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SiddharthaM
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resnet-18-feature-extraction

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+ ---
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+ license: apache-2.0
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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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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: resnet-18-feature-extraction
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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: 0.95
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+ - name: Precision
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+ type: precision
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+ value: 0.9652777777777778
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+ - name: Recall
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+ type: recall
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+ value: 0.9788732394366197
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+ - name: F1
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+ type: f1
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+ value: 0.972027972027972
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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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+ # resnet-18-feature-extraction
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+
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+ This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1485
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+ - Accuracy: 0.95
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+ - Precision: 0.9653
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+ - Recall: 0.9789
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+ - F1: 0.9720
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+ - Roc Auc: 0.8505
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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: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 256
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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 | Precision | Recall | F1 | Roc Auc |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
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+ | No log | 0.8 | 2 | 0.6232 | 0.75 | 0.9636 | 0.7465 | 0.8413 | 0.7621 |
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+ | No log | 1.8 | 4 | 0.6971 | 0.4875 | 1.0 | 0.4225 | 0.5941 | 0.7113 |
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+ | No log | 2.8 | 6 | 0.7915 | 0.2875 | 1.0 | 0.1972 | 0.3294 | 0.5986 |
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+ | No log | 3.8 | 8 | 0.8480 | 0.2875 | 1.0 | 0.1972 | 0.3294 | 0.5986 |
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+ | 0.8651 | 4.8 | 10 | 0.9094 | 0.2562 | 1.0 | 0.1620 | 0.2788 | 0.5810 |
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+ | 0.8651 | 5.8 | 12 | 0.7470 | 0.5625 | 1.0 | 0.5070 | 0.6729 | 0.7535 |
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+ | 0.8651 | 6.8 | 14 | 0.5915 | 0.85 | 1.0 | 0.8310 | 0.9077 | 0.9155 |
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+ | 0.8651 | 7.8 | 16 | 0.4817 | 0.8875 | 0.9844 | 0.8873 | 0.9333 | 0.8881 |
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+ | 0.8651 | 8.8 | 18 | 0.3455 | 0.9187 | 0.9778 | 0.9296 | 0.9531 | 0.8815 |
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+ | 0.5349 | 9.8 | 20 | 0.2966 | 0.9187 | 0.9708 | 0.9366 | 0.9534 | 0.8572 |
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+ | 0.5349 | 10.8 | 22 | 0.2347 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
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+ | 0.5349 | 11.8 | 24 | 0.2468 | 0.9313 | 0.9645 | 0.9577 | 0.9611 | 0.8400 |
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+ | 0.5349 | 12.8 | 26 | 0.2310 | 0.9563 | 0.9720 | 0.9789 | 0.9754 | 0.8783 |
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+ | 0.5349 | 13.8 | 28 | 0.2083 | 0.9313 | 0.9580 | 0.9648 | 0.9614 | 0.8157 |
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+ | 0.3593 | 14.8 | 30 | 0.1840 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
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+ | 0.3593 | 15.8 | 32 | 0.1947 | 0.9375 | 0.9648 | 0.9648 | 0.9648 | 0.8435 |
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+ | 0.3593 | 16.8 | 34 | 0.1837 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
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+ | 0.3593 | 17.8 | 36 | 0.1819 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
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+ | 0.3593 | 18.8 | 38 | 0.1924 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
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+ | 0.2737 | 19.8 | 40 | 0.1990 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
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+ | 0.2737 | 20.8 | 42 | 0.1759 | 0.95 | 0.9718 | 0.9718 | 0.9718 | 0.8748 |
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+ | 0.2737 | 21.8 | 44 | 0.1804 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
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+ | 0.2737 | 22.8 | 46 | 0.1666 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
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+ | 0.2737 | 23.8 | 48 | 0.1534 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
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+ | 0.2278 | 24.8 | 50 | 0.1612 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
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+ | 0.2278 | 25.8 | 52 | 0.1535 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
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+ | 0.2278 | 26.8 | 54 | 0.1568 | 0.9437 | 0.9716 | 0.9648 | 0.9682 | 0.8713 |
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+ | 0.2278 | 27.8 | 56 | 0.2107 | 0.9375 | 0.9714 | 0.9577 | 0.9645 | 0.8678 |
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+ | 0.2278 | 28.8 | 58 | 0.1592 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
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+ | 0.2057 | 29.8 | 60 | 0.1557 | 0.9375 | 0.9648 | 0.9648 | 0.9648 | 0.8435 |
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+ | 0.2057 | 30.8 | 62 | 0.1714 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
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+ | 0.2057 | 31.8 | 64 | 0.1571 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
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+ | 0.2057 | 32.8 | 66 | 0.1574 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
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+ | 0.2057 | 33.8 | 68 | 0.1423 | 0.9563 | 0.9720 | 0.9789 | 0.9754 | 0.8783 |
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+ | 0.2 | 34.8 | 70 | 0.1677 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
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+ | 0.2 | 35.8 | 72 | 0.1560 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
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+ | 0.2 | 36.8 | 74 | 0.1594 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
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+ | 0.2 | 37.8 | 76 | 0.1512 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
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+ | 0.2 | 38.8 | 78 | 0.1396 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
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+ | 0.1838 | 39.8 | 80 | 0.1509 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
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+ | 0.1838 | 40.8 | 82 | 0.1529 | 0.95 | 0.9718 | 0.9718 | 0.9718 | 0.8748 |
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+ | 0.1838 | 41.8 | 84 | 0.1506 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
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+ | 0.1838 | 42.8 | 86 | 0.1549 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
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+ | 0.1838 | 43.8 | 88 | 0.1331 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
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+ | 0.1872 | 44.8 | 90 | 0.1409 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
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+ | 0.1872 | 45.8 | 92 | 0.1639 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
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+ | 0.1872 | 46.8 | 94 | 0.1391 | 0.95 | 0.9589 | 0.9859 | 0.9722 | 0.8263 |
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+ | 0.1872 | 47.8 | 96 | 0.1436 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
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+ | 0.1872 | 48.8 | 98 | 0.1442 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
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+ | 0.185 | 49.8 | 100 | 0.1485 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.24.0.dev0
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+ - Pytorch 1.11.0+cu102
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+ - Datasets 2.6.1
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+ - Tokenizers 0.13.1