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smids_1x_beit_base_adamax_0001_fold1

Image Classification
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/beit-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: smids_1x_beit_base_adamax_0001_fold1
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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: test
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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: 0.9031719532554258
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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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+ # smids_1x_beit_base_adamax_0001_fold1
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+
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+ This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-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: 0.8043
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+ - Accuracy: 0.9032
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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: 0.0001
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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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+ - 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: 0.1
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+ - num_epochs: 50
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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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+ | 0.3546 | 1.0 | 76 | 0.4865 | 0.7930 |
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+ | 0.2399 | 2.0 | 152 | 0.3349 | 0.8731 |
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+ | 0.136 | 3.0 | 228 | 0.2999 | 0.8831 |
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+ | 0.1619 | 4.0 | 304 | 0.4346 | 0.8698 |
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+ | 0.1213 | 5.0 | 380 | 0.4295 | 0.8748 |
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+ | 0.0741 | 6.0 | 456 | 0.4439 | 0.8881 |
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+ | 0.0995 | 7.0 | 532 | 0.5033 | 0.8815 |
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+ | 0.0126 | 8.0 | 608 | 0.4887 | 0.8982 |
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+ | 0.0174 | 9.0 | 684 | 0.6241 | 0.8848 |
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+ | 0.0036 | 10.0 | 760 | 0.5630 | 0.8898 |
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+ | 0.0047 | 11.0 | 836 | 0.6256 | 0.8898 |
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+ | 0.025 | 12.0 | 912 | 0.5949 | 0.8982 |
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+ | 0.0037 | 13.0 | 988 | 0.6192 | 0.8898 |
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+ | 0.0095 | 14.0 | 1064 | 0.6191 | 0.8982 |
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+ | 0.0074 | 15.0 | 1140 | 0.6693 | 0.8948 |
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+ | 0.0061 | 16.0 | 1216 | 0.6785 | 0.8915 |
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+ | 0.0003 | 17.0 | 1292 | 0.6825 | 0.8898 |
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+ | 0.0001 | 18.0 | 1368 | 0.7695 | 0.8865 |
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+ | 0.0107 | 19.0 | 1444 | 0.6909 | 0.8965 |
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+ | 0.0125 | 20.0 | 1520 | 0.7272 | 0.8915 |
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+ | 0.0016 | 21.0 | 1596 | 0.7585 | 0.8848 |
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+ | 0.0028 | 22.0 | 1672 | 0.7524 | 0.8898 |
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+ | 0.0017 | 23.0 | 1748 | 0.8165 | 0.8865 |
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+ | 0.0046 | 24.0 | 1824 | 0.7698 | 0.8848 |
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+ | 0.004 | 25.0 | 1900 | 0.8060 | 0.8915 |
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+ | 0.003 | 26.0 | 1976 | 0.7525 | 0.8998 |
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+ | 0.0039 | 27.0 | 2052 | 0.8271 | 0.8848 |
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+ | 0.0001 | 28.0 | 2128 | 0.7809 | 0.8965 |
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+ | 0.0001 | 29.0 | 2204 | 0.8142 | 0.8948 |
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+ | 0.0 | 30.0 | 2280 | 0.7973 | 0.8881 |
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+ | 0.0023 | 31.0 | 2356 | 0.7501 | 0.8998 |
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+ | 0.0061 | 32.0 | 2432 | 0.7903 | 0.8932 |
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+ | 0.0085 | 33.0 | 2508 | 0.7939 | 0.8932 |
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+ | 0.0036 | 34.0 | 2584 | 0.7959 | 0.8982 |
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+ | 0.0089 | 35.0 | 2660 | 0.7729 | 0.8982 |
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+ | 0.0 | 36.0 | 2736 | 0.8000 | 0.8948 |
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+ | 0.0038 | 37.0 | 2812 | 0.7757 | 0.8998 |
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+ | 0.0028 | 38.0 | 2888 | 0.7902 | 0.8898 |
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+ | 0.0024 | 39.0 | 2964 | 0.7785 | 0.9048 |
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+ | 0.0001 | 40.0 | 3040 | 0.7668 | 0.9082 |
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+ | 0.0052 | 41.0 | 3116 | 0.7725 | 0.9048 |
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+ | 0.0 | 42.0 | 3192 | 0.7888 | 0.9032 |
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+ | 0.0 | 43.0 | 3268 | 0.7934 | 0.9032 |
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+ | 0.0 | 44.0 | 3344 | 0.7962 | 0.9032 |
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+ | 0.0053 | 45.0 | 3420 | 0.8046 | 0.9032 |
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+ | 0.0 | 46.0 | 3496 | 0.7994 | 0.9032 |
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+ | 0.003 | 47.0 | 3572 | 0.8008 | 0.9032 |
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+ | 0.0032 | 48.0 | 3648 | 0.8023 | 0.9032 |
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+ | 0.0018 | 49.0 | 3724 | 0.8041 | 0.9032 |
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+ | 0.0052 | 50.0 | 3800 | 0.8043 | 0.9032 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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