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AV_classifier_resnet50

Image Classification
Transformers
TensorFlow
resnet
generated_from_keras_callback
Inference Endpoints
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End of training

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  1. README.md +103 -103
  2. tf_model.h5 +1 -1
README.md CHANGED
@@ -14,9 +14,9 @@ probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Train Loss: 0.7480
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- - Validation Loss: 1.9590
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- - Train Accuracy: 0.7
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  - Epoch: 99
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  ## Model description
@@ -43,106 +43,106 @@ The following hyperparameters were used during training:
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  | Train Loss | Validation Loss | Train Accuracy | Epoch |
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  |:----------:|:---------------:|:--------------:|:-----:|
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- | 8.2909 | 8.8519 | 0.0 | 0 |
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- | 8.1073 | 8.1722 | 0.0 | 1 |
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- | 8.0937 | 7.9716 | 0.0 | 2 |
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- | 7.8267 | 7.8585 | 0.0 | 3 |
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- | 7.5311 | 7.6686 | 0.0 | 4 |
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- | 7.5912 | 7.6775 | 0.0 | 5 |
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- | 7.3261 | 7.5158 | 0.0 | 6 |
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- | 7.1478 | 7.5385 | 0.0 | 7 |
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- | 7.0659 | 7.4539 | 0.0 | 8 |
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- | 6.8157 | 7.3734 | 0.0 | 9 |
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- | 6.8977 | 7.2370 | 0.0 | 10 |
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- | 6.6346 | 7.0331 | 0.0 | 11 |
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- | 6.5901 | 6.9245 | 0.0 | 12 |
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- | 6.4704 | 7.0762 | 0.0 | 13 |
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- | 6.3974 | 6.8628 | 0.0 | 14 |
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- | 6.0737 | 6.7022 | 0.0 | 15 |
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- | 5.9180 | 6.6911 | 0.0 | 16 |
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- | 5.6420 | 6.6420 | 0.0 | 17 |
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- | 5.8163 | 6.6039 | 0.0 | 18 |
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- | 5.5002 | 6.5215 | 0.0 | 19 |
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- | 5.3426 | 6.4144 | 0.0 | 20 |
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- | 5.4375 | 6.3628 | 0.05 | 21 |
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- | 5.1789 | 6.2723 | 0.05 | 22 |
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- | 5.1680 | 6.2523 | 0.05 | 23 |
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- | 5.1469 | 6.1031 | 0.05 | 24 |
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- | 4.9030 | 6.0026 | 0.0 | 25 |
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- | 4.7596 | 5.8046 | 0.15 | 26 |
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- | 4.8801 | 5.8023 | 0.1 | 27 |
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- | 4.6371 | 5.8529 | 0.1 | 28 |
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- | 4.4716 | 5.6872 | 0.1 | 29 |
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- | 4.5640 | 5.5713 | 0.1 | 30 |
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- | 4.2409 | 5.6985 | 0.05 | 31 |
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- | 4.3464 | 5.6036 | 0.1 | 32 |
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- | 4.0358 | 5.4160 | 0.1 | 33 |
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- | 3.7727 | 5.2536 | 0.15 | 34 |
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- | 3.8634 | 5.1261 | 0.2 | 35 |
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- | 3.7902 | 5.0305 | 0.2 | 36 |
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- | 3.5799 | 4.9175 | 0.25 | 37 |
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- | 3.6493 | 4.8794 | 0.25 | 38 |
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- | 3.3610 | 4.7168 | 0.2 | 39 |
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- | 3.3305 | 4.7768 | 0.2 | 40 |
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- | 3.2444 | 4.6929 | 0.25 | 41 |
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- | 3.3055 | 4.7278 | 0.2 | 42 |
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- | 3.0663 | 4.5155 | 0.2 | 43 |
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- | 2.9070 | 4.4748 | 0.2 | 44 |
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- | 3.0524 | 4.2340 | 0.2 | 45 |
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- | 2.8021 | 4.1962 | 0.25 | 46 |
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- | 2.7445 | 4.1974 | 0.25 | 47 |
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- | 2.6257 | 4.0621 | 0.25 | 48 |
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- | 2.4276 | 4.0370 | 0.3 | 49 |
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- | 2.5626 | 4.0561 | 0.2 | 50 |
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- | 2.4725 | 3.9501 | 0.3 | 51 |
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- | 2.1471 | 3.8683 | 0.35 | 52 |
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- | 2.2171 | 3.7830 | 0.3 | 53 |
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- | 2.0710 | 3.8210 | 0.3 | 54 |
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- | 1.9833 | 3.5905 | 0.35 | 55 |
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- | 2.0103 | 3.5331 | 0.4 | 56 |
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- | 1.7876 | 3.5856 | 0.4 | 57 |
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- | 1.9404 | 3.5545 | 0.3 | 58 |
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- | 1.8680 | 3.4422 | 0.4 | 59 |
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- | 1.9024 | 3.4521 | 0.45 | 60 |
