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hushem_1x_beit_base_sgd_00001_fold4

Image Classification
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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: 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_sgd_00001_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: 0.2857142857142857
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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_sgd_00001_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: 1.4953
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+ - Accuracy: 0.2857
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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: 1e-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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+ - 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 | 1.5027 | 0.3095 |
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+ | 1.6067 | 2.0 | 12 | 1.5024 | 0.2857 |
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+ | 1.6067 | 3.0 | 18 | 1.5020 | 0.2857 |
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+ | 1.5707 | 4.0 | 24 | 1.5016 | 0.2857 |
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+ | 1.5808 | 5.0 | 30 | 1.5013 | 0.2857 |
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+ | 1.5808 | 6.0 | 36 | 1.5009 | 0.2857 |
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+ | 1.5893 | 7.0 | 42 | 1.5006 | 0.2857 |
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+ | 1.5893 | 8.0 | 48 | 1.5003 | 0.2857 |
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+ | 1.5944 | 9.0 | 54 | 1.5000 | 0.2857 |
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+ | 1.5821 | 10.0 | 60 | 1.4997 | 0.2857 |
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+ | 1.5821 | 11.0 | 66 | 1.4994 | 0.2857 |
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+ | 1.5703 | 12.0 | 72 | 1.4991 | 0.2857 |
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+ | 1.5703 | 13.0 | 78 | 1.4988 | 0.2857 |
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+ | 1.5654 | 14.0 | 84 | 1.4986 | 0.2857 |
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+ | 1.5848 | 15.0 | 90 | 1.4983 | 0.2857 |
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+ | 1.5848 | 16.0 | 96 | 1.4981 | 0.2857 |
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+ | 1.606 | 17.0 | 102 | 1.4978 | 0.2857 |
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+ | 1.606 | 18.0 | 108 | 1.4976 | 0.2857 |
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+ | 1.6306 | 19.0 | 114 | 1.4974 | 0.2857 |
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+ | 1.5966 | 20.0 | 120 | 1.4972 | 0.2857 |
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+ | 1.5966 | 21.0 | 126 | 1.4970 | 0.2857 |
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+ | 1.5946 | 22.0 | 132 | 1.4969 | 0.2857 |
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+ | 1.5946 | 23.0 | 138 | 1.4967 | 0.2857 |
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+ | 1.5656 | 24.0 | 144 | 1.4966 | 0.2857 |
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+ | 1.5572 | 25.0 | 150 | 1.4964 | 0.2857 |
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+ | 1.5572 | 26.0 | 156 | 1.4963 | 0.2857 |
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+ | 1.5856 | 27.0 | 162 | 1.4961 | 0.2857 |
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+ | 1.5856 | 28.0 | 168 | 1.4960 | 0.2857 |
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+ | 1.612 | 29.0 | 174 | 1.4959 | 0.2857 |
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+ | 1.581 | 30.0 | 180 | 1.4958 | 0.2857 |
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+ | 1.581 | 31.0 | 186 | 1.4957 | 0.2857 |
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+ | 1.566 | 32.0 | 192 | 1.4956 | 0.2857 |
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+ | 1.566 | 33.0 | 198 | 1.4956 | 0.2857 |
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+ | 1.5925 | 34.0 | 204 | 1.4955 | 0.2857 |
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+ | 1.5991 | 35.0 | 210 | 1.4954 | 0.2857 |
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+ | 1.5991 | 36.0 | 216 | 1.4954 | 0.2857 |
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+ | 1.5811 | 37.0 | 222 | 1.4954 | 0.2857 |
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+ | 1.5811 | 38.0 | 228 | 1.4953 | 0.2857 |
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+ | 1.5945 | 39.0 | 234 | 1.4953 | 0.2857 |
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+ | 1.5831 | 40.0 | 240 | 1.4953 | 0.2857 |
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+ | 1.5831 | 41.0 | 246 | 1.4953 | 0.2857 |
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+ | 1.5802 | 42.0 | 252 | 1.4953 | 0.2857 |
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+ | 1.5802 | 43.0 | 258 | 1.4953 | 0.2857 |
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+ | 1.6388 | 44.0 | 264 | 1.4953 | 0.2857 |
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+ | 1.5513 | 45.0 | 270 | 1.4953 | 0.2857 |
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+ | 1.5513 | 46.0 | 276 | 1.4953 | 0.2857 |
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+ | 1.5675 | 47.0 | 282 | 1.4953 | 0.2857 |
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+ | 1.5675 | 48.0 | 288 | 1.4953 | 0.2857 |
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+ | 1.6043 | 49.0 | 294 | 1.4953 | 0.2857 |
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+ | 1.6042 | 50.0 | 300 | 1.4953 | 0.2857 |
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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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