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ryan_model2

Image Classification
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vit
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  1. README.md +146 -0
  2. model.safetensors +1 -1
  3. runs/Mar26_00-50-38_ryanserver/events.out.tfevents.1711428648.ryanserver +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: ryan_model2
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+ results: []
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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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+ # ryan_model2
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.3649
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+ - Accuracy: 0.7100
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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: 0.0002
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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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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+ - num_epochs: 4
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+ - mixed_precision_training: Native AMP
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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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+ | 1.1685 | 0.05 | 100 | 1.1497 | 0.5270 |
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+ | 0.93 | 0.09 | 200 | 1.0087 | 0.5966 |
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+ | 0.8567 | 0.14 | 300 | 1.1028 | 0.5607 |
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+ | 0.9407 | 0.19 | 400 | 0.9464 | 0.6150 |
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+ | 0.9323 | 0.23 | 500 | 0.9542 | 0.6165 |
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+ | 0.8375 | 0.28 | 600 | 0.8750 | 0.6431 |
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+ | 1.0136 | 0.32 | 700 | 0.9315 | 0.6077 |
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+ | 1.0557 | 0.37 | 800 | 0.9124 | 0.6268 |
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+ | 0.7398 | 0.42 | 900 | 0.8843 | 0.6384 |
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+ | 0.7579 | 0.46 | 1000 | 0.8965 | 0.6338 |
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+ | 0.8872 | 0.51 | 1100 | 0.8624 | 0.6444 |
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+ | 0.889 | 0.56 | 1200 | 0.9395 | 0.6213 |
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+ | 0.8863 | 0.6 | 1300 | 0.8294 | 0.6645 |
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+ | 0.6924 | 0.65 | 1400 | 0.8748 | 0.6431 |
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+ | 0.7978 | 0.7 | 1500 | 0.8624 | 0.6497 |
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+ | 0.764 | 0.74 | 1600 | 0.8861 | 0.6389 |
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+ | 0.7159 | 0.79 | 1700 | 0.8413 | 0.6504 |
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+ | 0.7912 | 0.84 | 1800 | 0.8729 | 0.6376 |
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+ | 0.8232 | 0.88 | 1900 | 0.7743 | 0.6776 |
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+ | 0.7108 | 0.93 | 2000 | 0.8804 | 0.6361 |
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+ | 0.7324 | 0.97 | 2100 | 0.7950 | 0.6743 |
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+ | 0.5353 | 1.02 | 2200 | 0.9441 | 0.6285 |
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+ | 0.5808 | 1.07 | 2300 | 0.8193 | 0.6670 |
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+ | 0.5451 | 1.11 | 2400 | 0.9586 | 0.6258 |
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+ | 0.5201 | 1.16 | 2500 | 0.8172 | 0.6745 |
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+ | 0.5294 | 1.21 | 2600 | 0.8386 | 0.6713 |
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+ | 0.5595 | 1.25 | 2700 | 0.8296 | 0.6622 |
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+ | 0.488 | 1.3 | 2800 | 0.8134 | 0.6758 |
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+ | 0.5577 | 1.35 | 2900 | 0.8476 | 0.6763 |
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+ | 0.4918 | 1.39 | 3000 | 0.8701 | 0.6640 |
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+ | 0.5549 | 1.44 | 3100 | 0.9492 | 0.6371 |
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+ | 0.6421 | 1.48 | 3200 | 0.8248 | 0.6763 |
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+ | 0.5423 | 1.53 | 3300 | 0.7948 | 0.6838 |
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+ | 0.5654 | 1.58 | 3400 | 0.7697 | 0.6836 |
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+ | 0.5051 | 1.62 | 3500 | 0.8189 | 0.6818 |
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+ | 0.4797 | 1.67 | 3600 | 0.7995 | 0.6833 |
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+ | 0.5645 | 1.72 | 3700 | 0.8068 | 0.6796 |
