794 | 0.5647 | 0.8551 | 0.8551 | 0.8551 |
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| 154 |
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| 0.0903 | 74.9691 | 1818 | 0.6012 | 0.8551 | 0.8535 | 0.8551 |
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| 155 |
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| 0.074 | 76.0 | 1843 | 0.6048 | 0.8464 | 0.8461 | 0.8464 |
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| 156 |
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| 0.0907 | 76.9897 | 1867 | 0.5807 | 0.8493 | 0.8495 | 0.8493 |
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| 157 |
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| 0.0613 | 77.9794 | 1891 | 0.5775 | 0.8377 | 0.8382 | 0.8377 |
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| 158 |
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| 0.0964 | 78.9691 | 1915 | 0.5759 | 0.8667 | 0.8676 | 0.8667 |
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| 159 |
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| 0.0735 | 80.0 | 1940 | 0.5962 | 0.8551 | 0.8566 | 0.8551 |
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| 160 |
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| 0.0663 | 80.9897 | 1964 | 0.5769 | 0.8435 | 0.8441 | 0.8435 |
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| 161 |
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| 0.0719 | 81.9794 | 1988 | 0.5826 | 0.8493 | 0.8507 | 0.8493 |
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| 162 |
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| 0.0718 | 82.9691 | 2012 | 0.5880 | 0.8580 | 0.8590 | 0.8580 |
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| 163 |
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| 0.0925 | 84.0 | 2037 | 0.5986 | 0.8493 | 0.8513 | 0.8493 |
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| 164 |
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| 0.0621 | 84.9897 | 2061 | 0.5915 | 0.8493 | 0.8497 | 0.8493 |
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| 165 |
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| 0.059 | 85.9794 | 2085 | 0.5779 | 0.8580 | 0.8577 | 0.8580 |
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| 166 |
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| 0.0806 | 86.9691 | 2109 | 0.5928 | 0.8493 | 0.8501 | 0.8493 |
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| 167 |
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| 0.0617 | 88.0 | 2134 | 0.6062 | 0.8522 | 0.8520 | 0.8522 |
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| 168 |
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| 0.0651 | 88.9897 | 2158 | 0.6067 | 0.8522 | 0.8519 | 0.8522 |
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| 169 |
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| 0.0754 | 89.9794 | 2182 | 0.6108 | 0.8551 | 0.8553 | 0.8551 |
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| 170 |
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| 0.0682 | 90.9691 | 2206 | 0.6185 | 0.8493 | 0.8489 | 0.8493 |
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| 171 |
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| 0.0763 | 92.0 | 2231 | 0.6168 | 0.8580 | 0.8575 | 0.8580 |
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| 172 |
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| 0.0703 | 92.9897 | 2255 | 0.6259 | 0.8522 | 0.8521 | 0.8522 |
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| 173 |
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| 0.0861 | 93.9794 | 2279 | 0.6128 | 0.8551 | 0.8553 | 0.8551 |
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| 174 |
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| 0.0807 | 94.9691 | 2303 | 0.6140 | 0.8551 | 0.8547 | 0.8551 |
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| 175 |
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| 0.0621 | 96.0 | 2328 | 0.6133 | 0.8522 | 0.8532 | 0.8522 |
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| 176 |
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| 0.0831 | 96.9897 | 2352 | 0.6101 | 0.8493 | 0.8507 | 0.8493 |
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| 177 |
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| 0.0625 | 97.9794 | 2376 | 0.6097 | 0.8493 | 0.8507 | 0.8493 |
