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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-ConvNet-all-train |
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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: 0.9392265193370166 |
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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-ConvNet-all-train |
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2653 |
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- Accuracy: 0.9392 |
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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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| 0.2307 | 0.25 | 100 | 0.4912 | 0.8729 | |
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| 0.0652 | 0.49 | 200 | 0.3280 | 0.9085 | |
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| 0.1854 | 0.74 | 300 | 0.4850 | 0.8711 | |
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| 0.1831 | 0.98 | 400 | 0.3827 | 0.8938 | |
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| 0.1636 | 1.23 | 500 | 0.4071 | 0.9012 | |
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| 0.0868 | 1.47 | 600 | 0.3980 | 0.8999 | |
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| 0.2298 | 1.72 | 700 | 0.4855 | 0.8846 | |
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| 0.2291 | 1.97 | 800 | 0.4019 | 0.8883 | |
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| 0.2698 | 2.21 | 900 | 0.3855 | 0.8944 | |
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| 0.0923 | 2.46 | 1000 | 0.3690 | 0.8938 | |
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| 0.1396 | 2.7 | 1100 | 0.4715 | 0.8760 | |
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| 0.174 | 2.95 | 1200 | 0.3710 | 0.9006 | |
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| 0.1009 | 3.19 | 1300 | 0.3481 | 0.9030 | |
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| 0.1162 | 3.44 | 1400 | 0.3502 | 0.9153 | |
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| 0.1737 | 3.69 | 1500 | 0.4034 | 0.8999 | |
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| 0.2478 | 3.93 | 1600 | 0.4053 | 0.8913 | |
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| 0.1471 | 4.18 | 1700 | 0.3555 | 0.9036 | |
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| 0.1873 | 4.42 | 1800 | 0.3769 | 0.9122 | |
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| 0.0615 | 4.67 | 1900 | 0.4147 | 0.8987 | |
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| 0.1718 | 4.91 | 2000 | 0.2779 | 0.9214 | |
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| 0.1012 | 5.16 | 2100 | 0.3239 | 0.9159 | |
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| 0.0967 | 5.41 | 2200 | 0.3290 | 0.9079 | |
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| 0.0873 | 5.65 | 2300 | 0.4057 | 0.9055 | |
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| 0.0567 | 5.9 | 2400 | 0.3821 | 0.9018 | |
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| 0.1356 | 6.14 | 2500 | 0.4183 | 0.8944 | |
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| 0.168 | 6.39 | 2600 | 0.3755 | 0.9067 | |
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| 0.1592 | 6.63 | 2700 | 0.3413 | 0.9079 | |
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| 0.1239 | 6.88 | 2800 | 0.3299 | 0.9091 | |
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| 0.0382 | 7.13 | 2900 | 0.3391 | 0.9165 | |
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| 0.1167 | 7.37 | 3000 | 0.4274 | 0.8987 | |
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| 0.109 | 7.62 | 3100 | 0.3952 | 0.9018 | |
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| 0.0591 | 7.86 | 3200 | 0.4043 | 0.9122 | |
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| 0.1407 | 8.11 | 3300 | 0.3325 | 0.9134 | |
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| 0.054 | 8.35 | 3400 | 0.3333 | 0.9177 | |
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| 0.0633 | 8.6 | 3500 | 0.3275 | 0.9208 | |
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| 0.1038 | 8.85 | 3600 | 0.3982 | 0.9042 | |
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| 0.0435 | 9.09 | 3700 | 0.3656 | 0.9190 | |
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| 0.1549 | 9.34 | 3800 | 0.3367 | 0.9190 | |
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| 0.2299 | 9.58 | 3900 | 0.3872 | 0.9134 | |
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| 0.0375 | 9.83 | 4000 | 0.3206 | 0.9245 | |
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| 0.0204 | 10.07 | 4100 | 0.3133 | 0.9263 | |
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| 0.1208 | 10.32 | 4200 | 0.3373 | 0.9196 | |
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| 0.0617 | 10.57 | 4300 | 0.3045 | 0.9220 | |
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| 0.1426 | 10.81 | 4400 | 0.2972 | 0.9294 | |
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| 0.0351 | 11.06 | 4500 | 0.3409 | 0.9147 | |
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| 0.0311 | 11.3 | 4600 | 0.3003 | 0.9233 | |
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| 0.1255 | 11.55 | 4700 | 0.3447 | 0.9282 | |
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| 0.0569 | 11.79 | 4800 | 0.2703 | 0.9331 | |
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| 0.0918 | 12.04 | 4900 | 0.3170 | 0.9245 | |
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| 0.0656 | 12.29 | 5000 | 0.3223 | 0.9190 | |
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| 0.0971 | 12.53 | 5100 | 0.3209 | 0.9196 | |
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| 0.0742 | 12.78 | 5200 | 0.3030 | 0.9282 | |
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| 0.0662 | 13.02 | 5300 | 0.2780 | 0.9319 | |
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| 0.0453 | 13.27 | 5400 | 0.3360 | 0.9227 | |
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| 0.0869 | 13.51 | 5500 | 0.2417 | 0.9343 | |
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| 0.1786 | 13.76 | 5600 | 0.3078 | 0.9263 | |
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| 0.1563 | 14.0 | 5700 | 0.3046 | 0.9312 | |
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| 0.0584 | 14.25 | 5800 | 0.3011 | 0.9288 | |
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| 0.0783 | 14.5 | 5900 | 0.2705 | 0.9288 | |
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| 0.0486 | 14.74 | 6000 | 0.2583 | 0.9288 | |
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| 0.094 | 14.99 | 6100 | 0.2854 | 0.9282 | |
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| 0.0852 | 15.23 | 6200 | 0.2693 | 0.9325 | |
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| 0.0665 | 15.48 | 6300 | 0.2754 | 0.9282 | |
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| 0.0948 | 15.72 | 6400 | 0.2598 | 0.9349 | |
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| 0.0368 | 15.97 | 6500 | 0.2875 | 0.9355 | |
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| 0.0031 | 16.22 | 6600 | 0.2679 | 0.9325 | |
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| 0.0796 | 16.46 | 6700 | 0.2642 | 0.9300 | |
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| 0.0903 | 16.71 | 6800 | 0.2977 | 0.9269 | |
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| 0.0952 | 16.95 | 6900 | 0.2615 | 0.9337 | |
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| 0.1344 | 17.2 | 7000 | 0.2948 | 0.9251 | |
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| 0.0854 | 17.44 | 7100 | 0.2748 | 0.9368 | |
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| 0.0891 | 17.69 | 7200 | 0.2386 | 0.9325 | |
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| 0.1202 | 17.94 | 7300 | 0.2509 | 0.9355 | |
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| 0.0832 | 18.18 | 7400 | 0.2406 | 0.9398 | |
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| 0.0949 | 18.43 | 7500 | 0.2356 | 0.9386 | |
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| 0.0404 | 18.67 | 7600 | 0.2415 | 0.9386 | |
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| 0.1008 | 18.92 | 7700 | 0.2582 | 0.9355 | |
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| 0.092 | 19.16 | 7800 | 0.2724 | 0.9325 | |
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| 0.0993 | 19.41 | 7900 | 0.2655 | 0.9325 | |
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| 0.0593 | 19.66 | 8000 | 0.2423 | 0.9386 | |
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| 0.1011 | 19.9 | 8100 | 0.2653 | 0.9392 | |
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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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