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smids_1x_deit_small_adamax_0001_fold1

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  1. README.md +125 -0
  2. model.safetensors +1 -1
  3. runs/Dec04_13-24-02_e7698b4f2488/events.out.tfevents.1701696242.e7698b4f2488.1907.26 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/deit-small-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: smids_1x_deit_small_adamax_0001_fold1
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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: 0.8981636060100167
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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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+ # smids_1x_deit_small_adamax_0001_fold1
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+
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+ This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6387
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+ - Accuracy: 0.8982
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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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+ | 0.4614 | 1.0 | 76 | 0.3156 | 0.8831 |
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+ | 0.2855 | 2.0 | 152 | 0.2885 | 0.8915 |
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+ | 0.1652 | 3.0 | 228 | 0.3412 | 0.8781 |
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+ | 0.0912 | 4.0 | 304 | 0.3542 | 0.8965 |
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+ | 0.1011 | 5.0 | 380 | 0.5346 | 0.8798 |
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+ | 0.038 | 6.0 | 456 | 0.4288 | 0.8982 |
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+ | 0.0075 | 7.0 | 532 | 0.5068 | 0.8998 |
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+ | 0.0075 | 8.0 | 608 | 0.5044 | 0.9098 |
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+ | 0.0143 | 9.0 | 684 | 0.4561 | 0.9032 |
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+ | 0.0021 | 10.0 | 760 | 0.5259 | 0.8982 |
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+ | 0.0314 | 11.0 | 836 | 0.5405 | 0.8932 |
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+ | 0.0002 | 12.0 | 912 | 0.5496 | 0.8965 |
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+ | 0.0052 | 13.0 | 988 | 0.5683 | 0.9098 |
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+ | 0.0001 | 14.0 | 1064 | 0.5233 | 0.9015 |
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+ | 0.0093 | 15.0 | 1140 | 0.5621 | 0.9032 |
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+ | 0.0001 | 16.0 | 1216 | 0.5647 | 0.9048 |
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+ | 0.0001 | 17.0 | 1292 | 0.5914 | 0.8965 |
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+ | 0.0072 | 18.0 | 1368 | 0.5464 | 0.9032 |
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+ | 0.0001 | 19.0 | 1444 | 0.5470 | 0.8982 |
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+ | 0.0035 | 20.0 | 1520 | 0.6054 | 0.8965 |
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+ | 0.004 | 21.0 | 1596 | 0.5757 | 0.9015 |
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+ | 0.0036 | 22.0 | 1672 | 0.5664 | 0.8982 |
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+ | 0.0001 | 23.0 | 1748 | 0.6187 | 0.8948 |
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+ | 0.0 | 24.0 | 1824 | 0.5774 | 0.9048 |
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+ | 0.0072 | 25.0 | 1900 | 0.5831 | 0.8915 |
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+ | 0.0032 | 26.0 | 1976 | 0.5874 | 0.9015 |
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+ | 0.0042 | 27.0 | 2052 | 0.5879 | 0.8998 |
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+ | 0.0028 | 28.0 | 2128 | 0.6082 | 0.8965 |
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+ | 0.0055 | 29.0 | 2204 | 0.6022 | 0.8965 |
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+ | 0.0 | 30.0 | 2280 | 0.5996 | 0.8965 |
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+ | 0.0046 | 31.0 | 2356 | 0.6176 | 0.8948 |
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+ | 0.0082 | 32.0 | 2432 | 0.5942 | 0.8965 |
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+ | 0.0 | 33.0 | 2508 | 0.6226 | 0.8948 |
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+ | 0.0 | 34.0 | 2584 | 0.6117 | 0.8982 |
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+ | 0.0101 | 35.0 | 2660 | 0.6208 | 0.8932 |
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+ | 0.0032 | 36.0 | 2736 | 0.6175 | 0.8948 |
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+ | 0.006 | 37.0 | 2812 | 0.6334 | 0.8932 |
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+ | 0.0 | 38.0 | 2888 | 0.6155 | 0.8982 |
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+ | 0.0031 | 39.0 | 2964 | 0.6236 | 0.8982 |
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+ | 0.0 | 40.0 | 3040 | 0.6141 | 0.8998 |
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+ | 0.0026 | 41.0 | 3116 | 0.6351 | 0.8948 |
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+ | 0.0 | 42.0 | 3192 | 0.6313 | 0.8965 |
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+ | 0.0047 | 43.0 | 3268 | 0.6395 | 0.8932 |
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+ | 0.0 | 44.0 | 3344 | 0.6247 | 0.8998 |
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+ | 0.0 | 45.0 | 3420 | 0.6367 | 0.8982 |
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+ | 0.0 | 46.0 | 3496 | 0.6347 | 0.8982 |
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+ | 0.0 | 47.0 | 3572 | 0.6365 | 0.8982 |
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+ | 0.0024 | 48.0 | 3648 | 0.6411 | 0.8982 |
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+ | 0.0 | 49.0 | 3724 | 0.6383 | 0.8982 |
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+ | 0.0 | 50.0 | 3800 | 0.6387 | 0.8982 |
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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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