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Kushagra07
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deit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-14687

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End of training

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  1. README.md +62 -32
  2. model.safetensors +1 -1
  3. runs/Apr26_05-24-43_76c83c13b5a4/events.out.tfevents.1714109084.76c83c13b5a4.5796.0 +2 -2
  4. runs/Apr26_05-24-43_76c83c13b5a4/events.out.tfevents.1714131252.76c83c13b5a4.5796.1 +3 -0
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8458918688803746
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  - name: Recall
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  type: recall
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- value: 0.8458918688803746
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  - name: F1
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  type: f1
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- value: 0.843745130911636
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  - name: Precision
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  type: precision
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- value: 0.8521498018011563
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-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: 0.3635
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- - Accuracy: 0.8459
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- - Recall: 0.8459
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- - F1: 0.8437
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- - Precision: 0.8521
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  ## Model description
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@@ -76,32 +76,62 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 20
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
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- |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
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- | 0.6153 | 0.9974 | 293 | 0.6607 | 0.7739 | 0.7739 | 0.7506 | 0.7444 |
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- | 0.5075 | 1.9983 | 587 | 0.5850 | 0.7927 | 0.7927 | 0.7767 | 0.8081 |
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- | 0.5278 | 2.9991 | 881 | 0.4721 | 0.8199 | 0.8199 | 0.8170 | 0.8301 |
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- | 0.445 | 4.0 | 1175 | 0.4495 | 0.8186 | 0.8186 | 0.8136 | 0.8192 |
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- | 0.3781 | 4.9974 | 1468 | 0.4018 | 0.8263 | 0.8263 | 0.8249 | 0.8326 |
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- | 0.4025 | 5.9983 | 1762 | 0.4356 | 0.8221 | 0.8221 | 0.8195 | 0.8245 |
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- | 0.3409 | 6.9991 | 2056 | 0.3876 | 0.8267 | 0.8267 | 0.8248 | 0.8330 |
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- | 0.3181 | 8.0 | 2350 | 0.3849 | 0.8391 | 0.8391 | 0.8372 | 0.8436 |
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- | 0.3042 | 8.9974 | 2643 | 0.3850 | 0.8280 | 0.8280 | 0.8285 | 0.8347 |
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- | 0.2475 | 9.9983 | 2937 | 0.3624 | 0.8493 | 0.8493 | 0.8475 | 0.8571 |
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- | 0.2339 | 10.9991 | 3231 | 0.3865 | 0.8318 | 0.8318 | 0.8281 | 0.8307 |
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- | 0.2455 | 12.0 | 3525 | 0.3337 | 0.8387 | 0.8387 | 0.8371 | 0.8433 |
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- | 0.2127 | 12.9974 | 3818 | 0.3685 | 0.8306 | 0.8306 | 0.8281 | 0.8356 |
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- | 0.2288 | 13.9983 | 4112 | 0.3545 | 0.8370 | 0.8370 | 0.8352 | 0.8385 |
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- | 0.2534 | 14.9991 | 4406 | 0.3587 | 0.8429 | 0.8429 | 0.8398 | 0.8537 |
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- | 0.1911 | 16.0 | 4700 | 0.3573 | 0.8387 | 0.8387 | 0.8367 | 0.8396 |
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- | 0.2118 | 16.9974 | 4993 | 0.3676 | 0.8370 | 0.8370 | 0.8356 | 0.8415 |
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- | 0.22 | 17.9983 | 5287 | 0.3469 | 0.8357 | 0.8357 | 0.8326 | 0.8412 |
