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plant-seedlings-model-beit-free-0-6

Image Classification
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beit
Generated from Trainer
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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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+ model-index:
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+ - name: plant-seedlings-model-beit-free-0-6
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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: 0.7475442043222004
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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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+ # plant-seedlings-model-beit-free-0-6
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+
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+ This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7557
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+ - Accuracy: 0.7475
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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: 20
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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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+ | 2.4892 | 0.2 | 100 | 2.4909 | 0.0751 |
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+ | 2.4906 | 0.39 | 200 | 2.4886 | 0.0756 |
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+ | 2.3925 | 0.59 | 300 | 2.3344 | 0.1537 |
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+ | 2.31 | 0.79 | 400 | 2.3306 | 0.1464 |
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+ | 2.2355 | 0.98 | 500 | 2.2335 | 0.1778 |
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+ | 2.2642 | 1.18 | 600 | 2.1889 | 0.1807 |
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+ | 2.0806 | 1.38 | 700 | 2.3229 | 0.1680 |
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+ | 2.1013 | 1.57 | 800 | 2.1519 | 0.2004 |
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+ | 2.0094 | 1.77 | 900 | 2.0611 | 0.2146 |
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+ | 2.0387 | 1.96 | 1000 | 2.0413 | 0.2210 |
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+ | 2.0032 | 2.16 | 1100 | 1.9758 | 0.2618 |
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+ | 1.986 | 2.36 | 1200 | 1.9238 | 0.2638 |
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+ | 2.0885 | 2.55 | 1300 | 1.8944 | 0.2942 |
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+ | 1.8808 | 2.75 | 1400 | 1.9330 | 0.2868 |
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+ | 1.915 | 2.95 | 1500 | 1.8919 | 0.2814 |
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+ | 1.958 | 3.14 | 1600 | 1.8762 | 0.3114 |
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+ | 1.9001 | 3.34 | 1700 | 1.8389 | 0.3232 |
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+ | 1.8572 | 3.54 | 1800 | 1.7978 | 0.3487 |
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+ | 1.9969 | 3.73 | 1900 | 1.9371 | 0.3089 |
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+ | 1.9186 | 3.93 | 2000 | 1.8055 | 0.3502 |
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+ | 1.7591 | 4.13 | 2100 | 1.7695 | 0.3428 |
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+ | 1.8368 | 4.32 | 2200 | 1.7498 | 0.3502 |
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+ | 1.9842 | 4.52 | 2300 | 1.8049 | 0.3193 |
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+ | 1.7606 | 4.72 | 2400 | 1.6730 | 0.3954 |
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+ | 1.7787 | 4.91 | 2500 | 1.7104 | 0.3777 |
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+ | 1.6377 | 5.11 | 2600 | 1.6647 | 0.3870 |
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+ | 1.8834 | 5.3 | 2700 | 1.6325 | 0.3973 |
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+ | 1.6149 | 5.5 | 2800 | 1.6722 | 0.3787 |
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+ | 1.7038 | 5.7 | 2900 | 1.6425 | 0.3973 |
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+ | 1.682 | 5.89 | 3000 | 1.5927 | 0.4180 |
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+ | 1.6326 | 6.09 | 3100 | 1.4982 | 0.4622 |
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+ | 1.5687 | 6.29 | 3200 | 1.4440 | 0.4774 |
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+ | 1.3637 | 6.48 | 3300 | 1.4477 | 0.4877 |
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+ | 1.4079 | 6.68 | 3400 | 1.3827 | 0.5020 |
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+ | 1.3721 | 6.88 | 3500 | 1.4069 | 0.5010 |
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+ | 1.5675 | 7.07 | 3600 | 1.3595 | 0.5083 |
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+ | 1.5725 | 7.27 | 3700 | 1.3790 | 0.4956 |
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+ | 1.4522 | 7.47 | 3800 | 1.3116 | 0.5378 |
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+ | 1.4692 | 7.66 | 3900 | 1.3729 | 0.4980 |
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+ | 1.5073 | 7.86 | 4000 | 1.3799 | 0.5216 |
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+ | 1.2529 | 8.06 | 4100 | 1.2706 | 0.5486 |
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+ | 1.3727 | 8.25 | 4200 | 1.2519 | 0.5535 |
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+ | 1.2451 | 8.45 | 4300 | 1.2595 | 0.5648 |
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+ | 1.339 | 8.64 | 4400 | 1.3614 | 0.5172 |
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+ | 1.2858 | 8.84 | 4500 | 1.3028 | 0.5393 |
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+ | 1.1039 | 9.04 | 4600 | 1.2309 | 0.5771 |
