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vit-base-patch16-224-RU2-40

Image Classification
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vit
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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-RU2-40
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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: 0.7666666666666667
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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-RU2-40
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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: 1.2597
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+ - Accuracy: 0.7667
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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: 5.5e-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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+ | 1.3226 | 0.99 | 38 | 1.2293 | 0.6 |
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+ | 0.9048 | 2.0 | 77 | 0.7969 | 0.7 |
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+ | 0.4039 | 2.99 | 115 | 0.6800 | 0.7167 |
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+ | 0.281 | 4.0 | 154 | 0.8892 | 0.7667 |
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+ | 0.1755 | 4.99 | 192 | 0.9072 | 0.7333 |
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+ | 0.1035 | 6.0 | 231 | 0.8036 | 0.8167 |
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+ | 0.1275 | 6.99 | 269 | 0.8627 | 0.8 |
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+ | 0.107 | 8.0 | 308 | 0.8713 | 0.8 |
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+ | 0.0984 | 8.99 | 346 | 0.9660 | 0.8 |
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+ | 0.0823 | 10.0 | 385 | 1.0704 | 0.7833 |
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+ | 0.0771 | 10.99 | 423 | 0.9409 | 0.7667 |
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+ | 0.0527 | 12.0 | 462 | 1.0052 | 0.7833 |
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+ | 0.0708 | 12.99 | 500 | 0.9578 | 0.8 |
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+ | 0.0562 | 14.0 | 539 | 1.0712 | 0.8167 |
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+ | 0.0467 | 14.99 | 577 | 1.0586 | 0.8167 |
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+ | 0.0445 | 16.0 | 616 | 1.2066 | 0.7667 |
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+ | 0.0474 | 16.99 | 654 | 1.1863 | 0.75 |
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+ | 0.0263 | 18.0 | 693 | 1.1207 | 0.8167 |
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+ | 0.0307 | 18.99 | 731 | 1.1813 | 0.8167 |
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+ | 0.0393 | 20.0 | 770 | 1.3761 | 0.75 |
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+ | 0.0475 | 20.99 | 808 | 1.3008 | 0.7667 |
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+ | 0.0215 | 22.0 | 847 | 1.2625 | 0.7333 |
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+ | 0.0311 | 22.99 | 885 | 1.1508 | 0.8 |
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+ | 0.027 | 24.0 | 924 | 1.3035 | 0.7667 |
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+ | 0.0251 | 24.99 | 962 | 1.2270 | 0.7667 |
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+ | 0.0161 | 26.0 | 1001 | 1.1470 | 0.8167 |
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+ | 0.0258 | 26.99 | 1039 | 1.1473 | 0.8167 |
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+ | 0.0142 | 28.0 | 1078 | 1.2326 | 0.7667 |
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+ | 0.0151 | 28.99 | 1116 | 1.3978 | 0.7667 |
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+ | 0.021 | 30.0 | 1155 | 1.2003 | 0.8333 |
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+ | 0.0158 | 30.99 | 1193 | 1.2488 | 0.7667 |
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+ | 0.0163 | 32.0 | 1232 | 1.3232 | 0.75 |
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+ | 0.0143 | 32.99 | 1270 | 1.2467 | 0.8 |
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+ | 0.02 | 34.0 | 1309 | 1.3176 | 0.7833 |
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+ | 0.0128 | 34.99 | 1347 | 1.3083 | 0.7667 |
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+ | 0.0144 | 36.0 | 1386 | 1.3080 | 0.7667 |
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+ | 0.0109 | 36.99 | 1424 | 1.2999 | 0.8 |
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+ | 0.0082 | 38.0 | 1463 | 1.2718 | 0.8 |
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+ | 0.0064 | 38.99 | 1501 | 1.2588 | 0.7667 |
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+ | 0.0097 | 39.48 | 1520 | 1.2597 | 0.7667 |
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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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