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cvt-21-384-22k-finetuned-LeafBack

Image Classification
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cvt
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  1. README.md +66 -36
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.961038961038961
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/cvt-21-384-22k](https://huggingface.co/microsoft/cvt-21-384-22k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1357
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- - Accuracy: 0.9610
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  ## Model description
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@@ -53,47 +53,77 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 40
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- - eval_batch_size: 16
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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: 30
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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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- | No log | 1.0 | 16 | 0.4915 | 0.7662 |
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- | No log | 2.0 | 32 | 0.4215 | 0.7922 |
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- | No log | 3.0 | 48 | 0.3265 | 0.8831 |
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- | No log | 4.0 | 64 | 0.2275 | 0.8961 |
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- | No log | 5.0 | 80 | 0.2464 | 0.8571 |
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- | No log | 6.0 | 96 | 0.2023 | 0.9091 |
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- | No log | 7.0 | 112 | 0.2282 | 0.8961 |
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- | No log | 8.0 | 128 | 0.1933 | 0.9091 |
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- | No log | 9.0 | 144 | 0.2563 | 0.8701 |
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- | No log | 10.0 | 160 | 0.1911 | 0.9091 |
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- | No log | 11.0 | 176 | 0.2113 | 0.9351 |
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- | No log | 12.0 | 192 | 0.1680 | 0.9221 |
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- | No log | 13.0 | 208 | 0.2042 | 0.9351 |
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- | No log | 14.0 | 224 | 0.1466 | 0.9221 |
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- | No log | 15.0 | 240 | 0.1277 | 0.9351 |
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- | No log | 16.0 | 256 | 0.1558 | 0.9351 |
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- | No log | 17.0 | 272 | 0.1407 | 0.9221 |
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- | No log | 18.0 | 288 | 0.1538 | 0.9351 |
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- | No log | 19.0 | 304 | 0.1735 | 0.9221 |
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- | No log | 20.0 | 320 | 0.1640 | 0.9481 |
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- | No log | 21.0 | 336 | 0.1774 | 0.9221 |
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- | No log | 22.0 | 352 | 0.1369 | 0.9481 |
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- | No log | 23.0 | 368 | 0.1503 | 0.9481 |
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- | No log | 24.0 | 384 | 0.2836 | 0.9221 |
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- | No log | 25.0 | 400 | 0.1777 | 0.9221 |
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- | No log | 26.0 | 416 | 0.1820 | 0.9351 |
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- | No log | 27.0 | 432 | 0.1260 | 0.9481 |
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- | No log | 28.0 | 448 | 0.1539 | 0.9221 |
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- | No log | 29.0 | 464 | 0.1622 | 0.9481 |
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- | No log | 30.0 | 480 | 0.1357 | 0.9610 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.974025974025974
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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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  This model is a fine-tuned version of [microsoft/cvt-21-384-22k](https://huggingface.co/microsoft/cvt-21-384-22k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0832
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+ - Accuracy: 0.9740
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 100
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+ - eval_batch_size: 50
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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: 60
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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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+ | No log | 1.0 | 7 | 0.6551 | 0.6364 |
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+ | No log | 2.0 | 14 | 0.5215 | 0.7532 |
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+ | No log | 3.0 | 21 | 0.4506 | 0.7922 |
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+ | No log | 4.0 | 28 | 0.4032 | 0.8182 |
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+ | No log | 5.0 | 35 | 0.3416 | 0.8442 |
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+ | No log | 6.0 | 42 | 0.2768 | 0.9091 |