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- | 0.1938 | 18.9991 | 5581 | 0.3512 | 0.8365 | 0.8365 | 0.8343 | 0.8363 |
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- | 0.1816 | 19.9489 | 5860 | 0.3323 | 0.8463 | 0.8463 | 0.8449 | 0.8476 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.8309919114516816
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  - name: Recall
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  type: recall
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+ value: 0.8309919114516816
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  - name: F1
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  type: f1
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+ value: 0.8298114215031374
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  - name: Precision
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  type: precision
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+ value: 0.8359531770567361
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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 [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
46
  It achieves the following results on the evaluation set:
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+ - Loss: 0.3950
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+ - Accuracy: 0.8310
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+ - Recall: 0.8310
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+ - F1: 0.8298
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+ - Precision: 0.8360
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  ## Model description
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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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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
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+ |:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
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+ | 0.8293 | 0.9974 | 293 | 0.7793 | 0.7680 | 0.7680 | 0.7403 | 0.7277 |
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+ | 0.5921 | 1.9983 | 587 | 0.5663 | 0.7940 | 0.7940 | 0.7843 | 0.7839 |
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+ | 0.4308 | 2.9991 | 881 | 0.4589 | 0.8208 | 0.8208 | 0.8161 | 0.8213 |
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+ | 0.3999 | 4.0 | 1175 | 0.4772 | 0.8263 | 0.8263 | 0.8216 | 0.8337 |
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+ | 0.4801 | 4.9974 | 1468 | 0.4258 | 0.8378 | 0.8378 | 0.8306 | 0.8463 |
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+ | 0.4201 | 5.9983 | 1762 | 0.4120 | 0.8246 | 0.8246 | 0.8213 | 0.8394 |
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+ | 0.3233 | 6.9991 | 2056 | 0.3989 | 0.8306 | 0.8306 | 0.8268 | 0.8445 |
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+ | 0.3954 | 8.0 | 2350 | 0.3794 | 0.8365 | 0.8365 | 0.8341 | 0.8383 |
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+ | 0.2835 | 8.9974 | 2643 | 0.4438 | 0.8318 | 0.8318 | 0.8278 | 0.8434 |
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+ | 0.2913 | 9.9983 | 2937 | 0.3799 | 0.8416 | 0.8416 | 0.8404 | 0.8451 |
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+ | 0.3261 | 10.9991 | 3231 | 0.3694 | 0.8297 | 0.8297 | 0.8272 | 0.8306 |
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+ | 0.3299 | 12.0 | 3525 | 0.3637 | 0.8442 | 0.8442 | 0.8425 | 0.8529 |
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+ | 0.3273 | 12.9974 | 3818 | 0.3649 | 0.8421 | 0.8421 | 0.8411 | 0.8482 |
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+ | 0.2596 | 13.9983 | 4112 | 0.4152 | 0.8259 | 0.8259 | 0.8213 | 0.8281 |
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+ | 0.2813 | 14.9991 | 4406 | 0.3578 | 0.8429 | 0.8429 | 0.8409 | 0.8491 |
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+ | 0.2406 | 16.0 | 4700 | 0.3813 | 0.8323 | 0.8323 | 0.8285 | 0.8362 |
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+ | 0.2263 | 16.9974 | 4993 | 0.3808 | 0.8318 | 0.8318 | 0.8275 | 0.8377 |
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+ | 0.3192 | 17.9983 | 5287 | 0.3625 | 0.8412 | 0.8412 | 0.8372 | 0.8484 |
