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hushem_40x_beit_large_adamax_0001_fold5

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Eval Results
Inference Endpoints
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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-large-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_40x_beit_large_adamax_0001_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: 0.8536585365853658
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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_40x_beit_large_adamax_0001_fold5
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+
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+ This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9014
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+ - Accuracy: 0.8537
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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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+ | 0.0137 | 1.0 | 220 | 0.6252 | 0.8049 |
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+ | 0.0039 | 2.0 | 440 | 0.3651 | 0.9268 |
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+ | 0.0 | 3.0 | 660 | 0.2079 | 0.9512 |
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+ | 0.0 | 4.0 | 880 | 0.2782 | 0.8780 |
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+ | 0.0015 | 5.0 | 1100 | 0.3966 | 0.8780 |
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+ | 0.0006 | 6.0 | 1320 | 0.9179 | 0.8049 |
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+ | 0.0 | 7.0 | 1540 | 0.6543 | 0.8780 |
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+ | 0.0 | 8.0 | 1760 | 0.6721 | 0.8537 |
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+ | 0.0 | 9.0 | 1980 | 0.6667 | 0.8537 |
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+ | 0.0 | 10.0 | 2200 | 0.6892 | 0.8293 |
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+ | 0.0 | 11.0 | 2420 | 0.6788 | 0.8293 |
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+ | 0.0187 | 12.0 | 2640 | 0.6872 | 0.8537 |
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+ | 0.0 | 13.0 | 2860 | 1.1812 | 0.8049 |
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+ | 0.0 | 14.0 | 3080 | 0.6787 | 0.8537 |
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+ | 0.0 | 15.0 | 3300 | 0.7294 | 0.8293 |
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+ | 0.0 | 16.0 | 3520 | 1.0136 | 0.8293 |
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+ | 0.0 | 17.0 | 3740 | 0.9479 | 0.8293 |
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+ | 0.0 | 18.0 | 3960 | 0.9308 | 0.8293 |
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+ | 0.0 | 19.0 | 4180 | 0.8944 | 0.8293 |
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+ | 0.0 | 20.0 | 4400 | 0.8979 | 0.8293 |
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+ | 0.0 | 21.0 | 4620 | 0.8942 | 0.8293 |
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+ | 0.0 | 22.0 | 4840 | 0.9123 | 0.8293 |
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+ | 0.0 | 23.0 | 5060 | 0.7263 | 0.8537 |
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+ | 0.0 | 24.0 | 5280 | 0.7426 | 0.8537 |
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+ | 0.0 | 25.0 | 5500 | 0.7599 | 0.8293 |
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+ | 0.0 | 26.0 | 5720 | 0.7693 | 0.8293 |
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+ | 0.0 | 27.0 | 5940 | 0.8044 | 0.8293 |
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+ | 0.0 | 28.0 | 6160 | 0.8028 | 0.8293 |
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+ | 0.0 | 29.0 | 6380 | 0.6542 | 0.8293 |
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+ | 0.0 | 30.0 | 6600 | 0.6934 | 0.8293 |
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+ | 0.0 | 31.0 | 6820 | 0.6814 | 0.8293 |
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+ | 0.0 | 32.0 | 7040 | 0.6666 | 0.8537 |
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+ | 0.0 | 33.0 | 7260 | 0.7695 | 0.8293 |
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+ | 0.0 | 34.0 | 7480 | 1.0033 | 0.8293 |
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+ | 0.0 | 35.0 | 7700 | 0.9558 | 0.8537 |
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+ | 0.0 | 36.0 | 7920 | 0.8444 | 0.8537 |
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+ | 0.0 | 37.0 | 8140 | 0.9196 | 0.8537 |
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+ | 0.0 | 38.0 | 8360 | 0.8784 | 0.8537 |
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+ | 0.0 | 39.0 | 8580 | 0.8306 | 0.8780 |
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+ | 0.0 | 40.0 | 8800 | 0.9373 | 0.8537 |
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+ | 0.0 | 41.0 | 9020 | 0.9235 | 0.8537 |
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+ | 0.0 | 42.0 | 9240 | 0.9473 | 0.8537 |
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+ | 0.0 | 43.0 | 9460 | 0.9424 | 0.8537 |
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+ | 0.0 | 44.0 | 9680 | 0.9102 | 0.8537 |
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+ | 0.0 | 45.0 | 9900 | 0.9576 | 0.8537 |
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+ | 0.0 | 46.0 | 10120 | 0.9639 | 0.8537 |
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+ | 0.0 | 47.0 | 10340 | 0.9689 | 0.8537 |
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+ | 0.0 | 48.0 | 10560 | 0.8859 | 0.8537 |
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+ | 0.0 | 49.0 | 10780 | 0.9011 | 0.8537 |
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+ | 0.0 | 50.0 | 11000 | 0.9014 | 0.8537 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.1
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.2
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