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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: 0.9171143514965464
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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: 0.2966
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- - Accuracy: 0.9171
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  ## Model description
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@@ -57,65 +57,69 @@ 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: 16
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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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- | 1.2313 | 0.31 | 100 | 1.0832 | 0.6731 |
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- | 0.7221 | 0.61 | 200 | 0.6529 | 0.7913 |
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- | 0.5858 | 0.92 | 300 | 0.5267 | 0.8204 |
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- | 0.4257 | 1.23 | 400 | 0.5765 | 0.8051 |
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- | 0.6183 | 1.53 | 500 | 0.6322 | 0.7928 |
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- | 0.4392 | 1.84 | 600 | 0.4168 | 0.8649 |
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- | 0.3589 | 2.15 | 700 | 0.5549 | 0.8066 |
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- | 0.4259 | 2.45 | 800 | 0.4678 | 0.8396 |
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- | 0.3705 | 2.76 | 900 | 0.4542 | 0.8396 |
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- | 0.4609 | 3.07 | 1000 | 0.4723 | 0.8411 |
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- | 0.2082 | 3.37 | 1100 | 0.3631 | 0.8803 |
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- | 0.4583 | 3.68 | 1200 | 0.3835 | 0.8688 |
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- | 0.2218 | 3.99 | 1300 | 0.3913 | 0.8772 |
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- | 0.3716 | 4.29 | 1400 | 0.3858 | 0.8818 |
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- | 0.3675 | 4.6 | 1500 | 0.3849 | 0.8734 |
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- | 0.2602 | 4.91 | 1600 | 0.4080 | 0.8734 |
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- | 0.2091 | 5.21 | 1700 | 0.3767 | 0.8818 |
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- | 0.2071 | 5.52 | 1800 | 0.3883 | 0.8795 |
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- | 0.2426 | 5.83 | 1900 | 0.3557 | 0.8856 |
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- | 0.2917 | 6.13 | 2000 | 0.3550 | 0.8872 |
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- | 0.1417 | 6.44 | 2100 | 0.2918 | 0.9110 |
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- | 0.237 | 6.75 | 2200 | 0.3785 | 0.8864 |
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- | 0.1372 | 7.06 | 2300 | 0.3106 | 0.9025 |
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- | 0.161 | 7.36 | 2400 | 0.3809 | 0.8841 |
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- | 0.2354 | 7.67 | 2500 | 0.3739 | 0.8949 |
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- | 0.2489 | 7.98 | 2600 | 0.3442 | 0.8941 |
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- | 0.1962 | 8.28 | 2700 | 0.2875 | 0.9125 |
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- | 0.3157 | 8.59 | 2800 | 0.2959 | 0.9163 |
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- | 0.1204 | 8.9 | 2900 | 0.3017 | 0.9087 |
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- | 0.1272 | 9.2 | 3000 | 0.3380 | 0.9071 |
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- | 0.1768 | 9.51 | 3100 | 0.3611 | 0.9033 |
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- | 0.2211 | 9.82 | 3200 | 0.2704 | 0.9210 |
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- | 0.1213 | 10.12 | 3300 | 0.2813 | 0.9240 |
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- | 0.0432 | 10.43 | 3400 | 0.2956 | 0.9179 |
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- | 0.1152 | 10.74 | 3500 | 0.3256 | 0.9094 |
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- | 0.178 | 11.04 | 3600 | 0.3470 | 0.9094 |
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- | 0.1427 | 11.35 | 3700 | 0.3221 | 0.9079 |
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- | 0.1046 | 11.66 | 3800 | 0.2559 | 0.9286 |
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- | 0.1029 | 11.96 | 3900 | 0.2848 | 0.9202 |
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- | 0.0459 | 12.27 | 4000 | 0.3051 | 0.9156 |
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- | 0.1063 | 12.58 | 4100 | 0.2825 | 0.9225 |
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- | 0.0974 | 12.88 | 4200 | 0.3168 | 0.9233 |
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- | 0.0923 | 13.19 | 4300 | 0.3134 | 0.9194 |
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- | 0.0736 | 13.5 | 4400 | 0.2480 | 0.9325 |
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- | 0.0783 | 13.8 | 4500 | 0.2872 | 0.9202 |
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- | 0.1444 | 14.11 | 4600 | 0.3011 | 0.9225 |
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- | 0.1507 | 14.42 | 4700 | 0.2794 | 0.9271 |
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- | 0.1318 | 14.72 | 4800 | 0.2625 | 0.9271 |
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- | 0.0931 | 15.03 | 4900 | 0.2914 | 0.9279 |
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- | 0.074 | 15.34 | 5000 | 0.2826 | 0.9248 |
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- | 0.1306 | 15.64 | 5100 | 0.2836 | 0.9240 |
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- | 0.0856 | 15.95 | 5200 | 0.2966 | 0.9171 |
 
 
 
 
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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: 0.9429097605893186
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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
 
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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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  - 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