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smids_1x_beit_base_sgd_001_fold5

Image Classification
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  1. README.md +125 -0
  2. model.safetensors +1 -1
  3. runs/Dec13_01-15-40_e9f1c7e988d1/events.out.tfevents.1702430142.e9f1c7e988d1.694.14 +2 -2
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_sgd_001_fold5
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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: 0.84
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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_sgd_001_fold5
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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: 0.3960
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+ - Accuracy: 0.84
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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: 0.001
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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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+ | 1.0915 | 1.0 | 75 | 1.0482 | 0.46 |
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+ | 0.8641 | 2.0 | 150 | 0.8453 | 0.6317 |
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+ | 0.8005 | 3.0 | 225 | 0.7497 | 0.67 |
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+ | 0.7495 | 4.0 | 300 | 0.6945 | 0.7067 |
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+ | 0.7055 | 5.0 | 375 | 0.6553 | 0.7467 |
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+ | 0.7202 | 6.0 | 450 | 0.6245 | 0.75 |
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+ | 0.6588 | 7.0 | 525 | 0.6019 | 0.7583 |
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+ | 0.6049 | 8.0 | 600 | 0.5871 | 0.76 |
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+ | 0.6317 | 9.0 | 675 | 0.5605 | 0.7833 |
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+ | 0.5775 | 10.0 | 750 | 0.5437 | 0.785 |
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+ | 0.5951 | 11.0 | 825 | 0.5303 | 0.7933 |
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+ | 0.5297 | 12.0 | 900 | 0.5191 | 0.79 |
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+ | 0.5261 | 13.0 | 975 | 0.5051 | 0.7933 |
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+ | 0.5545 | 14.0 | 1050 | 0.4974 | 0.7983 |
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+ | 0.4597 | 15.0 | 1125 | 0.4949 | 0.805 |
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+ | 0.4273 | 16.0 | 1200 | 0.4837 | 0.8017 |
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+ | 0.4781 | 17.0 | 1275 | 0.4758 | 0.8067 |
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+ | 0.4613 | 18.0 | 1350 | 0.4662 | 0.815 |
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+ | 0.4966 | 19.0 | 1425 | 0.4609 | 0.8133 |
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+ | 0.5166 | 20.0 | 1500 | 0.4558 | 0.81 |
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+ | 0.4529 | 21.0 | 1575 | 0.4548 | 0.8167 |
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+ | 0.4333 | 22.0 | 1650 | 0.4478 | 0.8233 |
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+ | 0.4673 | 23.0 | 1725 | 0.4422 | 0.8183 |
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+ | 0.402 | 24.0 | 1800 | 0.4383 | 0.8283 |
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+ | 0.4207 | 25.0 | 1875 | 0.4375 | 0.8283 |
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+ | 0.4343 | 26.0 | 1950 | 0.4301 | 0.8267 |
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+ | 0.4249 | 27.0 | 2025 | 0.4265 | 0.8283 |
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+ | 0.4127 | 28.0 | 2100 | 0.4255 | 0.8267 |
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+ | 0.4286 | 29.0 | 2175 | 0.4192 | 0.8367 |
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+ | 0.3988 | 30.0 | 2250 | 0.4174 | 0.8367 |
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+ | 0.3838 | 31.0 | 2325 | 0.4145 | 0.8383 |
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+ | 0.3896 | 32.0 | 2400 | 0.4157 | 0.835 |
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+ | 0.4348 | 33.0 | 2475 | 0.4153 | 0.825 |
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+ | 0.41 | 34.0 | 2550 | 0.4109 | 0.8367 |
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+ | 0.3989 | 35.0 | 2625 | 0.4069 | 0.84 |
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+ | 0.3824 | 36.0 | 2700 | 0.4101 | 0.8367 |
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+ | 0.3688 | 37.0 | 2775 | 0.4062 | 0.8367 |
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+ | 0.4091 | 38.0 | 2850 | 0.4063 | 0.8367 |
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+ | 0.3672 | 39.0 | 2925 | 0.4039 | 0.835 |
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+ | 0.4219 | 40.0 | 3000 | 0.4009 | 0.8383 |
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+ | 0.4047 | 41.0 | 3075 | 0.4024 | 0.8383 |
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+ | 0.4168 | 42.0 | 3150 | 0.3989 | 0.835 |
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+ | 0.4198 | 43.0 | 3225 | 0.3971 | 0.8417 |
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+ | 0.4236 | 44.0 | 3300 | 0.3971 | 0.84 |
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+ | 0.3959 | 45.0 | 3375 | 0.3975 | 0.8433 |
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+ | 0.3933 | 46.0 | 3450 | 0.3984 | 0.8433 |
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+ | 0.3443 | 47.0 | 3525 | 0.3963 | 0.8417 |
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+ | 0.3626 | 48.0 | 3600 | 0.3958 | 0.8417 |
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+ | 0.38 | 49.0 | 3675 | 0.3960 | 0.8417 |
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+ | 0.3733 | 50.0 | 3750 | 0.3960 | 0.84 |
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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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