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- | 1.7234 | 3.4420 | 0.45 | 61 |
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- | 1.9552 | 3.4011 | 0.4 | 62 |
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- | 1.6278 | 3.3911 | 0.35 | 63 |
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- | 1.3892 | 3.3011 | 0.35 | 64 |
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- | 1.4450 | 3.2232 | 0.45 | 65 |
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- | 1.4787 | 3.2376 | 0.4 | 66 |
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- | 1.3586 | 3.1092 | 0.5 | 67 |
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- | 1.5565 | 3.1247 | 0.45 | 68 |
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- | 1.3352 | 3.0486 | 0.45 | 69 |
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- | 1.4656 | 2.9821 | 0.55 | 70 |
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- | 1.4609 | 2.8628 | 0.5 | 71 |
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- | 1.3140 | 2.7668 | 0.55 | 72 |
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- | 1.2623 | 2.7777 | 0.55 | 73 |
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- | 1.1311 | 2.7987 | 0.55 | 74 |
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- | 1.3050 | 2.7233 | 0.6 | 75 |
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- | 1.1644 | 2.6816 | 0.6 | 76 |
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- | 1.0867 | 2.6325 | 0.6 | 77 |
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- | 1.0870 | 2.6182 | 0.6 | 78 |
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- | 1.0695 | 2.6422 | 0.6 | 79 |
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- | 1.0438 | 2.6493 | 0.6 | 80 |
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- | 1.0208 | 2.6355 | 0.6 | 81 |
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- | 0.9287 | 2.4896 | 0.65 | 82 |
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- | 1.0166 | 2.5370 | 0.6 | 83 |
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- | 0.7797 | 2.6378 | 0.6 | 84 |
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- | 0.7836 | 2.5321 | 0.65 | 85 |
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- | 0.9135 | 2.4290 | 0.55 | 86 |
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- | 0.9067 | 2.3287 | 0.65 | 87 |
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- | 0.8000 | 2.2374 | 0.65 | 88 |
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- | 0.8086 | 2.3477 | 0.65 | 89 |
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- | 0.8166 | 2.2292 | 0.65 | 90 |
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- | 1.0275 | 2.2574 | 0.65 | 91 |
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- | 0.8453 | 2.1617 | 0.65 | 92 |
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- | 0.6428 | 2.1317 | 0.7 | 93 |
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- | 0.7761 | 2.0171 | 0.7 | 94 |
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- | 0.7433 | 2.0812 | 0.75 | 95 |
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- | 0.7227 | 2.2041 | 0.65 | 96 |
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- | 0.6323 | 2.0665 | 0.7 | 97 |
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- | 0.6911 | 2.0789 | 0.65 | 98 |
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- | 0.7480 | 1.9590 | 0.7 | 99 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Train Loss: 0.8261
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+ - Validation Loss: 2.4425
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+ - Train Accuracy: 0.6
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  - Epoch: 99
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  ## Model description
 
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  | Train Loss | Validation Loss | Train Accuracy | Epoch |
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  |:----------:|:---------------:|:--------------:|:-----:|
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+ | 8.2326 | 8.1060 | 0.0 | 0 |
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+ | 8.3420 | 7.6394 | 0.05 | 1 |
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+ | 7.9643 | 7.5706 | 0.05 | 2 |
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+ | 7.9337 | 7.6265 | 0.05 | 3 |
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+ | 7.8018 | 7.7736 | 0.05 | 4 |
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+ | 7.8009 | 7.7905 | 0.05 | 5 |
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+ | 7.6369 | 7.6354 | 0.05 | 6 |
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+ | 7.4782 | 7.5608 | 0.05 | 7 |
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+ | 7.3655 | 7.6271 | 0.05 | 8 |
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+ | 7.2886 | 7.6028 | 0.0 | 9 |
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+ | 7.1145 | 7.5211 | 0.0 | 10 |
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+ | 7.1232 | 7.2993 | 0.0 | 11 |
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+ | 6.8393 | 7.2079 | 0.0 | 12 |
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+ | 6.8202 | 7.2143 | 0.0 | 13 |
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+ | 6.7180 | 7.1236 | 0.05 | 14 |
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+ | 6.7318 | 7.1061 | 0.0 | 15 |
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+ | 6.4563 | 6.9758 | 0.05 | 16 |
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+ | 6.3765 | 6.9413 | 0.05 | 17 |
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+ | 6.1791 | 6.8315 | 0.05 | 18 |
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+ | 6.1946 | 6.7703 | 0.05 | 19 |
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+ | 5.8448 | 6.7431 | 0.1 | 20 |
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+ | 5.8514 | 6.6876 | 0.1 | 21 |
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+ | 5.8200 | 6.6353 | 0.05 | 22 |
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+ | 5.8323 | 6.5814 | 0.05 | 23 |
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+ | 5.5553 | 6.4306 | 0.05 | 24 |
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+ | 5.4999 | 6.4455 | 0.05 | 25 |
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+ | 5.4370 | 6.3026 | 0.05 | 26 |
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+ | 5.2288 | 6.0093 | 0.1 | 27 |
