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carolinetfls
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plant-seedlings-vit-freeze-0-6-aug-3-all-train-2

Image Classification
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vit
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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: 0.949048496009822
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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 [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1846
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- - Accuracy: 0.9490
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  ## Model description
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@@ -57,102 +57,19 @@ 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: 22
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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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- | 0.6118 | 0.25 | 100 | 0.6427 | 0.7901 |
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- | 0.5478 | 0.49 | 200 | 0.5610 | 0.8232 |
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- | 0.3263 | 0.74 | 300 | 0.4523 | 0.8508 |
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- | 0.3938 | 0.98 | 400 | 0.3913 | 0.8649 |
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- | 0.3764 | 1.23 | 500 | 0.4459 | 0.8539 |
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- | 0.422 | 1.47 | 600 | 0.3761 | 0.8711 |
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- | 0.491 | 1.72 | 700 | 0.3525 | 0.8729 |
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- | 0.361 | 1.97 | 800 | 0.3738 | 0.8699 |
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- | 0.2441 | 2.21 | 900 | 0.3580 | 0.8828 |
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- | 0.4054 | 2.46 | 1000 | 0.4232 | 0.8692 |
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- | 0.3191 | 2.7 | 1100 | 0.2954 | 0.8969 |
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- | 0.343 | 2.95 | 1200 | 0.3528 | 0.8785 |
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- | 0.1623 | 3.19 | 1300 | 0.2624 | 0.9122 |
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- | 0.3418 | 3.44 | 1400 | 0.4062 | 0.8686 |
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- | 0.2535 | 3.69 | 1500 | 0.3043 | 0.8975 |
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- | 0.3356 | 3.93 | 1600 | 0.2746 | 0.9104 |
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- | 0.2092 | 4.18 | 1700 | 0.3080 | 0.9048 |
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- | 0.2423 | 4.42 | 1800 | 0.2958 | 0.9018 |
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- | 0.3758 | 4.67 | 1900 | 0.2949 | 0.9055 |
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- | 0.3434 | 4.91 | 2000 | 0.2647 | 0.9251 |
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- | 0.1809 | 5.16 | 2100 | 0.3192 | 0.9036 |
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- | 0.1617 | 5.41 | 2200 | 0.3036 | 0.8975 |
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- | 0.3044 | 5.65 | 2300 | 0.3053 | 0.8956 |
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- | 0.1709 | 5.9 | 2400 | 0.3879 | 0.8846 |
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- | 0.2963 | 6.14 | 2500 | 0.3243 | 0.8932 |
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- | 0.2314 | 6.39 | 2600 | 0.2632 | 0.9147 |
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- | 0.1128 | 6.63 | 2700 | 0.1934 | 0.9374 |
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- | 0.3211 | 6.88 | 2800 | 0.3639 | 0.8901 |
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- | 0.1108 | 7.13 | 2900 | 0.2748 | 0.9116 |
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- | 0.1128 | 7.37 | 3000 | 0.3050 | 0.9091 |
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- | 0.1648 | 7.62 | 3100 | 0.2830 | 0.9134 |
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- | 0.0887 | 7.86 | 3200 | 0.2707 | 0.9134 |
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- | 0.198 | 8.11 | 3300 | 0.2978 | 0.9116 |
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- | 0.1902 | 8.35 | 3400 | 0.2946 | 0.9042 |
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- | 0.1294 | 8.6 | 3500 | 0.2440 | 0.9227 |
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- | 0.2045 | 8.85 | 3600 | 0.2637 | 0.9104 |
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- | 0.2953 | 9.09 | 3700 | 0.2741 | 0.9141 |
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- | 0.2298 | 9.34 | 3800 | 0.2652 | 0.9177 |
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- | 0.2703 | 9.58 | 3900 | 0.2832 | 0.9091 |
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- | 0.261 | 9.83 | 4000 | 0.2521 | 0.9239 |
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- | 0.1135 | 10.07 | 4100 | 0.2647 | 0.9227 |
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- | 0.2153 | 10.32 | 4200 | 0.2623 | 0.9165 |
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- | 0.2826 | 10.57 | 4300 | 0.2619 | 0.9134 |
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- | 0.14 | 10.81 | 4400 | 0.2275 | 0.9300 |
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- | 0.1469 | 11.06 | 4500 | 0.2015 | 0.9282 |
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- | 0.1961 | 11.3 | 4600 | 0.2150 | 0.9269 |
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- | 0.1918 | 11.55 | 4700 | 0.2377 | 0.9288 |
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- | 0.2371 | 11.79 | 4800 | 0.2622 | 0.9184 |
