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Raihan004
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Action_model

Image Classification
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  1. README.md +31 -68
  2. model.safetensors +1 -1
  3. runs/May01_07-50-39_5c48e9aaec37/events.out.tfevents.1714569453.5c48e9aaec37.34.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -2,7 +2,6 @@
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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- - image-classification
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  - generated_from_trainer
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  datasets:
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  - imagefolder
@@ -15,7 +14,7 @@ model-index:
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  name: Image Classification
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  type: image-classification
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  dataset:
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- name: action_class
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  type: imagefolder
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  config: default
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  split: train
@@ -23,7 +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: 0.7723809523809524
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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
@@ -31,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # Action_model
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- This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the action_class dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9484
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- - Accuracy: 0.7724
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  ## Model description
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@@ -66,68 +65,32 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.1624 | 0.16 | 100 | 1.0534 | 0.7638 |
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- | 0.2926 | 0.32 | 200 | 1.3484 | 0.6867 |
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- | 0.159 | 0.48 | 300 | 0.9484 | 0.7724 |
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- | 0.2145 | 0.64 | 400 | 1.0014 | 0.7476 |
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- | 0.1889 | 0.8 | 500 | 1.0321 | 0.7457 |
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- | 0.3064 | 0.96 | 600 | 1.0795 | 0.7314 |
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- | 0.2195 | 1.11 | 700 | 0.9886 | 0.7629 |
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- | 0.2982 | 1.27 | 800 | 1.0292 | 0.7590 |
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- | 0.2477 | 1.43 | 900 | 1.2391 | 0.7248 |
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- | 0.3076 | 1.59 | 1000 | 1.1326 | 0.7324 |
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- | 0.1863 | 1.75 | 1100 | 1.2596 | 0.7048 |
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- | 0.2577 | 1.91 | 1200 | 1.0649 | 0.7610 |
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- | 0.1491 | 2.07 | 1300 | 1.1044 | 0.7562 |
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- | 0.2635 | 2.23 | 1400 | 1.1965 | 0.7448 |
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- | 0.2597 | 2.39 | 1500 | 1.2241 | 0.7429 |
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- | 0.2468 | 2.55 | 1600 | 1.1452 | 0.7390 |
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- | 0.216 | 2.71 | 1700 | 1.2419 | 0.7276 |
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- | 0.1971 | 2.87 | 1800 | 1.1883 | 0.7362 |
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- | 0.2071 | 3.03 | 1900 | 1.4659 | 0.6952 |
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- | 0.1535 | 3.18 | 2000 | 1.0239 | 0.7724 |
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- | 0.1842 | 3.34 | 2100 | 1.1967 | 0.7390 |
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- | 0.2087 | 3.5 | 2200 | 1.1403 | 0.7467 |
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- | 0.1658 | 3.66 | 2300 | 1.2901 | 0.7343 |
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- | 0.1159 | 3.82 | 2400 | 1.1826 | 0.7438 |
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- | 0.1498 | 3.98 | 2500 | 1.2627 | 0.7419 |
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- | 0.135 | 4.14 | 2600 | 1.1383 | 0.76 |
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- | 0.1492 | 4.3 | 2700 | 1.2310 | 0.7343 |
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- | 0.0982 | 4.46 | 2800 | 1.4144 | 0.7105 |
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- | 0.1256 | 4.62 | 2900 | 1.3513 | 0.7171 |
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- | 0.1544 | 4.78 | 3000 | 1.4280 | 0.7019 |
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- | 0.0858 | 4.94 | 3100 | 1.2231 | 0.7429 |
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- | 0.1049 | 5.1 | 3200 | 1.2775 | 0.7352 |
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- | 0.1361 | 5.25 | 3300 | 1.2840 | 0.7429 |
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- | 0.1505 | 5.41 | 3400 | 1.3373 | 0.7390 |
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- | 0.1244 | 5.57 | 3500 | 1.2959 | 0.7438 |
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- | 0.1114 | 5.73 | 3600 | 1.3181 | 0.7381 |
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- | 0.0851 | 5.89 | 3700 | 1.3288 | 0.7457 |
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- | 0.0799 | 6.05 | 3800 | 1.1859 | 0.76 |
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- | 0.1331 | 6.21 | 3900 | 1.2544 | 0.7371 |
