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hushem_5x_beit_base_rms_0001_fold4

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: hushem_5x_beit_base_rms_0001_fold4
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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: 0.7857142857142857
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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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+ # hushem_5x_beit_base_rms_0001_fold4
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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: 1.8159
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+ - Accuracy: 0.7857
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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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+ | 1.4247 | 1.0 | 28 | 1.3411 | 0.2381 |
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+ | 1.3595 | 2.0 | 56 | 1.2501 | 0.4286 |
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+ | 1.3116 | 3.0 | 84 | 1.5240 | 0.2381 |
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+ | 1.303 | 4.0 | 112 | 1.0491 | 0.5238 |
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+ | 1.1942 | 5.0 | 140 | 0.8861 | 0.7143 |
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+ | 1.1712 | 6.0 | 168 | 0.9106 | 0.5238 |
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+ | 0.977 | 7.0 | 196 | 1.1447 | 0.6905 |
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+ | 0.9351 | 8.0 | 224 | 0.7191 | 0.7619 |
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+ | 0.8453 | 9.0 | 252 | 1.3331 | 0.5714 |
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+ | 0.8831 | 10.0 | 280 | 0.8305 | 0.6905 |
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+ | 0.8349 | 11.0 | 308 | 0.6872 | 0.7619 |
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+ | 0.845 | 12.0 | 336 | 0.7545 | 0.7619 |
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+ | 0.784 | 13.0 | 364 | 0.7961 | 0.7857 |
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+ | 0.7404 | 14.0 | 392 | 0.6338 | 0.8095 |
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+ | 0.6277 | 15.0 | 420 | 0.7200 | 0.7143 |
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+ | 0.6386 | 16.0 | 448 | 0.7383 | 0.8095 |
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+ | 0.6167 | 17.0 | 476 | 0.5440 | 0.8095 |
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+ | 0.5129 | 18.0 | 504 | 0.7061 | 0.7619 |
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+ | 0.3836 | 19.0 | 532 | 0.7181 | 0.7381 |
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+ | 0.3202 | 20.0 | 560 | 0.4277 | 0.8095 |
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+ | 0.1958 | 21.0 | 588 | 1.1637 | 0.7381 |
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+ | 0.2343 | 22.0 | 616 | 1.0581 | 0.8095 |
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+ | 0.2016 | 23.0 | 644 | 0.8968 | 0.7857 |
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+ | 0.116 | 24.0 | 672 | 1.0426 | 0.7857 |
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+ | 0.1027 | 25.0 | 700 | 0.6841 | 0.8333 |
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+ | 0.1133 | 26.0 | 728 | 0.8260 | 0.8095 |
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+ | 0.1258 | 27.0 | 756 | 1.3215 | 0.7619 |
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+ | 0.0595 | 28.0 | 784 | 1.0509 | 0.8810 |
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+ | 0.0945 | 29.0 | 812 | 1.3868 | 0.7857 |
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+ | 0.0022 | 30.0 | 840 | 1.7553 | 0.8095 |
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+ | 0.0004 | 31.0 | 868 | 1.9423 | 0.7857 |
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+ | 0.0466 | 32.0 | 896 | 2.0945 | 0.8095 |
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+ | 0.0367 | 33.0 | 924 | 1.6928 | 0.8095 |
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+ | 0.1032 | 34.0 | 952 | 1.3572 | 0.8571 |
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+ | 0.0331 | 35.0 | 980 | 2.0437 | 0.8095 |
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+ | 0.0001 | 36.0 | 1008 | 2.0414 | 0.8333 |
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+ | 0.0286 | 37.0 | 1036 | 2.0546 | 0.7619 |
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+ | 0.009 | 38.0 | 1064 | 2.8381 | 0.7857 |
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+ | 0.0573 | 39.0 | 1092 | 2.4470 | 0.7857 |
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+ | 0.0497 | 40.0 | 1120 | 1.8192 | 0.7857 |
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+ | 0.0003 | 41.0 | 1148 | 2.1421 | 0.7143 |
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+ | 0.0003 | 42.0 | 1176 | 2.2125 | 0.7381 |
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+ | 0.0001 | 43.0 | 1204 | 2.1555 | 0.7619 |
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+ | 0.0002 | 44.0 | 1232 | 1.8154 | 0.7381 |
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+ | 0.0197 | 45.0 | 1260 | 1.7188 | 0.7381 |
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+ | 0.0002 | 46.0 | 1288 | 1.6637 | 0.8095 |
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+ | 0.0152 | 47.0 | 1316 | 1.6954 | 0.8095 |
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+ | 0.0001 | 48.0 | 1344 | 1.8153 | 0.7857 |
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+ | 0.0002 | 49.0 | 1372 | 1.8159 | 0.7857 |
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+ | 0.0 | 50.0 | 1400 | 1.8159 | 0.7857 |
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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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