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+ | 0.2003 | 18.9991 | 5581 | 0.3549 | 0.8438 | 0.8438 | 0.8430 | 0.8462 |
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+ | 0.2431 | 20.0 | 5875 | 0.3620 | 0.8425 | 0.8425 | 0.8408 | 0.8467 |
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+ | 0.2654 | 20.9974 | 6168 | 0.3865 | 0.8340 | 0.8340 | 0.8320 | 0.8338 |
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+ | 0.2989 | 21.9983 | 6462 | 0.3632 | 0.8463 | 0.8463 | 0.8449 | 0.8498 |
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+ | 0.2403 | 22.9991 | 6756 | 0.3824 | 0.8301 | 0.8301 | 0.8267 | 0.8304 |
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+ | 0.2393 | 24.0 | 7050 | 0.3607 | 0.8489 | 0.8489 | 0.8473 | 0.8519 |
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+ | 0.2305 | 24.9974 | 7343 | 0.3758 | 0.8365 | 0.8365 | 0.8350 | 0.8401 |
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+ | 0.2654 | 25.9983 | 7637 | 0.3652 | 0.8421 | 0.8421 | 0.8392 | 0.8415 |
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+ | 0.176 | 26.9991 | 7931 | 0.3929 | 0.8306 | 0.8306 | 0.8289 | 0.8385 |
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+ | 0.1893 | 28.0 | 8225 | 0.3794 | 0.8374 | 0.8374 | 0.8365 | 0.8404 |
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+ | 0.2652 | 28.9974 | 8518 | 0.3995 | 0.8387 | 0.8387 | 0.8372 | 0.8423 |
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+ | 0.2029 | 29.9983 | 8812 | 0.3981 | 0.8433 | 0.8433 | 0.8411 | 0.8430 |
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+ | 0.1799 | 30.9991 | 9106 | 0.3554 | 0.8352 | 0.8352 | 0.8340 | 0.8368 |
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+ | 0.2002 | 32.0 | 9400 | 0.3618 | 0.8310 | 0.8310 | 0.8300 | 0.8322 |
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+ | 0.1525 | 32.9974 | 9693 | 0.3629 | 0.8348 | 0.8348 | 0.8343 | 0.8381 |
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+ | 0.1663 | 33.9983 | 9987 | 0.3664 | 0.8425 | 0.8425 | 0.8410 | 0.8427 |
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+ | 0.1728 | 34.9991 | 10281 | 0.3928 | 0.8429 | 0.8429 | 0.8415 | 0.8468 |
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+ | 0.2252 | 36.0 | 10575 | 0.3842 | 0.8421 | 0.8421 | 0.8420 | 0.8443 |
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+ | 0.1554 | 36.9974 | 10868 | 0.3889 | 0.8301 | 0.8301 | 0.8294 | 0.8349 |
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+ | 0.2179 | 37.9983 | 11162 | 0.3775 | 0.8399 | 0.8399 | 0.8389 | 0.8429 |
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+ | 0.1771 | 38.9991 | 11456 | 0.3906 | 0.8306 | 0.8306 | 0.8291 | 0.8324 |
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+ | 0.2167 | 40.0 | 11750 | 0.3870 | 0.8404 | 0.8404 | 0.8382 | 0.8456 |
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+ | 0.1563 | 40.9974 | 12043 | 0.3779 | 0.8284 | 0.8284 | 0.8277 | 0.8288 |
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+ | 0.1419 | 41.9983 | 12337 | 0.4049 | 0.8340 | 0.8340 | 0.8327 | 0.8360 |
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+ | 0.2083 | 42.9991 | 12631 | 0.3800 | 0.8421 | 0.8421 | 0.8410 | 0.8427 |
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+ | 0.2185 | 44.0 | 12925 | 0.3964 | 0.8433 | 0.8433 | 0.8422 | 0.8441 |
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+ | 0.1989 | 44.9974 | 13218 | 0.3870 | 0.8340 | 0.8340 | 0.8339 | 0.8357 |
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+ | 0.1731 | 45.9983 | 13512 | 0.4206 | 0.8340 | 0.8340 | 0.8335 | 0.8357 |
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+ | 0.1831 | 46.9991 | 13806 | 0.4027 | 0.8429 | 0.8429 | 0.8422 | 0.8439 |
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+ | 0.1471 | 48.0 | 14100 | 0.4016 | 0.8318 | 0.8318 | 0.8307 | 0.8320 |
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+ | 0.1879 | 48.9974 | 14393 | 0.3877 | 0.8438 | 0.8438 | 0.8441 | 0.8468 |
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+ | 0.1775 | 49.8723 | 14650 | 0.3984 | 0.8421 | 0.8421 | 0.8408 | 0.8428 |
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  ### Framework versions
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