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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: hbenitez/AV_classifier_resnet50 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# hbenitez/AV_classifier_resnet50 |
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.8261 |
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- Validation Loss: 2.4425 |
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- Train Accuracy: 0.6 |
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- Epoch: 99 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 8000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train Accuracy | Epoch | |
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|:----------:|:---------------:|:--------------:|:-----:| |
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| 8.2326 | 8.1060 | 0.0 | 0 | |
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| 8.3420 | 7.6394 | 0.05 | 1 | |
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| 7.9643 | 7.5706 | 0.05 | 2 | |
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| 7.9337 | 7.6265 | 0.05 | 3 | |
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| 7.8018 | 7.7736 | 0.05 | 4 | |
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| 7.8009 | 7.7905 | 0.05 | 5 | |
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| 7.6369 | 7.6354 | 0.05 | 6 | |
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| 7.4782 | 7.5608 | 0.05 | 7 | |
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| 7.3655 | 7.6271 | 0.05 | 8 | |
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| 7.2886 | 7.6028 | 0.0 | 9 | |
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| 7.1145 | 7.5211 | 0.0 | 10 | |
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| 7.1232 | 7.2993 | 0.0 | 11 | |
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| 6.8393 | 7.2079 | 0.0 | 12 | |
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| 6.8202 | 7.2143 | 0.0 | 13 | |
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| 6.7180 | 7.1236 | 0.05 | 14 | |
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| 6.7318 | 7.1061 | 0.0 | 15 | |
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| 6.4563 | 6.9758 | 0.05 | 16 | |
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| 6.3765 | 6.9413 | 0.05 | 17 | |
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| 6.1791 | 6.8315 | 0.05 | 18 | |
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| 6.1946 | 6.7703 | 0.05 | 19 | |
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| 5.8448 | 6.7431 | 0.1 | 20 | |
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| 5.8514 | 6.6876 | 0.1 | 21 | |
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| 5.8200 | 6.6353 | 0.05 | 22 | |
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| 5.8323 | 6.5814 | 0.05 | 23 | |
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| 5.5553 | 6.4306 | 0.05 | 24 | |
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| 5.4999 | 6.4455 | 0.05 | 25 | |
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| 5.4370 | 6.3026 | 0.05 | 26 | |
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| 5.2288 | 6.0093 | 0.1 | 27 | |
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| 5.2173 | 6.0593 | 0.05 | 28 | |
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| 5.2280 | 6.0598 | 0.05 | 29 | |
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| 5.0484 | 5.9769 | 0.05 | 30 | |
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| 4.8703 | 5.8336 | 0.05 | 31 | |
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| 4.9881 | 5.7711 | 0.1 | 32 | |
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| 4.5905 | 5.6685 | 0.1 | 33 | |
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| 4.7240 | 5.6156 | 0.15 | 34 | |
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| 4.5095 | 5.4680 | 0.15 | 35 | |
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| 4.2225 | 5.3962 | 0.15 | 36 | |
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| 4.3615 | 5.3290 | 0.2 | 37 | |
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| 4.1862 | 5.3602 | 0.15 | 38 | |
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| 3.9455 | 5.2635 | 0.15 | 39 | |
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| 3.9737 | 5.2337 | 0.15 | 40 | |
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| 4.0922 | 5.1268 | 0.15 | 41 | |
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| 3.6042 | 4.9972 | 0.2 | 42 | |
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| 3.7219 | 4.8787 | 0.15 | 43 | |
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| 3.5563 | 4.9075 | 0.2 | 44 | |
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| 3.5897 | 4.9157 | 0.25 | 45 | |
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| 3.5769 | 4.7936 | 0.25 | 46 | |
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| 3.6225 | 4.8689 | 0.2 | 47 | |
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| 3.4568 | 4.8767 | 0.2 | 48 | |
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| 3.1431 | 4.7520 | 0.25 | 49 | |
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| 3.0607 | 4.5815 | 0.3 | 50 | |
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| 2.8904 | 4.5007 | 0.2 | 51 | |
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| 2.8308 | 4.5054 | 0.25 | 52 | |
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| 2.8136 | 4.2745 | 0.25 | 53 | |
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| 2.6192 | 4.3300 | 0.2 | 54 | |
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| 2.5308 | 4.3180 | 0.2 | 55 | |
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| 2.5192 | 4.2706 | 0.2 | 56 | |
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| 2.5761 | 4.1395 | 0.25 | 57 | |
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| 2.3516 | 3.9031 | 0.3 | 58 | |
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| 2.3231 | 3.8172 | 0.35 | 59 | |
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| 2.2735 | 3.7651 | 0.35 | 60 | |
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| 2.1215 | 3.8034 | 0.35 | 61 | |
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| 2.3229 | 3.8096 | 0.35 | 62 | |
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| 2.2230 | 3.7000 | 0.35 | 63 | |
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| 1.9059 | 3.6666 | 0.25 | 64 | |
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| 2.0289 | 3.6743 | 0.25 | 65 | |
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| 1.9178 | 3.5819 | 0.3 | 66 | |
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| 2.0295 | 3.5087 | 0.35 | 67 | |
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| 1.6499 | 3.4962 | 0.4 | 68 | |
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| 1.6261 | 3.4146 | 0.3 | 69 | |
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| 1.7059 | 3.4097 | 0.35 | 70 | |
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| 1.4837 | 3.2702 | 0.35 | 71 | |
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| 1.3766 | 3.2214 | 0.4 | 72 | |
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| 1.5898 | 3.2674 | 0.4 | 73 | |
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| 1.5002 | 3.1907 | 0.4 | 74 | |
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| 1.2641 | 3.1176 | 0.4 | 75 | |
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| 1.3456 | 3.1562 | 0.4 | 76 | |
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| 1.2655 | 2.9548 | 0.5 | 77 | |
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| 1.5449 | 2.8738 | 0.5 | 78 | |
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| 1.2519 | 2.8336 | 0.45 | 79 | |
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| 1.0682 | 2.8478 | 0.35 | 80 | |
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| 1.1891 | 2.8408 | 0.5 | 81 | |
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| 1.2920 | 2.6254 | 0.5 | 82 | |
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| 1.1239 | 2.7507 | 0.5 | 83 | |
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| 1.0857 | 2.7772 | 0.4 | 84 | |
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| 0.9821 | 2.8372 | 0.45 | 85 | |
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| 1.0457 | 2.8636 | 0.45 | 86 | |
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| 1.1419 | 2.8426 | 0.45 | 87 | |
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| 1.0782 | 2.7856 | 0.5 | 88 | |
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| 0.9906 | 2.6826 | 0.55 | 89 | |
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| 1.0766 | 2.6707 | 0.5 | 90 | |
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| 1.1115 | 2.6457 | 0.5 | 91 | |
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| 1.2201 | 2.6838 | 0.55 | 92 | |
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| 0.8706 | 2.5262 | 0.55 | 93 | |
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| 0.7441 | 2.5422 | 0.55 | 94 | |
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| 0.9710 | 2.4211 | 0.6 | 95 | |
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| 0.9731 | 2.4090 | 0.6 | 96 | |
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| 0.8942 | 2.3773 | 0.6 | 97 | |
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| 1.0461 | 2.4159 | 0.55 | 98 | |
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| 0.8261 | 2.4425 | 0.6 | 99 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- TensorFlow 2.13.0-rc2 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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