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- | 0.121 | 6.37 | 4000 | 1.2186 | 0.7533 |
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- | 0.1276 | 6.53 | 4100 | 1.2964 | 0.7324 |
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- | 0.1194 | 6.69 | 4200 | 1.1907 | 0.7590 |
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- | 0.1649 | 6.85 | 4300 | 1.4679 | 0.7105 |
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- | 0.0558 | 7.01 | 4400 | 1.2028 | 0.7533 |
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- | 0.0687 | 7.17 | 4500 | 1.3242 | 0.7381 |
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- | 0.1419 | 7.32 | 4600 | 1.2328 | 0.76 |
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- | 0.0901 | 7.48 | 4700 | 1.1861 | 0.7676 |
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- | 0.1181 | 7.64 | 4800 | 1.4031 | 0.7352 |
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- | 0.1272 | 7.8 | 4900 | 1.3608 | 0.7438 |
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- | 0.0979 | 7.96 | 5000 | 1.3098 | 0.7495 |
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- | 0.0805 | 8.12 | 5100 | 1.2445 | 0.7533 |
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- | 0.0354 | 8.28 | 5200 | 1.2345 | 0.7581 |
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- | 0.0499 | 8.44 | 5300 | 1.1776 | 0.7571 |
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- | 0.1046 | 8.6 | 5400 | 1.1939 | 0.76 |
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- | 0.0912 | 8.76 | 5500 | 1.2373 | 0.7486 |
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- | 0.0589 | 8.92 | 5600 | 1.2165 | 0.7552 |
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- | 0.0829 | 9.08 | 5700 | 1.2684 | 0.7505 |
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- | 0.0897 | 9.24 | 5800 | 1.2467 | 0.7552 |
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- | 0.1114 | 9.39 | 5900 | 1.2303 | 0.7571 |
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- | 0.0712 | 9.55 | 6000 | 1.1997 | 0.7638 |
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- | 0.0621 | 9.71 | 6100 | 1.2094 | 0.7629 |
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- | 0.037 | 9.87 | 6200 | 1.2147 | 0.7619 |
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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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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  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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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8506151142355008
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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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  # Action_model
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
34
  It achieves the following results on the evaluation set:
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+ - Loss: 0.5271
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+ - Accuracy: 0.8506
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.2738 | 0.37 | 100 | 1.1306 | 0.7346 |
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+ | 0.9277 | 0.75 | 200 | 0.8469 | 0.7645 |
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+ | 0.9232 | 1.12 | 300 | 0.7195 | 0.7891 |
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+ | 0.7631 | 1.49 | 400 | 0.6575 | 0.8172 |
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+ | 0.6677 | 1.87 | 500 | 0.5807 | 0.8348 |
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+ | 0.5063 | 2.24 | 600 | 0.5663 | 0.8436 |
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+ | 0.5071 | 2.61 | 700 | 0.5481 | 0.8348 |
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+ | 0.579 | 2.99 | 800 | 0.5727 | 0.8260 |
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+ | 0.4298 | 3.36 | 900 | 0.5906 | 0.8278 |
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+ | 0.3999 | 3.73 | 1000 | 0.5693 | 0.8348 |
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+ | 0.414 | 4.1 | 1100 | 0.5115 | 0.8401 |
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+ | 0.408 | 4.48 | 1200 | 0.5281 | 0.8366 |
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+ | 0.2794 | 4.85 | 1300 | 0.5269 | 0.8453 |
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+ | 0.3881 | 5.22 | 1400 | 0.5246 | 0.8471 |
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+ | 0.3097 | 5.6 | 1500 | 0.4926 | 0.8524 |
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+ | 0.2272 | 5.97 | 1600 | 0.5248 | 0.8506 |
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+ | 0.2796 | 6.34 | 1700 | 0.5053 | 0.8612 |
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+ | 0.3279 | 6.72 | 1800 | 0.5019 | 0.8629 |
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+ | 0.2674 | 7.09 | 1900 | 0.4922 | 0.8541 |
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+ | 0.1587 | 7.46 | 2000 | 0.5443 | 0.8453 |
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+ | 0.1969 | 7.84 | 2100 | 0.4967 | 0.8453 |
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+ | 0.2137 | 8.21 | 2200 | 0.5276 | 0.8453 |
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+ | 0.1523 | 8.58 | 2300 | 0.5574 | 0.8401 |
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+ | 0.1046 | 8.96 | 2400 | 0.5302 | 0.8506 |
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+ | 0.2494 | 9.33 | 2500 | 0.5222 | 0.8594 |
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+ | 0.1924 | 9.7 | 2600 | 0.5271 | 0.8506 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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