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rshrott
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ryan03312024_lr_2e-5_wd_001_v2

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  1. README.md +115 -0
  2. model.safetensors +1 -1
  3. runs/Mar31_17-12-55_ryanserver/events.out.tfevents.1711919583.ryanserver.31714.0 +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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+ model-index:
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+ - name: ryan03312024_lr_2e-5_wd_001_v2
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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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+ # ryan03312024_lr_2e-5_wd_001_v2
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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: 0.1975
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+ - Ordinal Mae: 0.3813
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+ - Ordinal Accuracy: 0.7082
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+ - Na Accuracy: 0.8333
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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: 2e-05
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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: 2.0
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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 | Ordinal Mae | Ordinal Accuracy | Na Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------:|:----------------:|:-----------:|
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+ | 0.4426 | 0.04 | 100 | 0.3707 | 0.8707 | 0.3409 | 0.8076 |
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+ | 0.3133 | 0.07 | 200 | 0.3203 | 0.8545 | 0.4300 | 0.7749 |
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+ | 0.3349 | 0.11 | 300 | 0.2997 | 0.8339 | 0.4593 | 0.8419 |
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+ | 0.3173 | 0.14 | 400 | 0.2870 | 0.7993 | 0.4819 | 0.8660 |
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+ | 0.2946 | 0.18 | 500 | 0.2856 | 0.7690 | 0.5112 | 0.8935 |
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+ | 0.3002 | 0.22 | 600 | 0.2724 | 0.7233 | 0.5345 | 0.9210 |
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+ | 0.2817 | 0.25 | 700 | 0.2657 | 0.6928 | 0.5566 | 0.8625 |
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+ | 0.2939 | 0.29 | 800 | 0.2596 | 0.6425 | 0.5862 | 0.7921 |
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+ | 0.2525 | 0.32 | 900 | 0.2459 | 0.6053 | 0.6047 | 0.8265 |
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+ | 0.2163 | 0.36 | 1000 | 0.2400 | 0.5777 | 0.6245 | 0.8110 |
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+ | 0.2181 | 0.4 | 1100 | 0.2339 | 0.5430 | 0.6024 | 0.8763 |
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+ | 0.1949 | 0.43 | 1200 | 0.2331 | 0.5329 | 0.6286 | 0.7955 |
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+ | 0.214 | 0.47 | 1300 | 0.2424 | 0.5244 | 0.6183 | 0.7629 |
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+ | 0.27 | 0.5 | 1400 | 0.2298 | 0.4995 | 0.6368 | 0.7869 |
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+ | 0.2117 | 0.54 | 1500 | 0.2301 | 0.4950 | 0.6473 | 0.7784 |
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+ | 0.2038 | 0.58 | 1600 | 0.2156 | 0.4899 | 0.6550 | 0.8368 |
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+ | 0.1974 | 0.61 | 1700 | 0.2212 | 0.4639 | 0.6347 | 0.8282 |
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+ | 0.1916 | 0.65 | 1800 | 0.2151 | 0.4790 | 0.6440 | 0.8797 |
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+ | 0.1921 | 0.69 | 1900 | 0.2050 | 0.4614 | 0.6609 | 0.8729 |
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+ | 0.1936 | 0.72 | 2000 | 0.2061 | 0.4566 | 0.6496 | 0.8574 |
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+ | 0.1939 | 0.76 | 2100 | 0.2294 | 0.4657 | 0.6363 | 0.9089 |
