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carolinetfls
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plant-seedlings-model-mit

Image Classification
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segformer
Generated from Trainer
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+ ---
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+ license: other
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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-mit
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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: 0.9400785854616895
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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-mit
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+
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2052
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+ - Accuracy: 0.9401
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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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+ | 2.459 | 0.2 | 100 | 2.4084 | 0.1424 |
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+ | 1.7264 | 0.39 | 200 | 1.5604 | 0.4430 |
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+ | 1.427 | 0.59 | 300 | 1.2719 | 0.5447 |
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+ | 1.1796 | 0.79 | 400 | 0.9608 | 0.6469 |
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+ | 0.6449 | 0.98 | 500 | 0.9086 | 0.6783 |
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+ | 0.819 | 1.18 | 600 | 0.8235 | 0.7230 |
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+ | 0.711 | 1.38 | 700 | 0.8286 | 0.7161 |
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+ | 0.6829 | 1.57 | 800 | 0.6853 | 0.7829 |
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+ | 0.7093 | 1.77 | 900 | 0.8823 | 0.7112 |
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+ | 0.6265 | 1.96 | 1000 | 0.5434 | 0.8129 |
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+ | 0.6062 | 2.16 | 1100 | 0.4865 | 0.8301 |
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+ | 0.6318 | 2.36 | 1200 | 0.5239 | 0.8256 |
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+ | 0.5195 | 2.55 | 1300 | 0.5997 | 0.7809 |
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+ | 0.5847 | 2.75 | 1400 | 0.5282 | 0.8099 |
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+ | 0.4684 | 2.95 | 1500 | 0.4301 | 0.8502 |
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+ | 0.7026 | 3.14 | 1600 | 0.4628 | 0.8522 |
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+ | 0.443 | 3.34 | 1700 | 0.4201 | 0.8492 |
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+ | 0.6532 | 3.54 | 1800 | 0.4979 | 0.8330 |
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+ | 0.5021 | 3.73 | 1900 | 0.5098 | 0.8202 |
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+ | 0.4203 | 3.93 | 2000 | 0.4277 | 0.8512 |
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+ | 0.4201 | 4.13 | 2100 | 0.4046 | 0.8649 |
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+ | 0.397 | 4.32 | 2200 | 0.5747 | 0.8158 |
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+ | 0.472 | 4.52 | 2300 | 0.5175 | 0.8237 |
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+ | 0.5614 | 4.72 | 2400 | 0.4351 | 0.8443 |
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+ | 0.3184 | 4.91 | 2500 | 0.3635 | 0.8787 |
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+ | 0.3409 | 5.11 | 2600 | 0.4374 | 0.8571 |
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+ | 0.3132 | 5.3 | 2700 | 0.3622 | 0.8767 |
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+ | 0.3928 | 5.5 | 2800 | 0.3522 | 0.8797 |
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+ | 0.4538 | 5.7 | 2900 | 0.3652 | 0.8718 |
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+ | 0.5516 | 5.89 | 3000 | 0.4128 | 0.8689 |
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+ | 0.4113 | 6.09 | 3100 | 0.3973 | 0.8649 |
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+ | 0.3365 | 6.29 | 3200 | 0.4116 | 0.8635 |
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+ | 0.4611 | 6.48 | 3300 | 0.3312 | 0.8846 |
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+ | 0.312 | 6.68 | 3400 | 0.3888 | 0.8679 |
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+ | 0.3811 | 6.88 | 3500 | 0.3388 | 0.8841 |
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+ | 0.3711 | 7.07 | 3600 | 0.3300 | 0.8954 |
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+ | 0.4593 | 7.27 | 3700 | 0.3491 | 0.8831 |
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+ | 0.5211 | 7.47 | 3800 | 0.3682 | 0.8895 |
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+ | 0.2319 | 7.66 | 3900 | 0.3326 | 0.8861 |
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+ | 0.3811 | 7.86 | 4000 | 0.3407 | 0.8910 |
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+ | 0.4044 | 8.06 | 4100 | 0.3076 | 0.9028 |
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+ | 0.367 | 8.25 | 4200 | 0.3126 | 0.9023 |
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+ | 0.3862 | 8.45 | 4300 | 0.3281 | 0.8954 |
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+ | 0.2489 | 8.64 | 4400 | 0.3166 | 0.8929 |
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+ | 0.3197 | 8.84 | 4500 | 0.3564 | 0.8802 |
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+ | 0.3114 | 9.04 | 4600 | 0.2978 | 0.8969 |
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+ | 0.3589 | 9.23 | 4700 | 0.3438 | 0.8895 |
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+ | 0.3075 | 9.43 | 4800 | 0.2894 | 0.9082 |
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+ | 0.3862 | 9.63 | 4900 | 0.2880 | 0.9047 |
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+ | 0.3319 | 9.82 | 5000 | 0.3628 | 0.8915 |
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+ | 0.3022 | 10.02 | 5100 | 0.2624 | 0.9145 |
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+ | 0.2697 | 10.22 | 5200 | 0.3866 | 0.8851 |
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+ | 0.218 | 10.41 | 5300 | 0.2632 | 0.9101 |
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+ | 0.3331 | 10.61 | 5400 | 0.3117 | 0.9023 |
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+ | 0.3043 | 10.81 | 5500 | 0.3604 | 0.8900 |
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+ | 0.3105 | 11.0 | 5600 | 0.2847 | 0.9111 |
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+ | 0.1758 | 11.2 | 5700 | 0.3144 | 0.9082 |
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+ | 0.2081 | 11.39 | 5800 | 0.2898 | 0.9101 |
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+ | 0.4005 | 11.59 | 5900 | 0.3138 | 0.8998 |
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+ | 0.264 | 11.79 | 6000 | 0.2792 | 0.9136 |
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+ | 0.2765 | 11.98 | 6100 | 0.3021 | 0.9003 |
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+ | 0.2595 | 12.18 | 6200 | 0.2625 | 0.9091 |
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+ | 0.2745 | 12.38 | 6300 | 0.3078 | 0.9057 |
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+ | 0.2437 | 12.57 | 6400 | 0.2533 | 0.9194 |
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+ | 0.3765 | 12.77 | 6500 | 0.2972 | 0.9008 |
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+ | 0.2911 | 12.97 | 6600 | 0.2909 | 0.9096 |
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+ | 0.2335 | 13.16 | 6700 | 0.2684 | 0.9136 |
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+ | 0.3099 | 13.36 | 6800 | 0.3057 | 0.9086 |
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+ | 0.2377 | 13.56 | 6900 | 0.2862 | 0.9140 |
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+ | 0.3159 | 13.75 | 7000 | 0.2271 | 0.9273 |
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+ | 0.1893 | 13.95 | 7100 | 0.2519 | 0.9244 |
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+ | 0.1703 | 14.15 | 7200 | 0.2616 | 0.9209 |
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+ | 0.2527 | 14.34 | 7300 | 0.2393 | 0.9293 |
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+ | 0.3772 | 14.54 | 7400 | 0.2662 | 0.9160 |
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+ | 0.2574 | 14.73 | 7500 | 0.2724 | 0.9155 |
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+ | 0.1803 | 14.93 | 7600 | 0.2549 | 0.9199 |
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+ | 0.2935 | 15.13 | 7700 | 0.2561 | 0.9185 |
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+ | 0.2105 | 15.32 | 7800 | 0.2202 | 0.9244 |
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+ | 0.2877 | 15.52 | 7900 | 0.2428 | 0.9234 |
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+ | 0.2467 | 15.72 | 8000 | 0.2531 | 0.9229 |
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+ | 0.2955 | 15.91 | 8100 | 0.3258 | 0.9194 |
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+ | 0.3136 | 16.11 | 8200 | 0.2430 | 0.9263 |
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+ | 0.2543 | 16.31 | 8300 | 0.2502 | 0.9204 |
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+ | 0.161 | 16.5 | 8400 | 0.2241 | 0.9352 |
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+ | 0.194 | 16.7 | 8500 | 0.2313 | 0.9298 |
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+ | 0.1951 | 16.9 | 8600 | 0.2446 | 0.9219 |
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+ | 0.2515 | 17.09 | 8700 | 0.2476 | 0.9224 |
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+ | 0.1274 | 17.29 | 8800 | 0.2445 | 0.9273 |
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+ | 0.3035 | 17.49 | 8900 | 0.2704 | 0.9239 |
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+ | 0.2253 | 17.68 | 9000 | 0.2436 | 0.9332 |
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+ | 0.0982 | 17.88 | 9100 | 0.2523 | 0.9327 |
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+ | 0.1778 | 18.07 | 9200 | 0.2425 | 0.9322 |
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+ | 0.1362 | 18.27 | 9300 | 0.2653 | 0.9219 |
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+ | 0.2342 | 18.47 | 9400 | 0.2076 | 0.9401 |
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+ | 0.2231 | 18.66 | 9500 | 0.2238 | 0.9361 |
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+ | 0.2159 | 18.86 | 9600 | 0.2115 | 0.9357 |
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+ | 0.1826 | 19.06 | 9700 | 0.2079 | 0.9332 |
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+ | 0.2221 | 19.25 | 9800 | 0.2003 | 0.9366 |
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+ | 0.136 | 19.45 | 9900 | 0.2170 | 0.9401 |
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+ | 0.0959 | 19.65 | 10000 | 0.1891 | 0.9440 |
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+ | 0.1525 | 19.84 | 10100 | 0.2052 | 0.9401 |
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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