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vit-base-patch16-224-RU5-40

Image Classification
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  2. model.safetensors +1 -1
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README.md ADDED
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+ ---
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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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+ model-index:
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+ - name: vit-base-patch16-224-RU5-40
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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: 0.8
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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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+ # vit-base-patch16-224-RU5-40
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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: 0.8564
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+ - Accuracy: 0.8
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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: 5e-05
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+ - train_batch_size: 32
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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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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.05
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+ - num_epochs: 40
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.3806 | 0.95 | 14 | 1.3385 | 0.4833 |
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+ | 1.3323 | 1.97 | 29 | 1.1803 | 0.6 |
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+ | 1.1086 | 2.98 | 44 | 0.9835 | 0.6333 |
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+ | 0.927 | 4.0 | 59 | 0.8340 | 0.7167 |
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+ | 0.6591 | 4.95 | 73 | 0.7843 | 0.7167 |
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+ | 0.5201 | 5.97 | 88 | 0.7683 | 0.7167 |
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+ | 0.3763 | 6.98 | 103 | 0.7880 | 0.6833 |
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+ | 0.26 | 8.0 | 118 | 0.6876 | 0.7667 |
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+ | 0.2219 | 8.95 | 132 | 0.7188 | 0.7833 |
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+ | 0.2243 | 9.97 | 147 | 0.8730 | 0.7 |
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+ | 0.178 | 10.98 | 162 | 0.6872 | 0.7833 |
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+ | 0.1944 | 12.0 | 177 | 0.6150 | 0.85 |
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+ | 0.1422 | 12.95 | 191 | 0.6832 | 0.7833 |
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+ | 0.1117 | 13.97 | 206 | 0.7590 | 0.7833 |
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+ | 0.117 | 14.98 | 221 | 0.8429 | 0.7667 |
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+ | 0.1176 | 16.0 | 236 | 0.9741 | 0.7667 |
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+ | 0.1081 | 16.95 | 250 | 0.9106 | 0.7833 |
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+ | 0.0928 | 17.97 | 265 | 0.9179 | 0.7333 |
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+ | 0.0848 | 18.98 | 280 | 0.9695 | 0.7667 |
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+ | 0.1045 | 20.0 | 295 | 0.8805 | 0.8 |
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+ | 0.1159 | 20.95 | 309 | 0.9458 | 0.7667 |
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+ | 0.0748 | 21.97 | 324 | 0.8463 | 0.7667 |
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+ | 0.0641 | 22.98 | 339 | 0.8815 | 0.8 |
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+ | 0.0799 | 24.0 | 354 | 0.9426 | 0.75 |
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+ | 0.0921 | 24.95 | 368 | 0.9212 | 0.75 |
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+ | 0.0602 | 25.97 | 383 | 0.9828 | 0.75 |
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+ | 0.059 | 26.98 | 398 | 0.8861 | 0.8 |
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+ | 0.0669 | 28.0 | 413 | 0.9302 | 0.7333 |
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+ | 0.0508 | 28.95 | 427 | 1.0306 | 0.7167 |
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+ | 0.0585 | 29.97 | 442 | 0.9149 | 0.75 |
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+ | 0.0619 | 30.98 | 457 | 0.8942 | 0.7833 |
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+ | 0.0626 | 32.0 | 472 | 0.9069 | 0.7667 |
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+ | 0.0575 | 32.95 | 486 | 0.8656 | 0.8 |
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+ | 0.0483 | 33.97 | 501 | 0.8779 | 0.8167 |
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+ | 0.0576 | 34.98 | 516 | 0.9078 | 0.7833 |
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+ | 0.0633 | 36.0 | 531 | 0.8880 | 0.8 |
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+ | 0.0511 | 36.95 | 545 | 0.8573 | 0.8 |
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+ | 0.049 | 37.97 | 560 | 0.8564 | 0.8 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.1.2+cu118
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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