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+ | 0.257 | 0.79 | 2200 | 0.2054 | 0.4567 | 0.6527 | 0.8608 |
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+ | 0.2236 | 0.83 | 2300 | 0.2044 | 0.4542 | 0.6640 | 0.8763 |
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+ | 0.1925 | 0.87 | 2400 | 0.2085 | 0.4463 | 0.6887 | 0.8076 |
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+ | 0.1657 | 0.9 | 2500 | 0.2034 | 0.4392 | 0.6769 | 0.8522 |
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+ | 0.1723 | 0.94 | 2600 | 0.1957 | 0.4257 | 0.6756 | 0.8385 |
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+ | 0.2279 | 0.97 | 2700 | 0.1946 | 0.4287 | 0.6740 | 0.8643 |
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+ | 0.1421 | 1.01 | 2800 | 0.1914 | 0.4198 | 0.6843 | 0.8505 |
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+ | 0.1116 | 1.05 | 2900 | 0.2019 | 0.4214 | 0.6704 | 0.8230 |
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+ | 0.1194 | 1.08 | 3000 | 0.1954 | 0.4178 | 0.6807 | 0.8368 |
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+ | 0.1312 | 1.12 | 3100 | 0.1930 | 0.4166 | 0.6874 | 0.8591 |
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+ | 0.1836 | 1.15 | 3200 | 0.1989 | 0.4107 | 0.6794 | 0.8643 |
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+ | 0.1282 | 1.19 | 3300 | 0.1951 | 0.4127 | 0.6971 | 0.8540 |
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+ | 0.1406 | 1.23 | 3400 | 0.1959 | 0.4036 | 0.6974 | 0.8505 |
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+ | 0.0929 | 1.26 | 3500 | 0.1969 | 0.4020 | 0.6977 | 0.8454 |
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+ | 0.1135 | 1.3 | 3600 | 0.1957 | 0.4026 | 0.6982 | 0.8316 |
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+ | 0.1345 | 1.33 | 3700 | 0.1987 | 0.4107 | 0.6833 | 0.8814 |
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+ | 0.1198 | 1.37 | 3800 | 0.1969 | 0.3988 | 0.6992 | 0.8522 |
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+ | 0.1281 | 1.41 | 3900 | 0.1977 | 0.4066 | 0.6966 | 0.8402 |
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+ | 0.1153 | 1.44 | 4000 | 0.2014 | 0.4091 | 0.6936 | 0.8436 |
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+ | 0.1485 | 1.48 | 4100 | 0.1965 | 0.3989 | 0.7038 | 0.8385 |
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+ | 0.1292 | 1.51 | 4200 | 0.1969 | 0.3978 | 0.7031 | 0.8471 |
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+ | 0.1233 | 1.55 | 4300 | 0.1989 | 0.3993 | 0.6951 | 0.8660 |
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+ | 0.1128 | 1.59 | 4400 | 0.1998 | 0.3920 | 0.6971 | 0.8522 |
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+ | 0.0964 | 1.62 | 4500 | 0.2005 | 0.3926 | 0.6982 | 0.8625 |
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+ | 0.1184 | 1.66 | 4600 | 0.2008 | 0.3860 | 0.6969 | 0.8711 |
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+ | 0.108 | 1.69 | 4700 | 0.1994 | 0.3907 | 0.7020 | 0.8574 |
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+ | 0.129 | 1.73 | 4800 | 0.1985 | 0.3896 | 0.7033 | 0.8591 |
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+ | 0.1396 | 1.77 | 4900 | 0.1998 | 0.3834 | 0.6984 | 0.8574 |
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+ | 0.1323 | 1.8 | 5000 | 0.1986 | 0.3844 | 0.7051 | 0.8454 |
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+ | 0.1079 | 1.84 | 5100 | 0.1974 | 0.3833 | 0.7054 | 0.8402 |
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+ | 0.0802 | 1.88 | 5200 | 0.1965 | 0.3822 | 0.7074 | 0.8488 |
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+ | 0.1391 | 1.91 | 5300 | 0.1975 | 0.3809 | 0.7051 | 0.8454 |
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+ | 0.1183 | 1.95 | 5400 | 0.1973 | 0.3827 | 0.7087 | 0.8351 |
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+ | 0.1368 | 1.98 | 5500 | 0.1975 | 0.3813 | 0.7082 | 0.8333 |
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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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