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hushem_5x_deit_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: facebook/deit-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_deit_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: 0.9047619047619048
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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_deit_base_rms_0001_fold4
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+
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+ This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-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: 0.5888
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+ - Accuracy: 0.9048
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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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+ | 1.6398 | 1.0 | 28 | 1.4620 | 0.2381 |
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+ | 1.4471 | 2.0 | 56 | 1.4867 | 0.2619 |
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+ | 1.4043 | 3.0 | 84 | 1.4639 | 0.2381 |
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+ | 1.6225 | 4.0 | 112 | 1.1986 | 0.4524 |
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+ | 1.0459 | 5.0 | 140 | 1.1310 | 0.4762 |
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+ | 0.7275 | 6.0 | 168 | 0.7753 | 0.6429 |
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+ | 0.4185 | 7.0 | 196 | 0.5503 | 0.7857 |
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+ | 0.2249 | 8.0 | 224 | 0.5491 | 0.8571 |
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+ | 0.0749 | 9.0 | 252 | 0.2650 | 0.9286 |
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+ | 0.0643 | 10.0 | 280 | 0.5070 | 0.8333 |
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+ | 0.083 | 11.0 | 308 | 0.5183 | 0.8810 |
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+ | 0.0258 | 12.0 | 336 | 0.5166 | 0.8571 |
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+ | 0.0004 | 13.0 | 364 | 0.4395 | 0.9524 |
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+ | 0.03 | 14.0 | 392 | 0.5344 | 0.9048 |
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+ | 0.0374 | 15.0 | 420 | 1.0859 | 0.8095 |
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+ | 0.032 | 16.0 | 448 | 0.4372 | 0.9048 |
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+ | 0.0018 | 17.0 | 476 | 0.4691 | 0.9048 |
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+ | 0.0319 | 18.0 | 504 | 0.5620 | 0.8810 |
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+ | 0.022 | 19.0 | 532 | 0.4782 | 0.9048 |
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+ | 0.0002 | 20.0 | 560 | 0.4687 | 0.9048 |
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+ | 0.0001 | 21.0 | 588 | 0.4749 | 0.9048 |
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+ | 0.0001 | 22.0 | 616 | 0.4799 | 0.9048 |
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+ | 0.0001 | 23.0 | 644 | 0.4865 | 0.9048 |
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+ | 0.0001 | 24.0 | 672 | 0.4924 | 0.9048 |
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+ | 0.0001 | 25.0 | 700 | 0.4977 | 0.9048 |
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+ | 0.0001 | 26.0 | 728 | 0.5030 | 0.9048 |
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+ | 0.0 | 27.0 | 756 | 0.5085 | 0.9048 |
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+ | 0.0 | 28.0 | 784 | 0.5132 | 0.9048 |
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+ | 0.0 | 29.0 | 812 | 0.5184 | 0.9048 |
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+ | 0.0 | 30.0 | 840 | 0.5233 | 0.9048 |
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+ | 0.0 | 31.0 | 868 | 0.5283 | 0.9048 |
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+ | 0.0 | 32.0 | 896 | 0.5333 | 0.9048 |
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+ | 0.0 | 33.0 | 924 | 0.5383 | 0.9048 |
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+ | 0.0 | 34.0 | 952 | 0.5430 | 0.9048 |
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+ | 0.0 | 35.0 | 980 | 0.5476 | 0.9048 |
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+ | 0.0 | 36.0 | 1008 | 0.5522 | 0.9048 |
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+ | 0.0 | 37.0 | 1036 | 0.5569 | 0.9048 |
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+ | 0.0 | 38.0 | 1064 | 0.5613 | 0.9048 |
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+ | 0.0 | 39.0 | 1092 | 0.5655 | 0.9048 |
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+ | 0.0 | 40.0 | 1120 | 0.5694 | 0.9048 |
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+ | 0.0 | 41.0 | 1148 | 0.5725 | 0.9048 |
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+ | 0.0 | 42.0 | 1176 | 0.5761 | 0.9048 |
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+ | 0.0 | 43.0 | 1204 | 0.5794 | 0.9048 |
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+ | 0.0 | 44.0 | 1232 | 0.5824 | 0.9048 |
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+ | 0.0 | 45.0 | 1260 | 0.5848 | 0.9048 |
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+ | 0.0 | 46.0 | 1288 | 0.5868 | 0.9048 |
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+ | 0.0 | 47.0 | 1316 | 0.5882 | 0.9048 |
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+ | 0.0 | 48.0 | 1344 | 0.5888 | 0.9048 |
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+ | 0.0 | 49.0 | 1372 | 0.5888 | 0.9048 |
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+ | 0.0 | 50.0 | 1400 | 0.5888 | 0.9048 |
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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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