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| 0.0571 | 98.9691 | 2400 | 0.6084 | 0.8493 | 0.8507 | 0.8493 |
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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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all_results.json
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 98.96907216494846,
|
| 3 |
+
"eval_accuracy": 0.8492753623188406,
|
| 4 |
+
"eval_loss": 0.6083797812461853,
|
| 5 |
+
"eval_precision": 0.8507158478342087,
|
| 6 |
+
"eval_recall": 0.8492753623188406,
|
| 7 |
+
"eval_runtime": 1.7897,
|
| 8 |
+
"eval_samples_per_second": 192.765,
|
| 9 |
+
"eval_steps_per_second": 6.146,
|
| 10 |
+
"total_flos": 7.732715563096474e+18,
|
| 11 |
+
"train_loss": 0.2344164727628231,
|
| 12 |
+
"train_runtime": 4723.8268,
|
| 13 |
+
"train_samples_per_second": 65.709,
|
| 14 |
+
"train_steps_per_second": 0.508
|
| 15 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
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|
|
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|
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|
|
|
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|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "facebook/convnextv2-tiny-1k-224",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"ConvNextV2ForImageClassification"
|
| 5 |
+
],
|
| 6 |
+
"depths": [
|
| 7 |
+
3,
|
| 8 |
+
3,
|
| 9 |
+
9,
|
| 10 |
+
3
|
| 11 |
+
],
|
| 12 |
+
"drop_path_rate": 0.0,
|
| 13 |
+
"hidden_act": "gelu",
|
| 14 |
+
"hidden_sizes": [
|
| 15 |
+
96,
|
| 16 |
+
192,
|
| 17 |
+
384,
|
| 18 |
+
768
|
| 19 |
+
],
|
| 20 |
+
"id2label": {
|
| 21 |
+
"0": "mandarin neck",
|
| 22 |
+
"1": "notch neck",
|
| 23 |
+
"2": "round neck",
|
| 24 |
+
"3": "shirt collar",
|
| 25 |
+
"4": "v neck"
|
| 26 |
+
},
|
| 27 |
+
"image_size": 224,
|
| 28 |
+
"initializer_range": 0.02,
|
| 29 |
+
"label2id": {
|
| 30 |
+
"mandarin neck": 0,
|
| 31 |
+
"notch neck": 1,
|
| 32 |
+
"round neck": 2,
|
| 33 |
+
"shirt collar": 3,
|
| 34 |
+
"v neck": 4
|
| 35 |
+
},
|
| 36 |
+
"layer_norm_eps": 1e-12,
|
| 37 |
+
"model_type": "convnextv2",
|
| 38 |
+
"num_channels": 3,
|
| 39 |
+
"num_stages": 4,
|
| 40 |
+
"out_features": [
|
| 41 |
+
"stage4"
|
| 42 |
+
],
|
| 43 |
+
"out_indices": [
|
| 44 |
+
4
|
| 45 |
+
],
|
| 46 |
+
"patch_size": 4,
|
| 47 |
+
"problem_type": "single_label_classification",
|
| 48 |
+
"stage_names": [
|
| 49 |
+
"stem",
|
| 50 |
+
"stage1",
|
| 51 |
+
"stage2",
|
| 52 |
+
"stage3",
|
| 53 |
+
"stage4"
|
| 54 |
+
],
|
| 55 |
+
"torch_dtype": "float32",
|
| 56 |
+
"transformers_version": "4.44.0"
|
| 57 |
+
}
|
eval_results.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 98.96907216494846,
|
| 3 |
+
"eval_accuracy": 0.8492753623188406,
|
| 4 |
+
"eval_loss": 0.6083797812461853,
|
| 5 |
+
"eval_precision": 0.8507158478342087,
|
| 6 |
+
"eval_recall": 0.8492753623188406,
|
| 7 |
+
"eval_runtime": 1.7897,
|
| 8 |
+
"eval_samples_per_second": 192.765,
|
| 9 |
+
"eval_steps_per_second": 6.146
|
| 10 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fd531331fc29d04c342346e07e220f05b4ea254206e16c40e28e5a1524c30d57
|
| 3 |
+
size 111505052
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"crop_pct": 0.875,
|
| 3 |
+
"do_normalize": true,
|
| 4 |
+
"do_rescale": true,
|
| 5 |
+
"do_resize": true,
|
| 6 |
+
"image_mean": [
|
| 7 |
+
0.485,
|
| 8 |
+
0.456,
|
| 9 |
+
0.406
|
| 10 |
+
],
|
| 11 |
+
"image_processor_type": "ConvNextImageProcessor",
|
| 12 |
+
"image_std": [
|
| 13 |
+
0.229,
|
| 14 |
+
0.224,
|
| 15 |
+
0.225
|
| 16 |
+
],
|
| 17 |
+
"resample": 3,
|
| 18 |
+
"rescale_factor": 0.00392156862745098,
|
| 19 |
+
"size": {
|
| 20 |
+
"shortest_edge": 224
|
| 21 |
+
}
|
| 22 |
+
}
|
train_results.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 98.96907216494846,
|
| 3 |
+
"total_flos": 7.732715563096474e+18,
|
| 4 |
+
"train_loss": 0.2344164727628231,
|
| 5 |
+
"train_runtime": 4723.8268,
|
| 6 |
+
"train_samples_per_second": 65.709,
|
| 7 |
+
"train_steps_per_second": 0.508
|
| 8 |
+
}
|
trainer_state.json
ADDED
|
@@ -0,0 +1,2811 @@
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training_args.bin
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