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+ | 0.4865 | 1.76 | 3800 | 0.8162 | 0.6808 |
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+ | 0.502 | 1.81 | 3900 | 0.7947 | 0.6859 |
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+ | 0.5164 | 1.86 | 4000 | 0.8085 | 0.6801 |
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+ | 0.4822 | 1.9 | 4100 | 0.7611 | 0.6954 |
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+ | 0.4777 | 1.95 | 4200 | 0.8203 | 0.6823 |
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+ | 0.5423 | 2.0 | 4300 | 0.7761 | 0.6896 |
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+ | 0.2653 | 2.04 | 4400 | 0.8337 | 0.7004 |
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+ | 0.2646 | 2.09 | 4500 | 0.9206 | 0.6911 |
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+ | 0.2782 | 2.13 | 4600 | 0.9539 | 0.6924 |
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+ | 0.2032 | 2.18 | 4700 | 0.8932 | 0.6999 |
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+ | 0.2837 | 2.23 | 4800 | 0.9431 | 0.6914 |
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+ | 0.3152 | 2.27 | 4900 | 0.9220 | 0.7022 |
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+ | 0.4516 | 2.32 | 5000 | 0.9568 | 0.6904 |
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+ | 0.2151 | 2.37 | 5100 | 0.9406 | 0.7075 |
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+ | 0.2932 | 2.41 | 5200 | 0.9687 | 0.6904 |
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+ | 0.3352 | 2.46 | 5300 | 0.9500 | 0.7024 |
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+ | 0.2447 | 2.51 | 5400 | 0.9382 | 0.6982 |
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+ | 0.371 | 2.55 | 5500 | 0.9664 | 0.6916 |
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+ | 0.1435 | 2.6 | 5600 | 1.0167 | 0.6853 |
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+ | 0.2489 | 2.65 | 5700 | 0.9714 | 0.6941 |
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+ | 0.2744 | 2.69 | 5800 | 1.0301 | 0.6899 |
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+ | 0.2139 | 2.74 | 5900 | 1.0056 | 0.6861 |
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+ | 0.2953 | 2.78 | 6000 | 0.9620 | 0.7014 |
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+ | 0.2672 | 2.83 | 6100 | 0.9992 | 0.6919 |
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+ | 0.2384 | 2.88 | 6200 | 1.0486 | 0.6987 |
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+ | 0.2759 | 2.92 | 6300 | 1.0390 | 0.6896 |
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+ | 0.2098 | 2.97 | 6400 | 1.0927 | 0.6818 |
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+ | 0.0427 | 3.02 | 6500 | 1.0394 | 0.6957 |
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+ | 0.0582 | 3.06 | 6600 | 1.0990 | 0.7057 |
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+ | 0.0494 | 3.11 | 6700 | 1.1617 | 0.6999 |
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+ | 0.1249 | 3.16 | 6800 | 1.2645 | 0.6929 |
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+ | 0.0786 | 3.2 | 6900 | 1.2227 | 0.7002 |
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+ | 0.0728 | 3.25 | 7000 | 1.2736 | 0.6977 |
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+ | 0.1319 | 3.29 | 7100 | 1.3114 | 0.6969 |
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+ | 0.041 | 3.34 | 7200 | 1.3003 | 0.7022 |
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+ | 0.0174 | 3.39 | 7300 | 1.3064 | 0.6997 |
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+ | 0.0911 | 3.43 | 7400 | 1.3231 | 0.7009 |
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+ | 0.0187 | 3.48 | 7500 | 1.3725 | 0.6979 |
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+ | 0.1097 | 3.53 | 7600 | 1.3446 | 0.7034 |
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+ | 0.1588 | 3.57 | 7700 | 1.3276 | 0.7060 |
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+ | 0.0598 | 3.62 | 7800 | 1.3460 | 0.7029 |
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+ | 0.0418 | 3.67 | 7900 | 1.3614 | 0.7027 |
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+ | 0.0522 | 3.71 | 8000 | 1.3581 | 0.7062 |
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+ | 0.0932 | 3.76 | 8100 | 1.3598 | 0.7072 |
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+ | 0.092 | 3.81 | 8200 | 1.3826 | 0.7039 |
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+ | 0.0199 | 3.85 | 8300 | 1.3744 | 0.7057 |
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+ | 0.0251 | 3.9 | 8400 | 1.3652 | 0.7065 |
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+ | 0.1199 | 3.94 | 8500 | 1.3612 | 0.7102 |
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+ | 0.0629 | 3.99 | 8600 | 1.3649 | 0.7100 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.1
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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