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hushem_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: hushem_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: 0.7333333333333333
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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_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: 1.1942
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+ - Accuracy: 0.7333
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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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+ | No log | 1.0 | 6 | 1.1737 | 0.5111 |
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+ | 1.2996 | 2.0 | 12 | 0.6731 | 0.7111 |
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+ | 1.2996 | 3.0 | 18 | 0.5816 | 0.7778 |
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+ | 0.3034 | 4.0 | 24 | 0.5950 | 0.7778 |
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+ | 0.0484 | 5.0 | 30 | 0.7873 | 0.7333 |
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+ | 0.0484 | 6.0 | 36 | 0.7472 | 0.7556 |
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+ | 0.0106 | 7.0 | 42 | 0.8528 | 0.8 |
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+ | 0.0106 | 8.0 | 48 | 0.7211 | 0.7778 |
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+ | 0.0205 | 9.0 | 54 | 0.6347 | 0.7778 |
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+ | 0.0012 | 10.0 | 60 | 0.6115 | 0.8 |
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+ | 0.0012 | 11.0 | 66 | 0.6050 | 0.8222 |
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+ | 0.0005 | 12.0 | 72 | 0.6253 | 0.8222 |
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+ | 0.0005 | 13.0 | 78 | 0.7723 | 0.8 |
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+ | 0.0021 | 14.0 | 84 | 0.9287 | 0.8 |
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+ | 0.0003 | 15.0 | 90 | 1.0136 | 0.7778 |
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+ | 0.0003 | 16.0 | 96 | 0.9985 | 0.7778 |
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+ | 0.0004 | 17.0 | 102 | 0.9348 | 0.7778 |
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+ | 0.0004 | 18.0 | 108 | 0.8985 | 0.8 |
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+ | 0.0003 | 19.0 | 114 | 0.8733 | 0.8222 |
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+ | 0.0009 | 20.0 | 120 | 0.8790 | 0.8222 |
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+ | 0.0009 | 21.0 | 126 | 1.1330 | 0.7778 |
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+ | 0.0002 | 22.0 | 132 | 1.2620 | 0.7556 |
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+ | 0.0002 | 23.0 | 138 | 1.3184 | 0.7556 |
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+ | 0.0003 | 24.0 | 144 | 1.3104 | 0.7778 |
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+ | 0.0003 | 25.0 | 150 | 1.2554 | 0.7556 |
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+ | 0.0003 | 26.0 | 156 | 1.2162 | 0.7556 |
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+ | 0.0002 | 27.0 | 162 | 1.1923 | 0.7333 |
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+ | 0.0002 | 28.0 | 168 | 1.1869 | 0.7333 |
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+ | 0.0002 | 29.0 | 174 | 1.1546 | 0.7333 |
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+ | 0.0002 | 30.0 | 180 | 1.1302 | 0.7556 |
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+ | 0.0002 | 31.0 | 186 | 1.1214 | 0.7556 |
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+ | 0.0003 | 32.0 | 192 | 1.1205 | 0.7556 |
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+ | 0.0003 | 33.0 | 198 | 1.1222 | 0.7556 |
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+ | 0.0018 | 34.0 | 204 | 1.1316 | 0.7556 |
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+ | 0.0004 | 35.0 | 210 | 1.1630 | 0.7556 |
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+ | 0.0004 | 36.0 | 216 | 1.1838 | 0.7333 |
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+ | 0.0002 | 37.0 | 222 | 1.1946 | 0.7333 |
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+ | 0.0002 | 38.0 | 228 | 1.1949 | 0.7333 |
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+ | 0.0004 | 39.0 | 234 | 1.1930 | 0.7333 |
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+ | 0.0002 | 40.0 | 240 | 1.1932 | 0.7333 |
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+ | 0.0002 | 41.0 | 246 | 1.1940 | 0.7333 |
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+ | 0.0002 | 42.0 | 252 | 1.1942 | 0.7333 |
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+ | 0.0002 | 43.0 | 258 | 1.1942 | 0.7333 |
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+ | 0.0002 | 44.0 | 264 | 1.1942 | 0.7333 |
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+ | 0.0002 | 45.0 | 270 | 1.1942 | 0.7333 |
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+ | 0.0002 | 46.0 | 276 | 1.1942 | 0.7333 |
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+ | 0.0002 | 47.0 | 282 | 1.1942 | 0.7333 |
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+ | 0.0002 | 48.0 | 288 | 1.1942 | 0.7333 |
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+ | 0.0003 | 49.0 | 294 | 1.1942 | 0.7333 |
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+ | 0.0001 | 50.0 | 300 | 1.1942 | 0.7333 |
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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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