End of training
Browse files- README.md +117 -0
- all_results.json +12 -0
- config.json +60 -0
- eval_results.json +8 -0
- model.safetensors +3 -0
- preprocessor_config.json +22 -0
- runs/Dec01_09-12-28_DESKTOP-SKBE9FB/events.out.tfevents.1733065949.DESKTOP-SKBE9FB.16948.0 +3 -0
- runs/Dec01_09-12-28_DESKTOP-SKBE9FB/events.out.tfevents.1733067415.DESKTOP-SKBE9FB.16948.1 +3 -0
- train_results.json +7 -0
- trainer_state.json +1350 -0
- training_args.bin +3 -0
README.md
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| 1 |
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---
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| 2 |
+
license: apache-2.0
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base_model: microsoft/swinv2-tiny-patch4-window8-256
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tags:
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- generated_from_trainer
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| 6 |
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datasets:
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- imagefolder
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| 8 |
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metrics:
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| 9 |
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- accuracy
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| 10 |
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model-index:
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- name: swinv2-tiny-patch4-window8-256-RD-aptos19
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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: validation
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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: 0.8836363636363637
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| 26 |
+
---
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| 27 |
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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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# swinv2-tiny-patch4-window8-256-RD-aptos19
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3399
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- Accuracy: 0.8836
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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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| 47 |
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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: 0.00015
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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| 62 |
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- lr_scheduler_type: linear
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| 63 |
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- lr_scheduler_warmup_ratio: 0.1
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| 64 |
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- num_epochs: 40
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| 65 |
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 69 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 70 |
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| 1.2888 | 0.99 | 40 | 1.2042 | 0.4927 |
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| 71 |
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| 0.8739 | 1.99 | 80 | 0.6776 | 0.7491 |
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| 72 |
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| 0.7913 | 2.98 | 120 | 0.5920 | 0.7618 |
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| 73 |
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| 0.6803 | 4.0 | 161 | 0.5377 | 0.7691 |
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| 74 |
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| 0.624 | 4.99 | 201 | 0.5212 | 0.7745 |
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| 75 |
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| 0.6068 | 5.99 | 241 | 0.5286 | 0.82 |
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| 76 |
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| 0.6274 | 6.98 | 281 | 0.4594 | 0.84 |
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| 77 |
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| 0.5571 | 8.0 | 322 | 0.4194 | 0.8491 |
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| 78 |
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| 0.5803 | 8.99 | 362 | 0.4615 | 0.82 |
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| 79 |
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| 0.5856 | 9.99 | 402 | 0.4559 | 0.8364 |
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| 80 |
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| 0.5364 | 10.98 | 442 | 0.3981 | 0.8582 |
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| 81 |
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| 0.5287 | 12.0 | 483 | 0.3939 | 0.8418 |
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| 82 |
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| 0.4689 | 12.99 | 523 | 0.4536 | 0.8327 |
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| 83 |
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| 0.422 | 13.99 | 563 | 0.3476 | 0.8745 |
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| 84 |
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| 0.5027 | 14.98 | 603 | 0.3864 | 0.8545 |
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| 85 |
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| 0.4776 | 16.0 | 644 | 0.3470 | 0.8636 |
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| 86 |
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| 0.4598 | 16.99 | 684 | 0.3690 | 0.8655 |
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| 87 |
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| 0.4846 | 17.99 | 724 | 0.3708 | 0.8655 |
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| 88 |
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| 0.4126 | 18.98 | 764 | 0.3506 | 0.86 |
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| 0.4497 | 20.0 | 805 | 0.3338 | 0.8655 |
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| 90 |
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| 0.4594 | 20.99 | 845 | 0.3469 | 0.8673 |
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| 91 |
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| 0.4163 | 21.99 | 885 | 0.3703 | 0.8582 |
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| 92 |
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| 0.3539 | 22.98 | 925 | 0.3594 | 0.8636 |
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| 93 |
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| 0.3751 | 24.0 | 966 | 0.3559 | 0.8582 |
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| 94 |
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| 0.3598 | 24.99 | 1006 | 0.3316 | 0.8818 |
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| 95 |
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| 0.3839 | 25.99 | 1046 | 0.3482 | 0.8764 |
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| 96 |
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| 0.3029 | 26.98 | 1086 | 0.3474 | 0.88 |
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| 97 |
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| 0.286 | 28.0 | 1127 | 0.3399 | 0.8836 |
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| 98 |
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| 0.361 | 28.99 | 1167 | 0.3660 | 0.8709 |
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| 99 |
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| 0.3283 | 29.99 | 1207 | 0.3968 | 0.8655 |
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| 100 |
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| 0.3192 | 30.98 | 1247 | 0.3601 | 0.8745 |
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| 101 |
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| 0.3127 | 32.0 | 1288 | 0.3793 | 0.8745 |
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| 102 |
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| 0.2907 | 32.99 | 1328 | 0.4207 | 0.8691 |
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| 103 |
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| 0.2715 | 33.99 | 1368 | 0.4298 | 0.8727 |
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| 104 |
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| 0.2984 | 34.98 | 1408 | 0.4065 | 0.8818 |
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| 105 |
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| 0.2584 | 36.0 | 1449 | 0.4050 | 0.8836 |
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| 106 |
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| 0.2137 | 36.99 | 1489 | 0.4129 | 0.8836 |
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| 107 |
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| 0.24 | 37.99 | 1529 | 0.4160 | 0.88 |
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| 108 |
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| 0.2326 | 38.98 | 1569 | 0.4104 | 0.8782 |
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| 109 |
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| 0.2121 | 39.75 | 1600 | 0.4130 | 0.8782 |
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| 110 |
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| 111 |
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| 112 |
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### Framework versions
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| 113 |
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| 114 |
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- Transformers 4.36.2
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| 115 |
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- Pytorch 2.1.2+cu118
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| 116 |
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- Datasets 2.16.1
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| 117 |
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- Tokenizers 0.15.0
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all_results.json
ADDED
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| 1 |
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{
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| 2 |
+
"epoch": 39.75,
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| 3 |
+
"eval_accuracy": 0.8836363636363637,
|
| 4 |
+
"eval_loss": 0.3399273157119751,
|
| 5 |
+
"eval_runtime": 3.0032,
|
| 6 |
+
"eval_samples_per_second": 183.136,
|
| 7 |
+
"eval_steps_per_second": 11.654,
|
| 8 |
+
"train_loss": 0.4701251982152462,
|
| 9 |
+
"train_runtime": 1462.7895,
|
| 10 |
+
"train_samples_per_second": 70.085,
|
| 11 |
+
"train_steps_per_second": 1.094
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| 12 |
+
}
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config.json
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{
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| 2 |
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"_name_or_path": "microsoft/swinv2-tiny-patch4-window8-256",
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| 3 |
+
"architectures": [
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| 4 |
+
"Swinv2ForImageClassification"
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| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.0,
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| 7 |
+
"depths": [
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| 8 |
+
2,
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| 9 |
+
2,
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| 10 |
+
6,
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| 11 |
+
2
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| 12 |
+
],
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| 13 |
+
"drop_path_rate": 0.1,
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| 14 |
+
"embed_dim": 96,
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| 15 |
+
"encoder_stride": 32,
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| 16 |
+
"hidden_act": "gelu",
|
| 17 |
+
"hidden_dropout_prob": 0.0,
|
| 18 |
+
"hidden_size": 768,
|
| 19 |
+
"id2label": {
|
| 20 |
+
"0": "Mild",
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| 21 |
+
"1": "Moderate",
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| 22 |
+
"2": "No_DR",
|
| 23 |
+
"3": "Proliferate_DR",
|
| 24 |
+
"4": "Severe"
|
| 25 |
+
},
|
| 26 |
+
"image_size": 256,
|
| 27 |
+
"initializer_range": 0.02,
|
| 28 |
+
"label2id": {
|
| 29 |
+
"Mild": 0,
|
| 30 |
+
"Moderate": 1,
|
| 31 |
+
"No_DR": 2,
|
| 32 |
+
"Proliferate_DR": 3,
|
| 33 |
+
"Severe": 4
|
| 34 |
+
},
|
| 35 |
+
"layer_norm_eps": 1e-05,
|
| 36 |
+
"mlp_ratio": 4.0,
|
| 37 |
+
"model_type": "swinv2",
|
| 38 |
+
"num_channels": 3,
|
| 39 |
+
"num_heads": [
|
| 40 |
+
3,
|
| 41 |
+
6,
|
| 42 |
+
12,
|
| 43 |
+
24
|
| 44 |
+
],
|
| 45 |
+
"num_layers": 4,
|
| 46 |
+
"patch_size": 4,
|
| 47 |
+
"path_norm": true,
|
| 48 |
+
"pretrained_window_sizes": [
|
| 49 |
+
0,
|
| 50 |
+
0,
|
| 51 |
+
0,
|
| 52 |
+
0
|
| 53 |
+
],
|
| 54 |
+
"problem_type": "single_label_classification",
|
| 55 |
+
"qkv_bias": true,
|
| 56 |
+
"torch_dtype": "float32",
|
| 57 |
+
"transformers_version": "4.36.2",
|
| 58 |
+
"use_absolute_embeddings": false,
|
| 59 |
+
"window_size": 8
|
| 60 |
+
}
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eval_results.json
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| 1 |
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{
|
| 2 |
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"epoch": 39.75,
|
| 3 |
+
"eval_accuracy": 0.8836363636363637,
|
| 4 |
+
"eval_loss": 0.3399273157119751,
|
| 5 |
+
"eval_runtime": 3.0032,
|
| 6 |
+
"eval_samples_per_second": 183.136,
|
| 7 |
+
"eval_steps_per_second": 11.654
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| 8 |
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:6774402736e4138f445efc5c4e4436abacf3a6bd35d451fb16e2a45b57bcbe5a
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| 3 |
+
size 110359372
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preprocessor_config.json
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| 1 |
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{
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| 2 |
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"do_normalize": true,
|
| 3 |
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"do_rescale": true,
|
| 4 |
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"do_resize": true,
|
| 5 |
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"image_mean": [
|
| 6 |
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0.485,
|
| 7 |
+
0.456,
|
| 8 |
+
0.406
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| 9 |
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],
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| 10 |
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"image_processor_type": "ViTImageProcessor",
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| 11 |
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"image_std": [
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| 12 |
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0.229,
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| 13 |
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0.224,
|
| 14 |
+
0.225
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| 15 |
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],
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| 16 |
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"resample": 3,
|
| 17 |
+
"rescale_factor": 0.00392156862745098,
|
| 18 |
+
"size": {
|
| 19 |
+
"height": 256,
|
| 20 |
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"width": 256
|
| 21 |
+
}
|
| 22 |
+
}
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runs/Dec01_09-12-28_DESKTOP-SKBE9FB/events.out.tfevents.1733065949.DESKTOP-SKBE9FB.16948.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:8d742326281dfc19e92a22ab433c4db12e4abc9b8c32aec923325029e340d952
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| 3 |
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size 43196
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runs/Dec01_09-12-28_DESKTOP-SKBE9FB/events.out.tfevents.1733067415.DESKTOP-SKBE9FB.16948.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:17202eaa0b36a0da82196c2d55408fa24015bd251bd41d37bd1779a673d28034
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size 411
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train_results.json
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{
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"epoch": 39.75,
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| 3 |
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"train_loss": 0.4701251982152462,
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| 4 |
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"train_runtime": 1462.7895,
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| 5 |
+
"train_samples_per_second": 70.085,
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| 6 |
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"train_steps_per_second": 1.094
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| 7 |
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}
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trainer_state.json
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