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beit-base-patch16-224-OT

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  1. README.md +114 -0
  2. all_results.json +12 -0
  3. config.json +77 -0
  4. eval_results.json +8 -0
  5. model.safetensors +3 -0
  6. preprocessor_config.json +28 -0
  7. runs/Oct11_08-45-26_DESKTOP-SKBE9FB/events.out.tfevents.1728657927.DESKTOP-SKBE9FB.18060.0 +3 -0
  8. runs/Oct11_08-45-26_DESKTOP-SKBE9FB/events.out.tfevents.1728658641.DESKTOP-SKBE9FB.18060.1 +3 -0
  9. train_results.json +7 -0
  10. trainer_state.json +483 -0
  11. training_args.bin +3 -0
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: beit-base-patch16-224-OT
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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: train
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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: 0.9516129032258065
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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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+ # beit-base-patch16-224-OT
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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: 0.3612
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+ - Accuracy: 0.9516
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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: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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: 40
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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 | 0.91 | 5 | 1.3762 | 0.4677 |
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+ | 1.3741 | 2.0 | 11 | 1.3227 | 0.4516 |
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+ | 1.3741 | 2.91 | 16 | 1.2451 | 0.4516 |
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+ | 1.2883 | 4.0 | 22 | 1.1303 | 0.5484 |
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+ | 1.2883 | 4.91 | 27 | 1.0044 | 0.7419 |
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+ | 1.1053 | 6.0 | 33 | 0.8687 | 0.7581 |
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+ | 1.1053 | 6.91 | 38 | 0.7694 | 0.8387 |
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+ | 0.917 | 8.0 | 44 | 0.6563 | 0.8065 |
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+ | 0.917 | 8.91 | 49 | 0.5870 | 0.8710 |
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+ | 0.7172 | 10.0 | 55 | 0.5842 | 0.7903 |
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+ | 0.5924 | 10.91 | 60 | 0.4820 | 0.8710 |
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+ | 0.5924 | 12.0 | 66 | 0.5346 | 0.8065 |
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+ | 0.5272 | 12.91 | 71 | 0.3612 | 0.9516 |
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+ | 0.5272 | 14.0 | 77 | 0.3838 | 0.9194 |
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+ | 0.4901 | 14.91 | 82 | 0.4009 | 0.9032 |
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+ | 0.4901 | 16.0 | 88 | 0.3721 | 0.8548 |
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+ | 0.47 | 16.91 | 93 | 0.4358 | 0.8710 |
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+ | 0.47 | 18.0 | 99 | 0.3734 | 0.8710 |
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+ | 0.4714 | 18.91 | 104 | 0.4338 | 0.8548 |
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+ | 0.3805 | 20.0 | 110 | 0.4152 | 0.8548 |
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+ | 0.3805 | 20.91 | 115 | 0.3676 | 0.9194 |
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+ | 0.388 | 22.0 | 121 | 0.3727 | 0.8871 |
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+ | 0.388 | 22.91 | 126 | 0.3751 | 0.8871 |
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+ | 0.3868 | 24.0 | 132 | 0.4173 | 0.8548 |
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+ | 0.3868 | 24.91 | 137 | 0.3992 | 0.8710 |
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+ | 0.3399 | 26.0 | 143 | 0.3749 | 0.8871 |
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+ | 0.3399 | 26.91 | 148 | 0.4060 | 0.8548 |
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+ | 0.3271 | 28.0 | 154 | 0.3926 | 0.9032 |
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+ | 0.3271 | 28.91 | 159 | 0.3731 | 0.8710 |
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+ | 0.3299 | 30.0 | 165 | 0.3836 | 0.8710 |
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+ | 0.3114 | 30.91 | 170 | 0.4074 | 0.8871 |
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+ | 0.3114 | 32.0 | 176 | 0.4274 | 0.8548 |
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+ | 0.2738 | 32.91 | 181 | 0.3812 | 0.8710 |
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+ | 0.2738 | 34.0 | 187 | 0.3795 | 0.8710 |
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+ | 0.2906 | 34.91 | 192 | 0.3813 | 0.8710 |
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+ | 0.2906 | 36.0 | 198 | 0.3886 | 0.8710 |
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+ | 0.2623 | 36.36 | 200 | 0.3893 | 0.8710 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.1.2+cu118
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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