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carolinetfls
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plant-seedlings-model-swin

Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results
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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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+
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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-swin
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+
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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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+
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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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+ | 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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+
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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