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+ | 5.2173 | 6.0593 | 0.05 | 28 |
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+ | 5.2280 | 6.0598 | 0.05 | 29 |
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+ | 5.0484 | 5.9769 | 0.05 | 30 |
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+ | 4.8703 | 5.8336 | 0.05 | 31 |
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+ | 4.9881 | 5.7711 | 0.1 | 32 |
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+ | 4.5905 | 5.6685 | 0.1 | 33 |
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+ | 4.7240 | 5.6156 | 0.15 | 34 |
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+ | 4.5095 | 5.4680 | 0.15 | 35 |
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+ | 4.2225 | 5.3962 | 0.15 | 36 |
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+ | 4.3615 | 5.3290 | 0.2 | 37 |
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+ | 4.1862 | 5.3602 | 0.15 | 38 |
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+ | 3.9455 | 5.2635 | 0.15 | 39 |
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+ | 3.9737 | 5.2337 | 0.15 | 40 |
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+ | 4.0922 | 5.1268 | 0.15 | 41 |
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+ | 3.6042 | 4.9972 | 0.2 | 42 |
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+ | 3.7219 | 4.8787 | 0.15 | 43 |
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+ | 3.5563 | 4.9075 | 0.2 | 44 |
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+ | 3.5897 | 4.9157 | 0.25 | 45 |
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+ | 3.5769 | 4.7936 | 0.25 | 46 |
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+ | 3.6225 | 4.8689 | 0.2 | 47 |
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+ | 3.4568 | 4.8767 | 0.2 | 48 |
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+ | 3.1431 | 4.7520 | 0.25 | 49 |
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+ | 3.0607 | 4.5815 | 0.3 | 50 |
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+ | 2.8904 | 4.5007 | 0.2 | 51 |
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+ | 2.8308 | 4.5054 | 0.25 | 52 |
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+ | 2.8136 | 4.2745 | 0.25 | 53 |
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+ | 2.6192 | 4.3300 | 0.2 | 54 |
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+ | 2.5308 | 4.3180 | 0.2 | 55 |
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+ | 2.5192 | 4.2706 | 0.2 | 56 |
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+ | 2.5761 | 4.1395 | 0.25 | 57 |
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+ | 2.3516 | 3.9031 | 0.3 | 58 |
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+ | 2.3231 | 3.8172 | 0.35 | 59 |
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+ | 2.2735 | 3.7651 | 0.35 | 60 |
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+ | 2.1215 | 3.8034 | 0.35 | 61 |
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+ | 2.3229 | 3.8096 | 0.35 | 62 |
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+ | 2.2230 | 3.7000 | 0.35 | 63 |
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+ | 1.9059 | 3.6666 | 0.25 | 64 |
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+ | 2.0289 | 3.6743 | 0.25 | 65 |
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+ | 1.9178 | 3.5819 | 0.3 | 66 |
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+ | 2.0295 | 3.5087 | 0.35 | 67 |
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+ | 1.6499 | 3.4962 | 0.4 | 68 |
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+ | 1.6261 | 3.4146 | 0.3 | 69 |
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+ | 1.7059 | 3.4097 | 0.35 | 70 |
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+ | 1.4837 | 3.2702 | 0.35 | 71 |
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+ | 1.3766 | 3.2214 | 0.4 | 72 |
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+ | 1.5898 | 3.2674 | 0.4 | 73 |
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+ | 1.5002 | 3.1907 | 0.4 | 74 |
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+ | 1.2641 | 3.1176 | 0.4 | 75 |
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+ | 1.3456 | 3.1562 | 0.4 | 76 |
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+ | 1.2655 | 2.9548 | 0.5 | 77 |
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+ | 1.5449 | 2.8738 | 0.5 | 78 |
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+ | 1.2519 | 2.8336 | 0.45 | 79 |
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+ | 1.0682 | 2.8478 | 0.35 | 80 |
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+ | 1.1891 | 2.8408 | 0.5 | 81 |
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+ | 1.2920 | 2.6254 | 0.5 | 82 |
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+ | 1.1239 | 2.7507 | 0.5 | 83 |
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+ | 1.0857 | 2.7772 | 0.4 | 84 |
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+ | 0.9821 | 2.8372 | 0.45 | 85 |
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+ | 1.0457 | 2.8636 | 0.45 | 86 |
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+ | 1.1419 | 2.8426 | 0.45 | 87 |
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+ | 1.0782 | 2.7856 | 0.5 | 88 |
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+ | 0.9906 | 2.6826 | 0.55 | 89 |
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+ | 1.0766 | 2.6707 | 0.5 | 90 |
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+ | 1.1115 | 2.6457 | 0.5 | 91 |
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+ | 1.2201 | 2.6838 | 0.55 | 92 |
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+ | 0.8706 | 2.5262 | 0.55 | 93 |
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+ | 0.7441 | 2.5422 | 0.55 | 94 |
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+ | 0.9710 | 2.4211 | 0.6 | 95 |
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+ | 0.9731 | 2.4090 | 0.6 | 96 |
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+ | 0.8942 | 2.3773 | 0.6 | 97 |
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+ | 1.0461 | 2.4159 | 0.55 | 98 |
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+ | 0.8261 | 2.4425 | 0.6 | 99 |
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  ### Framework versions
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