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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-swin |
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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: 0.9474459724950884 |
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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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# plant-seedlings-model-swin |
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This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2169 |
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- Accuracy: 0.9474 |
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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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- 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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.8259 | 0.2 | 100 | 0.7181 | 0.7520 | |
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| 1.0121 | 0.39 | 200 | 0.7504 | 0.7092 | |
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| 0.5952 | 0.59 | 300 | 0.6254 | 0.7986 | |
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| 0.6031 | 0.79 | 400 | 0.4595 | 0.8438 | |
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| 0.637 | 0.98 | 500 | 0.5830 | 0.8080 | |
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| 0.5896 | 1.18 | 600 | 0.5042 | 0.8384 | |
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| 0.6758 | 1.38 | 700 | 0.4827 | 0.8325 | |
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| 0.543 | 1.57 | 800 | 0.4713 | 0.8433 | |
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| 0.3312 | 1.77 | 900 | 0.4752 | 0.8546 | |
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| 0.5559 | 1.96 | 1000 | 0.4578 | 0.8369 | |
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| 0.4303 | 2.16 | 1100 | 0.5034 | 0.8389 | |
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| 0.5705 | 2.36 | 1200 | 0.4322 | 0.8502 | |
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| 0.5369 | 2.55 | 1300 | 0.4646 | 0.8404 | |
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| 0.3628 | 2.75 | 1400 | 0.3984 | 0.8659 | |
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| 0.4071 | 2.95 | 1500 | 0.3872 | 0.8689 | |
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| 0.4988 | 3.14 | 1600 | 0.3543 | 0.8792 | |
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| 0.4607 | 3.34 | 1700 | 0.3933 | 0.8674 | |
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| 0.3342 | 3.54 | 1800 | 0.3883 | 0.8639 | |
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| 0.4141 | 3.73 | 1900 | 0.3886 | 0.8644 | |
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| 0.5513 | 3.93 | 2000 | 0.3335 | 0.8900 | |
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| 0.4659 | 4.13 | 2100 | 0.4286 | 0.8590 | |
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| 0.2263 | 4.32 | 2200 | 0.3587 | 0.8772 | |
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| 0.4518 | 4.52 | 2300 | 0.3332 | 0.8870 | |
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| 0.3422 | 4.72 | 2400 | 0.2723 | 0.9062 | |
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| 0.6113 | 4.91 | 2500 | 0.2811 | 0.9057 | |
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| 0.3636 | 5.11 | 2600 | 0.3157 | 0.8939 | |
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| 0.2794 | 5.3 | 2700 | 0.2773 | 0.9082 | |
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| 0.3486 | 5.5 | 2800 | 0.3099 | 0.8978 | |
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| 0.2563 | 5.7 | 2900 | 0.3077 | 0.9052 | |
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| 0.3709 | 5.89 | 3000 | 0.3650 | 0.8836 | |
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| 0.3732 | 6.09 | 3100 | 0.3132 | 0.8988 | |
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| 0.2218 | 6.29 | 3200 | 0.2947 | 0.9052 | |
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| 0.2488 | 6.48 | 3300 | 0.2737 | 0.9131 | |
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| 0.2689 | 6.68 | 3400 | 0.3471 | 0.8924 | |
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| 0.3212 | 6.88 | 3500 | 0.3447 | 0.8905 | |
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| 0.3604 | 7.07 | 3600 | 0.2974 | 0.9086 | |
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| 0.2492 | 7.27 | 3700 | 0.3057 | 0.8993 | |
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| 0.1674 | 7.47 | 3800 | 0.3241 | 0.9032 | |
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| 0.3248 | 7.66 | 3900 | 0.2952 | 0.9077 | |
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| 0.204 | 7.86 | 4000 | 0.2883 | 0.9111 | |
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| 0.2783 | 8.06 | 4100 | 0.3017 | 0.9047 | |
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| 0.3721 | 8.25 | 4200 | 0.2782 | 0.9136 | |
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| 0.2554 | 8.45 | 4300 | 0.2625 | 0.9170 | |
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| 0.1104 | 8.64 | 4400 | 0.2590 | 0.9190 | |
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| 0.247 | 8.84 | 4500 | 0.3021 | 0.9096 | |
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| 0.3316 | 9.04 | 4600 | 0.3190 | 0.8988 | |
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| 0.3214 | 9.23 | 4700 | 0.2883 | 0.9140 | |
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| 0.192 | 9.43 | 4800 | 0.2770 | 0.9155 | |
