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vit-base-patch16-224-ve-U13b-80RX

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-ve-U13b-80RX
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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: 0.8043478260869565
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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-ve-U13b-80RX
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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: 0.8808
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+ - Accuracy: 0.8043
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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: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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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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+ | 1.3805 | 0.97 | 25 | 1.3567 | 0.5 |
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+ | 1.1576 | 1.98 | 51 | 1.1360 | 0.4348 |
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+ | 0.9331 | 2.99 | 77 | 0.8531 | 0.8043 |
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+ | 0.6657 | 4.0 | 103 | 0.6856 | 0.7826 |
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+ | 0.5642 | 4.97 | 128 | 0.6162 | 0.7826 |
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+ | 0.3632 | 5.98 | 154 | 0.5902 | 0.8043 |
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+ | 0.3384 | 6.99 | 180 | 0.4995 | 0.8043 |
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+ | 0.2261 | 8.0 | 206 | 0.6854 | 0.7609 |
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+ | 0.2066 | 8.97 | 231 | 0.5605 | 0.7826 |
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+ | 0.1635 | 9.98 | 257 | 0.7209 | 0.7391 |
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+ | 0.1829 | 10.99 | 283 | 0.9293 | 0.6957 |
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+ | 0.1455 | 12.0 | 309 | 0.5999 | 0.7826 |
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+ | 0.1072 | 12.97 | 334 | 0.7919 | 0.7826 |
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+ | 0.1059 | 13.98 | 360 | 0.7782 | 0.8043 |
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+ | 0.0971 | 14.99 | 386 | 0.8249 | 0.8043 |
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+ | 0.0456 | 16.0 | 412 | 0.7965 | 0.7826 |
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+ | 0.0483 | 16.97 | 437 | 0.7163 | 0.8261 |
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+ | 0.0832 | 17.98 | 463 | 0.8122 | 0.7826 |
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+ | 0.055 | 18.99 | 489 | 0.8250 | 0.7826 |
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+ | 0.0753 | 20.0 | 515 | 0.6866 | 0.8478 |
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+ | 0.14 | 20.97 | 540 | 0.6456 | 0.8696 |
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+ | 0.0506 | 21.98 | 566 | 0.9127 | 0.7826 |
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+ | 0.0963 | 22.99 | 592 | 0.6365 | 0.8261 |
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+ | 0.0612 | 24.0 | 618 | 0.8252 | 0.8043 |
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+ | 0.0875 | 24.97 | 643 | 0.8844 | 0.7391 |
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+ | 0.1041 | 25.98 | 669 | 0.6594 | 0.8261 |
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+ | 0.0512 | 26.99 | 695 | 0.9883 | 0.7826 |
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+ | 0.0675 | 28.0 | 721 | 0.9216 | 0.8043 |
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+ | 0.0492 | 28.97 | 746 | 0.9284 | 0.8043 |
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+ | 0.0679 | 29.98 | 772 | 0.9341 | 0.7826 |
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+ | 0.0996 | 30.99 | 798 | 0.9608 | 0.8043 |
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+ | 0.0729 | 32.0 | 824 | 1.0155 | 0.7826 |
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+ | 0.0296 | 32.97 | 849 | 1.0314 | 0.7826 |
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+ | 0.0414 | 33.98 | 875 | 0.8358 | 0.8043 |
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+ | 0.04 | 34.99 | 901 | 0.8912 | 0.8043 |
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+ | 0.0179 | 36.0 | 927 | 0.8544 | 0.8043 |
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+ | 0.0665 | 36.97 | 952 | 0.9154 | 0.8043 |
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+ | 0.0413 | 37.98 | 978 | 0.8834 | 0.8043 |
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+ | 0.04 | 38.83 | 1000 | 0.8808 | 0.8043 |
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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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