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carolinetfls
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plant-seedlings-model-ConvNet-final

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convnext
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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9429097605893186
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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
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.2056
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- - Accuracy: 0.9429
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  ## Model description
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@@ -57,69 +57,94 @@ The following hyperparameters were used during training:
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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: 14
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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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- | 0.4109 | 0.25 | 100 | 0.5246 | 0.8195 |
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- | 0.248 | 0.49 | 200 | 0.4594 | 0.8459 |
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- | 0.3389 | 0.74 | 300 | 0.4443 | 0.8551 |
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- | 0.4217 | 0.98 | 400 | 0.4500 | 0.8490 |
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- | 0.2815 | 1.23 | 500 | 0.3939 | 0.8588 |
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- | 0.3077 | 1.47 | 600 | 0.3813 | 0.8643 |
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- | 0.5098 | 1.72 | 700 | 0.4276 | 0.8576 |
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- | 0.3191 | 1.97 | 800 | 0.4218 | 0.8570 |
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- | 0.2761 | 2.21 | 900 | 0.3404 | 0.8883 |
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- | 0.2184 | 2.46 | 1000 | 0.3226 | 0.8889 |
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- | 0.3106 | 2.7 | 1100 | 0.3621 | 0.8729 |
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- | 0.3118 | 2.95 | 1200 | 0.3656 | 0.8797 |
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- | 0.2857 | 3.19 | 1300 | 0.3123 | 0.9012 |
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- | 0.2193 | 3.44 | 1400 | 0.2907 | 0.9048 |
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- | 0.2959 | 3.69 | 1500 | 0.3544 | 0.8840 |
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- | 0.3176 | 3.93 | 1600 | 0.3389 | 0.8877 |
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- | 0.2927 | 4.18 | 1700 | 0.3418 | 0.8864 |
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- | 0.2719 | 4.42 | 1800 | 0.3558 | 0.8821 |
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- | 0.2176 | 4.67 | 1900 | 0.3374 | 0.8981 |
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- | 0.1912 | 4.91 | 2000 | 0.3092 | 0.8999 |
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- | 0.2272 | 5.16 | 2100 | 0.2902 | 0.9128 |
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- | 0.175 | 5.41 | 2200 | 0.3002 | 0.9134 |
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- | 0.1513 | 5.65 | 2300 | 0.3356 | 0.8999 |
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- | 0.1439 | 5.9 | 2400 | 0.2954 | 0.9061 |
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- | 0.2341 | 6.14 | 2500 | 0.3343 | 0.8993 |
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- | 0.2178 | 6.39 | 2600 | 0.2891 | 0.9122 |
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- | 0.1731 | 6.63 | 2700 | 0.3235 | 0.9030 |
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- | 0.19 | 6.88 | 2800 | 0.2938 | 0.9042 |
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- | 0.1168 | 7.13 | 2900 | 0.2937 | 0.9110 |
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- | 0.1528 | 7.37 | 3000 | 0.2963 | 0.9104 |
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- | 0.1374 | 7.62 | 3100 | 0.2929 | 0.9085 |
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- | 0.2204 | 7.86 | 3200 | 0.3257 | 0.9048 |
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- | 0.1519 | 8.11 | 3300 | 0.2683 | 0.9171 |
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- | 0.0711 | 8.35 | 3400 | 0.2609 | 0.9251 |
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- | 0.1019 | 8.6 | 3500 | 0.2523 | 0.9251 |
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- | 0.1764 | 8.85 | 3600 | 0.2769 | 0.9202 |
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- | 0.0849 | 9.09 | 3700 | 0.2668 | 0.9214 |
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- | 0.2077 | 9.34 | 3800 | 0.2914 | 0.9165 |
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- | 0.2543 | 9.58 | 3900 | 0.2507 | 0.9251 |
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- | 0.0347 | 9.83 | 4000 | 0.2333 | 0.9269 |
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- | 0.0731 | 10.07 | 4100 | 0.2598 | 0.9269 |
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- | 0.238 | 10.32 | 4200 | 0.2675 | 0.9294 |
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- | 0.1114 | 10.57 | 4300 | 0.2317 | 0.9269 |
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- | 0.0836 | 10.81 | 4400 | 0.2344 | 0.9288 |
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- | 0.0598 | 11.06 | 4500 | 0.2499 | 0.9276 |
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- | 0.0488 | 11.3 | 4600 | 0.2361 | 0.9288 |
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- | 0.1437 | 11.55 | 4700 | 0.2551 | 0.9282 |
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- | 0.0773 | 11.79 | 4800 | 0.2276 | 0.9294 |
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- | 0.1013 | 12.04 | 4900 | 0.2537 | 0.9288 |
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- | 0.0943 | 12.29 | 5000 | 0.2368 | 0.9331 |
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- | 0.0538 | 12.53 | 5100 | 0.2157 | 0.9349 |
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- | 0.0425 | 12.78 | 5200 | 0.2330 | 0.9411 |
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- | 0.1301 | 13.02 | 5300 | 0.2564 | 0.9331 |
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- | 0.062 | 13.27 | 5400 | 0.2193 | 0.9417 |
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- | 0.1012 | 13.51 | 5500 | 0.1873 | 0.9466 |
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- | 0.1643 | 13.76 | 5600 | 0.2056 | 0.9429 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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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  ---
26
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
31
 
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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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  - 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