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| 0.3568 | 9.63 | 4900 | 0.2475 | 0.9229 | |
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| 0.3365 | 9.82 | 5000 | 0.2568 | 0.9229 | |
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| 0.1226 | 10.02 | 5100 | 0.2534 | 0.9204 | |
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| 0.2359 | 10.22 | 5200 | 0.2679 | 0.9131 | |
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| 0.1623 | 10.41 | 5300 | 0.3127 | 0.9204 | |
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| 0.2369 | 10.61 | 5400 | 0.2779 | 0.9170 | |
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| 0.1234 | 10.81 | 5500 | 0.2486 | 0.9273 | |
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| 0.1823 | 11.0 | 5600 | 0.2608 | 0.9239 | |
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| 0.2875 | 11.2 | 5700 | 0.2612 | 0.9190 | |
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| 0.1408 | 11.39 | 5800 | 0.2208 | 0.9298 | |
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| 0.1094 | 11.59 | 5900 | 0.2399 | 0.9332 | |
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| 0.213 | 11.79 | 6000 | 0.2636 | 0.9209 | |
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| 0.1599 | 11.98 | 6100 | 0.2458 | 0.9249 | |
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| 0.2565 | 12.18 | 6200 | 0.2698 | 0.9204 | |
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| 0.0773 | 12.38 | 6300 | 0.2348 | 0.9322 | |
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| 0.1515 | 12.57 | 6400 | 0.2370 | 0.9263 | |
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| 0.2308 | 12.77 | 6500 | 0.2185 | 0.9307 | |
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| 0.2009 | 12.97 | 6600 | 0.2211 | 0.9342 | |
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| 0.2126 | 13.16 | 6700 | 0.2552 | 0.9342 | |
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| 0.1348 | 13.36 | 6800 | 0.2206 | 0.9371 | |
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| 0.1473 | 13.56 | 6900 | 0.2199 | 0.9357 | |
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| 0.1861 | 13.75 | 7000 | 0.2512 | 0.9224 | |
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| 0.1136 | 13.95 | 7100 | 0.2803 | 0.9214 | |
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| 0.1726 | 14.15 | 7200 | 0.2201 | 0.9361 | |
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| 0.202 | 14.34 | 7300 | 0.2105 | 0.9371 | |
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| 0.2043 | 14.54 | 7400 | 0.2472 | 0.9263 | |
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| 0.1427 | 14.73 | 7500 | 0.2250 | 0.9381 | |
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| 0.1599 | 14.93 | 7600 | 0.2270 | 0.9391 | |
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| 0.1216 | 15.13 | 7700 | 0.2409 | 0.9307 | |
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| 0.2869 | 15.32 | 7800 | 0.2208 | 0.9386 | |
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| 0.1254 | 15.52 | 7900 | 0.2298 | 0.9332 | |
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| 0.1314 | 15.72 | 8000 | 0.1959 | 0.9416 | |
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| 0.1106 | 15.91 | 8100 | 0.2183 | 0.9342 | |
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| 0.2211 | 16.11 | 8200 | 0.2581 | 0.9337 | |
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| 0.1589 | 16.31 | 8300 | 0.2091 | 0.9381 | |
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| 0.0791 | 16.5 | 8400 | 0.1792 | 0.9455 | |
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| 0.0849 | 16.7 | 8500 | 0.2481 | 0.9298 | |
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| 0.089 | 16.9 | 8600 | 0.2143 | 0.9386 | |
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| 0.0609 | 17.09 | 8700 | 0.2020 | 0.9524 | |
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| 0.1509 | 17.29 | 8800 | 0.2039 | 0.9396 | |
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| 0.0934 | 17.49 | 8900 | 0.2242 | 0.9322 | |
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| 0.0398 | 17.68 | 9000 | 0.1891 | 0.9460 | |
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| 0.1106 | 17.88 | 9100 | 0.1939 | 0.9470 | |
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| 0.1742 | 18.07 | 9200 | 0.1965 | 0.9479 | |
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| 0.1015 | 18.27 | 9300 | 0.1886 | 0.9440 | |
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| 0.089 | 18.47 | 9400 | 0.1851 | 0.9479 | |
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| 0.1393 | 18.66 | 9500 | 0.1844 | 0.9484 | |
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| 0.0849 | 18.86 | 9600 | 0.2205 | 0.9396 | |
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| 0.0708 | 19.06 | 9700 | 0.1888 | 0.9435 | |
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| 0.1037 | 19.25 | 9800 | 0.2070 | 0.9450 | |
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| 0.1109 | 19.45 | 9900 | 0.2079 | 0.9460 | |
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| 0.0533 | 19.65 | 10000 | 0.2036 | 0.9489 | |
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| 0.0757 | 19.84 | 10100 | 0.2169 | 0.9474 | |
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### Framework versions |
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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 |
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