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+ | No log | 7.0 | 49 | 0.3287 | 0.8571 |
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+ | No log | 8.0 | 56 | 0.2419 | 0.9091 |
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+ | No log | 9.0 | 63 | 0.2510 | 0.9091 |
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+ | No log | 10.0 | 70 | 0.2100 | 0.8961 |
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+ | No log | 11.0 | 77 | 0.2691 | 0.8961 |
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+ | No log | 12.0 | 84 | 0.2717 | 0.8961 |
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+ | No log | 13.0 | 91 | 0.1412 | 0.9481 |
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+ | No log | 14.0 | 98 | 0.1541 | 0.9351 |
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+ | No log | 15.0 | 105 | 0.2572 | 0.8701 |
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+ | No log | 16.0 | 112 | 0.1843 | 0.8961 |
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+ | No log | 17.0 | 119 | 0.1376 | 0.9481 |
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+ | No log | 18.0 | 126 | 0.1632 | 0.9351 |
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+ | No log | 19.0 | 133 | 0.1901 | 0.9091 |
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+ | No log | 20.0 | 140 | 0.1495 | 0.9221 |
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+ | No log | 21.0 | 147 | 0.1710 | 0.9221 |
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+ | No log | 22.0 | 154 | 0.1906 | 0.9351 |
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+ | No log | 23.0 | 161 | 0.1192 | 0.9610 |
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+ | No log | 24.0 | 168 | 0.2000 | 0.9091 |
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+ | No log | 25.0 | 175 | 0.1611 | 0.9221 |
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+ | No log | 26.0 | 182 | 0.1074 | 0.9610 |
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+ | No log | 27.0 | 189 | 0.1080 | 0.9481 |
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+ | No log | 28.0 | 196 | 0.1405 | 0.9351 |
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+ | No log | 29.0 | 203 | 0.0890 | 0.9610 |
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+ | No log | 30.0 | 210 | 0.0777 | 0.9740 |
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+ | No log | 31.0 | 217 | 0.0636 | 0.9740 |
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+ | No log | 32.0 | 224 | 0.0709 | 0.9740 |
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+ | No log | 33.0 | 231 | 0.0671 | 0.9740 |
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+ | No log | 34.0 | 238 | 0.1055 | 0.9610 |
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+ | No log | 35.0 | 245 | 0.1366 | 0.9610 |
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+ | No log | 36.0 | 252 | 0.1070 | 0.9610 |
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+ | No log | 37.0 | 259 | 0.0890 | 0.9481 |
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+ | No log | 38.0 | 266 | 0.0876 | 0.9481 |
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+ | No log | 39.0 | 273 | 0.0838 | 0.9481 |
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+ | No log | 40.0 | 280 | 0.1502 | 0.9610 |
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+ | No log | 41.0 | 287 | 0.1241 | 0.9610 |
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+ | No log | 42.0 | 294 | 0.1043 | 0.9740 |
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+ | No log | 43.0 | 301 | 0.1126 | 0.9740 |
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+ | No log | 44.0 | 308 | 0.1283 | 0.9610 |
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+ | No log | 45.0 | 315 | 0.1123 | 0.9610 |
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+ | No log | 46.0 | 322 | 0.1198 | 0.9610 |
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+ | No log | 47.0 | 329 | 0.1434 | 0.9610 |
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+ | No log | 48.0 | 336 | 0.1340 | 0.9610 |
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+ | No log | 49.0 | 343 | 0.1073 | 0.9610 |
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+ | No log | 50.0 | 350 | 0.1112 | 0.9610 |
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+ | No log | 51.0 | 357 | 0.1035 | 0.9610 |
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+ | No log | 52.0 | 364 | 0.0972 | 0.9610 |
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+ | No log | 53.0 | 371 | 0.0960 | 0.9610 |
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+ | No log | 54.0 | 378 | 0.0881 | 0.9610 |
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+ | No log | 55.0 | 385 | 0.0797 | 0.9610 |
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+ | No log | 56.0 | 392 | 0.0773 | 0.9740 |
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+ | No log | 57.0 | 399 | 0.0736 | 0.9740 |
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+ | No log | 58.0 | 406 | 0.0794 | 0.9740 |
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+ | No log | 59.0 | 413 | 0.0841 | 0.9740 |
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+ | No log | 60.0 | 420 | 0.0832 | 0.9740 |
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  ### Framework versions
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