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nqvii
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fold_2

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
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End of training Fold 2

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  1. README.md +131 -0
  2. config.json +37 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +23 -0
  5. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224
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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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+ - recall
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+ model-index:
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+ - name: fold_2
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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: None
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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: 0.9551724137931035
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+ - name: Recall
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+ type: recall
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+ value: 0.9574229691876751
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+ ---
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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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+
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+ # fold_2
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1210
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+ - Accuracy: 0.9552
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+ - F1 Score: 0.9586
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+ - Recall: 0.9574
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 150
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+ - num_epochs: 100
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+ - label_smoothing_factor: 0.15
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
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+ | 2.9670 | 1.0 | 19 | 2.9365 | 0.2310 | 0.2142 | 0.2367 |
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+ | 2.8214 | 2.0 | 38 | 2.7318 | 0.3138 | 0.2945 | 0.3061 |
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+ | 2.5025 | 3.0 | 57 | 2.4676 | 0.4621 | 0.4648 | 0.4657 |
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+ | 2.1814 | 4.0 | 76 | 2.1569 | 0.6414 | 0.6613 | 0.6742 |
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+ | 1.7422 | 5.0 | 95 | 1.8021 | 0.7966 | 0.8177 | 0.8209 |
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+ | 1.4256 | 6.0 | 114 | 1.5250 | 0.8586 | 0.8736 | 0.8760 |
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+ | 1.2174 | 7.0 | 133 | 1.3764 | 0.8966 | 0.9043 | 0.9064 |
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+ | 1.1838 | 8.0 | 152 | 1.3033 | 0.9276 | 0.9313 | 0.9306 |
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+ | 1.1151 | 9.0 | 171 | 1.2473 | 0.9345 | 0.9384 | 0.9391 |
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+ | 1.0729 | 10.0 | 190 | 1.2358 | 0.9345 | 0.9381 | 0.9393 |
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+ | 1.0619 | 11.0 | 209 | 1.2143 | 0.9379 | 0.9416 | 0.9428 |
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+ | 1.0402 | 12.0 | 228 | 1.1999 | 0.9414 | 0.9450 | 0.9477 |
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+ | 1.0234 | 13.0 | 247 | 1.2005 | 0.9448 | 0.9483 | 0.9526 |
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+ | 0.9990 | 14.0 | 266 | 1.1818 | 0.9414 | 0.9443 | 0.9440 |
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+ | 1.0022 | 15.0 | 285 | 1.1740 | 0.9276 | 0.9317 | 0.9306 |
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+ | 1.0124 | 16.0 | 304 | 1.1663 | 0.9414 | 0.9452 | 0.9489 |
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+ | 1.0106 | 17.0 | 323 | 1.1633 | 0.9414 | 0.9452 | 0.9489 |
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+ | 0.9904 | 18.0 | 342 | 1.1482 | 0.9517 | 0.9547 | 0.9561 |
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+ | 0.9845 | 19.0 | 361 | 1.1522 | 0.9483 | 0.9515 | 0.9524 |
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+ | 0.9829 | 20.0 | 380 | 1.1542 | 0.9448 | 0.9479 | 0.9502 |
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+ | 0.9808 | 21.0 | 399 | 1.1519 | 0.9414 | 0.9450 | 0.9453 |
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+ | 0.9780 | 22.0 | 418 | 1.1522 | 0.9379 | 0.9421 | 0.9428 |
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+ | 0.9700 | 23.0 | 437 | 1.1469 | 0.9448 | 0.9486 | 0.9488 |
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+ | 0.9899 | 24.0 | 456 | 1.1524 | 0.9448 | 0.9483 | 0.9453 |
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+ | 0.9710 | 25.0 | 475 | 1.1482 | 0.9414 | 0.9450 | 0.9477 |
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+ | 0.9649 | 26.0 | 494 | 1.1427 | 0.9379 | 0.9415 | 0.9442 |
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+ | 0.9655 | 27.0 | 513 | 1.1497 | 0.9379 | 0.9423 | 0.9465 |
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+ | 0.9712 | 28.0 | 532 | 1.1389 | 0.9414 | 0.9453 | 0.9476 |
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+ | 0.9629 | 29.0 | 551 | 1.1383 | 0.9379 | 0.9413 | 0.9416 |
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+ | 0.9616 | 30.0 | 570 | 1.1358 | 0.9517 | 0.9547 | 0.9561 |
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+ | 0.9715 | 31.0 | 589 | 1.1387 | 0.9448 | 0.9477 | 0.9489 |
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+ | 0.9589 | 32.0 | 608 | 1.1361 | 0.9483 | 0.9528 | 0.9512 |
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+ | 0.9800 | 33.0 | 627 | 1.1569 | 0.9414 | 0.9460 | 0.9502 |
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+ | 0.9658 | 34.0 | 646 | 1.1392 | 0.9483 | 0.9530 | 0.9524 |
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+ | 0.9648 | 35.0 | 665 | 1.1371 | 0.9448 | 0.9496 | 0.9475 |
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+ | 0.9629 | 36.0 | 684 | 1.1359 | 0.9517 | 0.9555 | 0.9524 |
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+ | 0.9599 | 37.0 | 703 | 1.1223 | 0.9517 | 0.9558 | 0.9562 |
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+ | 0.9649 | 38.0 | 722 | 1.1210 | 0.9552 | 0.9586 | 0.9574 |
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+ | 0.9641 | 39.0 | 741 | 1.1382 | 0.9517 | 0.9555 | 0.9573 |
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+ | 0.9593 | 40.0 | 760 | 1.1376 | 0.9517 | 0.9555 | 0.9524 |
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+ | 0.9619 | 41.0 | 779 | 1.1370 | 0.9448 | 0.9485 | 0.9502 |
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+ | 0.9573 | 42.0 | 798 | 1.1355 | 0.9517 | 0.9558 | 0.9562 |
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+ | 0.9648 | 43.0 | 817 | 1.1430 | 0.9448 | 0.9498 | 0.9536 |
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+ | 0.9605 | 44.0 | 836 | 1.1383 | 0.9483 | 0.9528 | 0.9512 |
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+ | 0.9601 | 45.0 | 855 | 1.1421 | 0.9414 | 0.9471 | 0.9475 |
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+ | 0.9610 | 46.0 | 874 | 1.1446 | 0.9379 | 0.9431 | 0.9452 |
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+ | 0.9625 | 47.0 | 893 | 1.1427 | 0.9414 | 0.9462 | 0.9488 |
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+ | 0.9602 | 48.0 | 912 | 1.1389 | 0.9414 | 0.9460 | 0.9476 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 5.0.0
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+ - Pytorch 2.10.0+cu128
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+ - Datasets 5.0.0
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+ - Tokenizers 0.22.2
config.json ADDED
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+ {
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "dtype": "float32",
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+ "encoder_stride": 16,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "Bacterial Leaf Blight",
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+ "1": "Brown Spot",
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+ "2": "Healthy Leaf",
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+ "3": "Tungro Virus"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "Bacterial Leaf Blight": "0",
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+ "Brown Spot": "1",
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+ "Healthy Leaf": "2",
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+ "Tungro Virus": "3"
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 12,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 16,
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+ "pooler_act": "tanh",
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+ "pooler_output_size": 768,
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+ "qkv_bias": true,
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+ "transformers_version": "5.0.0",
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+ "use_cache": false
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+ }
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+ "do_normalize": true,
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+ "do_rescale": true,
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+ "do_resize": true,
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+ "image_mean": [
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+ "image_processor_type": "ViTImageProcessor",
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+ "image_std": [
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+ "resample": 2,
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+ "size": {
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