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nqvii
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fold_5

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
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End of training Fold 5

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  1. README.md +124 -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_5
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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: 0.9584775086505191
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+ - name: Recall
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+ type: recall
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+ value: 0.96098499101133
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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_5
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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: 1.1415
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+ - Accuracy: 0.9585
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+ - F1 Score: 0.9604
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+ - Recall: 0.9610
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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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+ | 2.7679 | 1.0 | 19 | 2.7376 | 0.3356 | 0.2925 | 0.3120 |
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+ | 2.6412 | 2.0 | 38 | 2.5528 | 0.4567 | 0.3907 | 0.4082 |
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+ | 2.2891 | 3.0 | 57 | 2.3004 | 0.6125 | 0.5730 | 0.5668 |
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+ | 1.9662 | 4.0 | 76 | 1.9746 | 0.7509 | 0.7537 | 0.7393 |
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+ | 1.5390 | 5.0 | 95 | 1.6498 | 0.8374 | 0.8436 | 0.8325 |
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+ | 1.3191 | 6.0 | 114 | 1.4345 | 0.8789 | 0.8884 | 0.8848 |
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+ | 1.1787 | 7.0 | 133 | 1.3242 | 0.8962 | 0.9025 | 0.8958 |
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+ | 1.1229 | 8.0 | 152 | 1.2923 | 0.8927 | 0.8969 | 0.8856 |
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+ | 1.0605 | 9.0 | 171 | 1.2239 | 0.9239 | 0.9278 | 0.9253 |
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+ | 1.0463 | 10.0 | 190 | 1.2173 | 0.9308 | 0.9339 | 0.9300 |
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+ | 1.0295 | 11.0 | 209 | 1.2042 | 0.9412 | 0.9437 | 0.9426 |
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+ | 1.0210 | 12.0 | 228 | 1.1941 | 0.9412 | 0.9437 | 0.9426 |
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+ | 1.0174 | 13.0 | 247 | 1.1868 | 0.9446 | 0.9474 | 0.9464 |
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+ | 1.0169 | 14.0 | 266 | 1.1932 | 0.9343 | 0.9366 | 0.9337 |
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+ | 1.0026 | 15.0 | 285 | 1.2058 | 0.9377 | 0.9401 | 0.9362 |
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+ | 1.0057 | 16.0 | 304 | 1.1721 | 0.9446 | 0.9467 | 0.9462 |
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+ | 0.9959 | 17.0 | 323 | 1.1659 | 0.9412 | 0.9435 | 0.9425 |
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+ | 0.9822 | 18.0 | 342 | 1.1760 | 0.9412 | 0.9437 | 0.9412 |
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+ | 0.9793 | 19.0 | 361 | 1.1816 | 0.9412 | 0.9434 | 0.9399 |
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+ | 0.9772 | 20.0 | 380 | 1.1641 | 0.9516 | 0.9530 | 0.9510 |
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+ | 0.9745 | 21.0 | 399 | 1.1622 | 0.9516 | 0.9533 | 0.9522 |
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+ | 0.9757 | 22.0 | 418 | 1.1569 | 0.9550 | 0.9573 | 0.9574 |
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+ | 0.9639 | 23.0 | 437 | 1.1593 | 0.9446 | 0.9467 | 0.9462 |
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+ | 0.9746 | 24.0 | 456 | 1.1542 | 0.9481 | 0.9505 | 0.9500 |
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+ | 0.9691 | 25.0 | 475 | 1.1527 | 0.9550 | 0.9567 | 0.9573 |
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+ | 0.9656 | 26.0 | 494 | 1.1619 | 0.9481 | 0.9501 | 0.9496 |
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+ | 0.9629 | 27.0 | 513 | 1.1539 | 0.9516 | 0.9533 | 0.9534 |
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+ | 0.9690 | 28.0 | 532 | 1.1523 | 0.9516 | 0.9539 | 0.9534 |
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+ | 0.9660 | 29.0 | 551 | 1.1474 | 0.9550 | 0.9573 | 0.9573 |
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+ | 0.9728 | 30.0 | 570 | 1.1424 | 0.9585 | 0.9603 | 0.9608 |
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+ | 0.9644 | 31.0 | 589 | 1.1415 | 0.9585 | 0.9604 | 0.9610 |
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+ | 0.9610 | 32.0 | 608 | 1.1462 | 0.9550 | 0.9563 | 0.9558 |
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+ | 0.9623 | 33.0 | 627 | 1.1423 | 0.9550 | 0.9566 | 0.9571 |
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+ | 0.9630 | 34.0 | 646 | 1.1416 | 0.9516 | 0.9536 | 0.9546 |
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+ | 0.9664 | 35.0 | 665 | 1.1438 | 0.9550 | 0.9566 | 0.9571 |
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+ | 0.9643 | 36.0 | 684 | 1.1454 | 0.9550 | 0.9566 | 0.9571 |
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+ | 0.9670 | 37.0 | 703 | 1.1431 | 0.9550 | 0.9566 | 0.9571 |
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+ | 0.9631 | 38.0 | 722 | 1.1490 | 0.9481 | 0.9503 | 0.9498 |
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+ | 0.9589 | 39.0 | 741 | 1.1512 | 0.9481 | 0.9503 | 0.9498 |
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+ | 0.9620 | 40.0 | 760 | 1.1427 | 0.9516 | 0.9535 | 0.9535 |
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+ | 0.9552 | 41.0 | 779 | 1.1427 | 0.9516 | 0.9535 | 0.9535 |
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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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