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+ | 1.0351 | 9.23 | 4700 | 1.2678 | 0.5609 |
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+ | 1.1125 | 9.43 | 4800 | 1.2786 | 0.5624 |
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+ | 1.1667 | 9.63 | 4900 | 1.2131 | 0.5840 |
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+ | 1.1386 | 9.82 | 5000 | 1.1359 | 0.6154 |
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+ | 1.1888 | 10.02 | 5100 | 1.1309 | 0.6041 |
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+ | 1.1777 | 10.22 | 5200 | 1.1288 | 0.6287 |
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+ | 1.3693 | 10.41 | 5300 | 1.3827 | 0.5182 |
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+ | 1.1016 | 10.61 | 5400 | 1.2255 | 0.5594 |
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+ | 1.1527 | 10.81 | 5500 | 1.0772 | 0.6434 |
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+ | 1.1039 | 11.0 | 5600 | 1.1032 | 0.6100 |
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+ | 1.2502 | 11.2 | 5700 | 1.1230 | 0.6169 |
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+ | 1.0818 | 11.39 | 5800 | 1.0750 | 0.6302 |
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+ | 1.0872 | 11.59 | 5900 | 1.0397 | 0.6331 |
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+ | 1.0425 | 11.79 | 6000 | 1.0231 | 0.6483 |
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+ | 1.0791 | 11.98 | 6100 | 1.0250 | 0.6636 |
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+ | 0.9736 | 12.18 | 6200 | 1.0879 | 0.6267 |
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+ | 0.9788 | 12.38 | 6300 | 1.1334 | 0.5968 |
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+ | 0.8982 | 12.57 | 6400 | 0.9934 | 0.6528 |
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+ | 1.077 | 12.77 | 6500 | 0.9698 | 0.6812 |
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+ | 1.0347 | 12.97 | 6600 | 1.0265 | 0.6513 |
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+ | 0.9159 | 13.16 | 6700 | 0.9442 | 0.6788 |
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+ | 1.1187 | 13.36 | 6800 | 0.9738 | 0.6685 |
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+ | 0.9624 | 13.56 | 6900 | 1.0008 | 0.6699 |
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+ | 0.922 | 13.75 | 7000 | 0.9502 | 0.6906 |
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+ | 0.9317 | 13.95 | 7100 | 0.9687 | 0.6758 |
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+ | 0.9979 | 14.15 | 7200 | 0.9869 | 0.6768 |
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+ | 0.8362 | 14.34 | 7300 | 0.9220 | 0.6994 |
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+ | 0.8449 | 14.54 | 7400 | 0.9181 | 0.6861 |
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+ | 0.9678 | 14.73 | 7500 | 0.9789 | 0.6729 |
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+ | 0.9119 | 14.93 | 7600 | 0.8879 | 0.7009 |
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+ | 0.9517 | 15.13 | 7700 | 0.8816 | 0.6994 |
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+ | 0.9688 | 15.32 | 7800 | 0.8803 | 0.7117 |
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+ | 0.8625 | 15.52 | 7900 | 0.8782 | 0.7038 |
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+ | 0.9121 | 15.72 | 8000 | 0.8225 | 0.7191 |
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+ | 0.9035 | 15.91 | 8100 | 0.8649 | 0.7087 |
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+ | 0.8762 | 16.11 | 8200 | 0.8427 | 0.7102 |
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+ | 0.7708 | 16.31 | 8300 | 0.8685 | 0.7117 |
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+ | 0.8893 | 16.5 | 8400 | 0.8178 | 0.7264 |
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+ | 0.9584 | 16.7 | 8500 | 0.8709 | 0.7092 |
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+ | 0.757 | 16.9 | 8600 | 0.8244 | 0.7254 |
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+ | 0.8184 | 17.09 | 8700 | 0.8128 | 0.7240 |
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+ | 0.8858 | 17.29 | 8800 | 0.8360 | 0.7156 |
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+ | 0.7116 | 17.49 | 8900 | 0.7952 | 0.7279 |
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+ | 0.9579 | 17.68 | 9000 | 0.8263 | 0.7274 |
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+ | 0.7037 | 17.88 | 9100 | 0.7884 | 0.7348 |
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+ | 1.0359 | 18.07 | 9200 | 0.8118 | 0.7402 |
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+ | 1.067 | 18.27 | 9300 | 0.8203 | 0.7186 |
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+ | 0.8503 | 18.47 | 9400 | 0.7918 | 0.7362 |
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+ | 0.8552 | 18.66 | 9500 | 0.7972 | 0.7382 |
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+ | 0.7498 | 18.86 | 9600 | 0.8038 | 0.7343 |
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+ | 0.8542 | 19.06 | 9700 | 0.7799 | 0.7333 |
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+ | 0.9539 | 19.25 | 9800 | 0.7795 | 0.7333 |
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+ | 0.7369 | 19.45 | 9900 | 0.8103 | 0.7269 |
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+ | 0.6637 | 19.65 | 10000 | 0.7597 | 0.7441 |
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+ | 0.6712 | 19.84 | 10100 | 0.7557 | 0.7475 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.1
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+ - Pytorch 2.0.0+cu118
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+ - Datasets 2.11.0
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+ - Tokenizers 0.13.3