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- | 0.0774 | 12.04 | 4900 | 0.2443 | 0.9239 |
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- | 0.136 | 12.29 | 5000 | 0.2577 | 0.9196 |
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- | 0.2154 | 12.53 | 5100 | 0.2278 | 0.9300 |
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- | 0.0926 | 12.78 | 5200 | 0.2209 | 0.9349 |
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- | 0.16 | 13.02 | 5300 | 0.2616 | 0.9196 |
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- | 0.0983 | 13.27 | 5400 | 0.2337 | 0.9276 |
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- | 0.1474 | 13.51 | 5500 | 0.2231 | 0.9355 |
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- | 0.1653 | 13.76 | 5600 | 0.2356 | 0.9245 |
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- | 0.099 | 14.0 | 5700 | 0.1976 | 0.9417 |
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- | 0.1248 | 14.25 | 5800 | 0.2684 | 0.9257 |
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- | 0.1565 | 14.5 | 5900 | 0.2197 | 0.9294 |
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- | 0.1752 | 14.74 | 6000 | 0.2312 | 0.9368 |
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- | 0.1962 | 14.99 | 6100 | 0.1968 | 0.9398 |
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- | 0.1373 | 15.23 | 6200 | 0.1925 | 0.9435 |
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- | 0.1003 | 15.48 | 6300 | 0.2182 | 0.9325 |
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- | 0.0511 | 15.72 | 6400 | 0.1993 | 0.9454 |
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- | 0.0401 | 15.97 | 6500 | 0.1941 | 0.9417 |
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- | 0.1051 | 16.22 | 6600 | 0.2161 | 0.9349 |
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- | 0.0593 | 16.46 | 6700 | 0.1940 | 0.9423 |
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- | 0.1215 | 16.71 | 6800 | 0.2579 | 0.9269 |
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- | 0.0568 | 16.95 | 6900 | 0.1968 | 0.9423 |
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- | 0.087 | 17.2 | 7000 | 0.1827 | 0.9441 |
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- | 0.0666 | 17.44 | 7100 | 0.2130 | 0.9454 |
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- | 0.0971 | 17.69 | 7200 | 0.2082 | 0.9398 |
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- | 0.1444 | 17.94 | 7300 | 0.2233 | 0.9337 |
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- | 0.0687 | 18.18 | 7400 | 0.2127 | 0.9429 |
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- | 0.0586 | 18.43 | 7500 | 0.2116 | 0.9380 |
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- | 0.0501 | 18.67 | 7600 | 0.2089 | 0.9441 |
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- | 0.0849 | 18.92 | 7700 | 0.1946 | 0.9490 |
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- | 0.0702 | 19.16 | 7800 | 0.2154 | 0.9454 |
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- | 0.0542 | 19.41 | 7900 | 0.1922 | 0.9478 |
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- | 0.0617 | 19.66 | 8000 | 0.2004 | 0.9423 |
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- | 0.061 | 19.9 | 8100 | 0.1923 | 0.9466 |
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- | 0.145 | 20.15 | 8200 | 0.1738 | 0.9503 |
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- | 0.0443 | 20.39 | 8300 | 0.2079 | 0.9429 |
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- | 0.0462 | 20.64 | 8400 | 0.1900 | 0.9478 |
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- | 0.0642 | 20.88 | 8500 | 0.1792 | 0.9460 |
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- | 0.0715 | 21.13 | 8600 | 0.1926 | 0.9466 |
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- | 0.0606 | 21.38 | 8700 | 0.1596 | 0.9527 |
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- | 0.0677 | 21.62 | 8800 | 0.2054 | 0.9454 |
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- | 0.0807 | 21.87 | 8900 | 0.1846 | 0.9490 |
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  ### Framework versions
 
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  metrics:
22
  - name: Accuracy
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  type: accuracy
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+ value: 0.9502762430939227
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  ---
26
 
27
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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32
  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
33
  It achieves the following results on the evaluation set:
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+ - Loss: 0.1475
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+ - Accuracy: 0.9503
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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: 1
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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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+ | 0.256 | 0.16 | 100 | 0.2983 | 0.9153 |
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+ | 0.145 | 0.31 | 200 | 0.1943 | 0.9392 |
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+ | 0.2502 | 0.47 | 300 | 0.2294 | 0.9227 |
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+ | 0.0876 | 0.63 | 400 | 0.1904 | 0.9355 |
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+ | 0.1218 | 0.79 | 500 | 0.1430 | 0.9540 |
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+ | 0.0952 | 0.94 | 600 | 0.1475 | 0.9503 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions