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Dogs-Breed-Image-Classification-V0

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resnet
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1
- ---
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- license: apache-2.0
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- base_model: microsoft/resnet-50
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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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- metrics:
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- - accuracy
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- model-index:
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- - name: Dogs-Breed-Image-Classification-V0
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- results:
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- - task:
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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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- args: default
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- metrics:
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- - name: Accuracy
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- type: accuracy
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- value: 0.7444120505344995
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- ---
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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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- should probably proofread and complete it, then remove this comment. -->
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-
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- # Dogs-Breed-Image-Classification-V0
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-
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- This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 1.8210
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- - Accuracy: 0.7444
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
44
- More information needed
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-
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- ## Training and evaluation data
47
-
48
- More information needed
49
-
50
- ## Training procedure
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-
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- ### Training hyperparameters
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-
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- The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 32
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- - eval_batch_size: 32
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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: 100
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-
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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- | 13.4902 | 1.0 | 515 | 4.7822 | 0.0104 |
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- | 4.7159 | 2.0 | 1030 | 4.6822 | 0.0323 |
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- | 4.6143 | 3.0 | 1545 | 4.5940 | 0.0554 |
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- | 4.4855 | 4.0 | 2060 | 4.5027 | 0.0935 |
71
- | 4.36 | 5.0 | 2575 | 4.3961 | 0.1239 |
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- | 4.2198 | 6.0 | 3090 | 4.3112 | 0.1528 |
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- | 4.0882 | 7.0 | 3605 | 4.1669 | 0.1747 |
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- | 3.9314 | 8.0 | 4120 | 4.0775 | 0.2021 |
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- | 3.7863 | 9.0 | 4635 | 3.9487 | 0.2310 |
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- | 3.6511 | 10.0 | 5150 | 3.9028 | 0.2466 |
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- | 3.5168 | 11.0 | 5665 | 3.8635 | 0.2626 |
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- | 3.3999 | 12.0 | 6180 | 3.7550 | 0.2767 |
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- | 3.3037 | 13.0 | 6695 | 3.6973 | 0.2884 |
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- | 3.1613 | 14.0 | 7210 | 3.6315 | 0.3037 |
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- | 3.0754 | 15.0 | 7725 | 3.4839 | 0.3188 |
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- | 2.9441 | 16.0 | 8240 | 3.4406 | 0.3302 |
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- | 2.8579 | 17.0 | 8755 | 3.3528 | 0.3406 |
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- | 2.7531 | 18.0 | 9270 | 3.3132 | 0.3472 |
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- | 2.6477 | 19.0 | 9785 | 3.2736 | 0.3567 |
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- | 2.5422 | 20.0 | 10300 | 3.1950 | 0.3756 |
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- | 2.4629 | 21.0 | 10815 | 3.1174 | 0.4004 |
88
- | 2.3735 | 22.0 | 11330 | 2.9916 | 0.4225 |
89
- | 2.2436 | 23.0 | 11845 | 2.9205 | 0.4509 |
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- | 2.1578 | 24.0 | 12360 | 2.9197 | 0.4689 |
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- | 2.0671 | 25.0 | 12875 | 2.8196 | 0.4866 |
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- | 1.9902 | 26.0 | 13390 | 2.7117 | 0.4961 |
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- | 1.8737 | 27.0 | 13905 | 2.7129 | 0.5078 |
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- | 1.7945 | 28.0 | 14420 | 2.6654 | 0.5143 |
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- | 1.7092 | 29.0 | 14935 | 2.6273 | 0.5301 |
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- | 1.6228 | 30.0 | 15450 | 2.5407 | 0.5454 |
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- | 1.5744 | 31.0 | 15965 | 2.5412 | 0.5559 |
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- | 1.4761 | 32.0 | 16480 | 2.4658 | 0.5658 |
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- | 1.4084 | 33.0 | 16995 | 2.4247 | 0.5673 |
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- | 1.2624 | 34.0 | 17510 | 2.3766 | 0.5758 |
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- | 1.2066 | 35.0 | 18025 | 2.2879 | 0.5843 |
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- | 1.124 | 36.0 | 18540 | 2.2039 | 0.5872 |
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- | 1.074 | 37.0 | 19055 | 2.2469 | 0.5965 |
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- | 0.9937 | 38.0 | 19570 | 2.1575 | 0.6011 |
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- | 0.9418 | 39.0 | 20085 | 2.0854 | 0.6122 |
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- | 0.8812 | 40.0 | 20600 | 1.9991 | 0.6254 |
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- | 0.819 | 41.0 | 21115 | 2.0161 | 0.6312 |
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- | 0.771 | 42.0 | 21630 | 1.9253 | 0.6375 |
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- | 0.7128 | 43.0 | 22145 | 1.9412 | 0.6390 |
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- | 0.6434 | 44.0 | 22660 | 1.8463 | 0.6509 |
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- | 0.6138 | 45.0 | 23175 | 1.8163 | 0.6650 |
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- | 0.5325 | 46.0 | 23690 | 1.7881 | 0.6710 |
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- | 0.498 | 47.0 | 24205 | 1.7526 | 0.6744 |
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- | 0.4565 | 48.0 | 24720 | 1.7155 | 0.6859 |
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- | 0.4109 | 49.0 | 25235 | 1.6874 | 0.6946 |
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- | 0.3681 | 50.0 | 25750 | 1.7386 | 0.6997 |
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- | 0.3306 | 51.0 | 26265 | 1.6578 | 0.7104 |
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- | 0.2913 | 52.0 | 26780 | 1.6641 | 0.7104 |
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- | 0.2598 | 53.0 | 27295 | 1.6823 | 0.7162 |
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- | 0.2311 | 54.0 | 27810 | 1.6835 | 0.7157 |
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- | 0.2115 | 55.0 | 28325 | 1.6581 | 0.7206 |
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- | 0.1843 | 56.0 | 28840 | 1.6286 | 0.7274 |
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- | 0.1668 | 57.0 | 29355 | 1.6358 | 0.7225 |
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- | 0.1483 | 58.0 | 29870 | 1.6422 | 0.7250 |
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- | 0.132 | 59.0 | 30385 | 1.6618 | 0.7284 |
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- | 0.1164 | 60.0 | 30900 | 1.6894 | 0.7262 |
127
- | 0.1043 | 61.0 | 31415 | 1.6923 | 0.7276 |
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- | 0.0937 | 62.0 | 31930 | 1.6627 | 0.7323 |
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- | 0.0826 | 63.0 | 32445 | 1.6280 | 0.7342 |
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- | 0.0743 | 64.0 | 32960 | 1.6204 | 0.7366 |
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- | 0.0638 | 65.0 | 33475 | 1.6890 | 0.7383 |
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- | 0.0603 | 66.0 | 33990 | 1.6967 | 0.7335 |
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- | 0.0491 | 67.0 | 34505 | 1.6975 | 0.7306 |
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- | 0.0459 | 68.0 | 35020 | 1.7242 | 0.7337 |
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- | 0.0416 | 69.0 | 35535 | 1.7019 | 0.7374 |
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- | 0.0382 | 70.0 | 36050 | 1.7098 | 0.7381 |
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- | 0.0378 | 71.0 | 36565 | 1.7188 | 0.7383 |
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- | 0.0326 | 72.0 | 37080 | 1.8212 | 0.7376 |
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- | 0.0323 | 73.0 | 37595 | 1.7965 | 0.7393 |
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- | 0.0299 | 74.0 | 38110 | 1.7934 | 0.7301 |
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- | 0.0259 | 75.0 | 38625 | 1.7799 | 0.7335 |
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- | 0.0276 | 76.0 | 39140 | 1.8456 | 0.7301 |
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- | 0.0257 | 77.0 | 39655 | 1.8551 | 0.7391 |
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- | 0.0234 | 78.0 | 40170 | 1.7780 | 0.7391 |
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- | 0.0222 | 79.0 | 40685 | 1.8216 | 0.7362 |
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- | 0.0195 | 80.0 | 41200 | 1.8333 | 0.7352 |
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- | 0.0214 | 81.0 | 41715 | 1.8526 | 0.7430 |
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- | 0.0207 | 82.0 | 42230 | 1.8581 | 0.7364 |
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- | 0.0171 | 83.0 | 42745 | 1.8329 | 0.7393 |
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- | 0.0175 | 84.0 | 43260 | 1.8841 | 0.7396 |
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- | 0.0165 | 85.0 | 43775 | 1.8381 | 0.7345 |
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- | 0.0152 | 86.0 | 44290 | 1.8192 | 0.7379 |
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- | 0.0168 | 87.0 | 44805 | 1.8538 | 0.7388 |
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- | 0.0158 | 88.0 | 45320 | 1.8390 | 0.7371 |
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- | 0.0181 | 89.0 | 45835 | 1.8555 | 0.7374 |
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- | 0.0142 | 90.0 | 46350 | 1.7987 | 0.7352 |
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- | 0.0147 | 91.0 | 46865 | 1.8446 | 0.7427 |
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- | 0.0142 | 92.0 | 47380 | 1.8210 | 0.7444 |
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- | 0.0124 | 93.0 | 47895 | 1.8233 | 0.7405 |
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- | 0.0128 | 94.0 | 48410 | 1.8517 | 0.7393 |
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- | 0.0135 | 95.0 | 48925 | 1.8408 | 0.7413 |
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- | 0.0122 | 96.0 | 49440 | 1.8153 | 0.7396 |
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- | 0.0141 | 97.0 | 49955 | 1.8645 | 0.7432 |
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- | 0.0121 | 98.0 | 50470 | 1.8526 | 0.7430 |
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- | 0.0124 | 99.0 | 50985 | 1.8693 | 0.7388 |
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- | 0.0113 | 100.0 | 51500 | 1.8051 | 0.7427 |
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.37.2
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- - Pytorch 2.3.0
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- - Datasets 2.15.0
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- - Tokenizers 0.15.1
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ base_model: microsoft/resnet-50
4
+ tags:
5
+ - generated_from_trainer
6
+ datasets:
7
+ - imagefolder
8
+ metrics:
9
+ - accuracy
10
+ model-index:
11
+ - name: Dogs-Breed-Image-Classification-V0
12
+ results:
13
+ - task:
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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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+ args: default
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+ metrics:
23
+ - name: Accuracy
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+ type: accuracy
25
+ value: 0.7444120505344995
26
+ ---
27
+
28
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
29
+ should probably proofread and complete it, then remove this comment. -->
30
+
31
+ # Dogs-Breed-Image-Classification-V0
32
+
33
+ This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
34
+ It achieves the following results on the evaluation set:
35
+ - Loss: 1.8210
36
+ - Accuracy: 0.7444
37
+
38
+ ## Model description
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+
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+ This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.)
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+
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+ Quotes from the website:
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+ The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.
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+
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+ citation:
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+ Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex]
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+
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+ Secondary:
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+ J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex]
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+ ## Intended uses & limitations
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+
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+ This model is fined tune solely for classifiying 120 species of dogs.
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+
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+ ## Training and evaluation data
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+
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+ 75% training data, 25% testing data.
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+
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+ ## Training procedure
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+
60
+ ### Training hyperparameters
61
+
62
+ The following hyperparameters were used during training:
63
+ - learning_rate: 5e-05
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+ - train_batch_size: 32
65
+ - eval_batch_size: 32
66
+ - seed: 42
67
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
68
+ - lr_scheduler_type: linear
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+ - num_epochs: 100
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+
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+ ### Training results
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+
73
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
74
+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
75
+ | 13.4902 | 1.0 | 515 | 4.7822 | 0.0104 |
76
+ | 4.7159 | 2.0 | 1030 | 4.6822 | 0.0323 |
77
+ | 4.6143 | 3.0 | 1545 | 4.5940 | 0.0554 |
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+ | 4.4855 | 4.0 | 2060 | 4.5027 | 0.0935 |
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+ | 4.36 | 5.0 | 2575 | 4.3961 | 0.1239 |
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+ | 4.2198 | 6.0 | 3090 | 4.3112 | 0.1528 |
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+ | 4.0882 | 7.0 | 3605 | 4.1669 | 0.1747 |
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+ | 3.9314 | 8.0 | 4120 | 4.0775 | 0.2021 |
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+ | 3.7863 | 9.0 | 4635 | 3.9487 | 0.2310 |
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+ | 3.6511 | 10.0 | 5150 | 3.9028 | 0.2466 |
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+ | 3.5168 | 11.0 | 5665 | 3.8635 | 0.2626 |
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+ | 3.3999 | 12.0 | 6180 | 3.7550 | 0.2767 |
87
+ | 3.3037 | 13.0 | 6695 | 3.6973 | 0.2884 |
88
+ | 3.1613 | 14.0 | 7210 | 3.6315 | 0.3037 |
89
+ | 3.0754 | 15.0 | 7725 | 3.4839 | 0.3188 |
90
+ | 2.9441 | 16.0 | 8240 | 3.4406 | 0.3302 |
91
+ | 2.8579 | 17.0 | 8755 | 3.3528 | 0.3406 |
92
+ | 2.7531 | 18.0 | 9270 | 3.3132 | 0.3472 |
93
+ | 2.6477 | 19.0 | 9785 | 3.2736 | 0.3567 |
94
+ | 2.5422 | 20.0 | 10300 | 3.1950 | 0.3756 |
95
+ | 2.4629 | 21.0 | 10815 | 3.1174 | 0.4004 |
96
+ | 2.3735 | 22.0 | 11330 | 2.9916 | 0.4225 |
97
+ | 2.2436 | 23.0 | 11845 | 2.9205 | 0.4509 |
98
+ | 2.1578 | 24.0 | 12360 | 2.9197 | 0.4689 |
99
+ | 2.0671 | 25.0 | 12875 | 2.8196 | 0.4866 |
100
+ | 1.9902 | 26.0 | 13390 | 2.7117 | 0.4961 |
101
+ | 1.8737 | 27.0 | 13905 | 2.7129 | 0.5078 |
102
+ | 1.7945 | 28.0 | 14420 | 2.6654 | 0.5143 |
103
+ | 1.7092 | 29.0 | 14935 | 2.6273 | 0.5301 |
104
+ | 1.6228 | 30.0 | 15450 | 2.5407 | 0.5454 |
105
+ | 1.5744 | 31.0 | 15965 | 2.5412 | 0.5559 |
106
+ | 1.4761 | 32.0 | 16480 | 2.4658 | 0.5658 |
107
+ | 1.4084 | 33.0 | 16995 | 2.4247 | 0.5673 |
108
+ | 1.2624 | 34.0 | 17510 | 2.3766 | 0.5758 |
109
+ | 1.2066 | 35.0 | 18025 | 2.2879 | 0.5843 |
110
+ | 1.124 | 36.0 | 18540 | 2.2039 | 0.5872 |
111
+ | 1.074 | 37.0 | 19055 | 2.2469 | 0.5965 |
112
+ | 0.9937 | 38.0 | 19570 | 2.1575 | 0.6011 |
113
+ | 0.9418 | 39.0 | 20085 | 2.0854 | 0.6122 |
114
+ | 0.8812 | 40.0 | 20600 | 1.9991 | 0.6254 |
115
+ | 0.819 | 41.0 | 21115 | 2.0161 | 0.6312 |
116
+ | 0.771 | 42.0 | 21630 | 1.9253 | 0.6375 |
117
+ | 0.7128 | 43.0 | 22145 | 1.9412 | 0.6390 |
118
+ | 0.6434 | 44.0 | 22660 | 1.8463 | 0.6509 |
119
+ | 0.6138 | 45.0 | 23175 | 1.8163 | 0.6650 |
120
+ | 0.5325 | 46.0 | 23690 | 1.7881 | 0.6710 |
121
+ | 0.498 | 47.0 | 24205 | 1.7526 | 0.6744 |
122
+ | 0.4565 | 48.0 | 24720 | 1.7155 | 0.6859 |
123
+ | 0.4109 | 49.0 | 25235 | 1.6874 | 0.6946 |
124
+ | 0.3681 | 50.0 | 25750 | 1.7386 | 0.6997 |
125
+ | 0.3306 | 51.0 | 26265 | 1.6578 | 0.7104 |
126
+ | 0.2913 | 52.0 | 26780 | 1.6641 | 0.7104 |
127
+ | 0.2598 | 53.0 | 27295 | 1.6823 | 0.7162 |
128
+ | 0.2311 | 54.0 | 27810 | 1.6835 | 0.7157 |
129
+ | 0.2115 | 55.0 | 28325 | 1.6581 | 0.7206 |
130
+ | 0.1843 | 56.0 | 28840 | 1.6286 | 0.7274 |
131
+ | 0.1668 | 57.0 | 29355 | 1.6358 | 0.7225 |
132
+ | 0.1483 | 58.0 | 29870 | 1.6422 | 0.7250 |
133
+ | 0.132 | 59.0 | 30385 | 1.6618 | 0.7284 |
134
+ | 0.1164 | 60.0 | 30900 | 1.6894 | 0.7262 |
135
+ | 0.1043 | 61.0 | 31415 | 1.6923 | 0.7276 |
136
+ | 0.0937 | 62.0 | 31930 | 1.6627 | 0.7323 |
137
+ | 0.0826 | 63.0 | 32445 | 1.6280 | 0.7342 |
138
+ | 0.0743 | 64.0 | 32960 | 1.6204 | 0.7366 |
139
+ | 0.0638 | 65.0 | 33475 | 1.6890 | 0.7383 |
140
+ | 0.0603 | 66.0 | 33990 | 1.6967 | 0.7335 |
141
+ | 0.0491 | 67.0 | 34505 | 1.6975 | 0.7306 |
142
+ | 0.0459 | 68.0 | 35020 | 1.7242 | 0.7337 |
143
+ | 0.0416 | 69.0 | 35535 | 1.7019 | 0.7374 |
144
+ | 0.0382 | 70.0 | 36050 | 1.7098 | 0.7381 |
145
+ | 0.0378 | 71.0 | 36565 | 1.7188 | 0.7383 |
146
+ | 0.0326 | 72.0 | 37080 | 1.8212 | 0.7376 |
147
+ | 0.0323 | 73.0 | 37595 | 1.7965 | 0.7393 |
148
+ | 0.0299 | 74.0 | 38110 | 1.7934 | 0.7301 |
149
+ | 0.0259 | 75.0 | 38625 | 1.7799 | 0.7335 |
150
+ | 0.0276 | 76.0 | 39140 | 1.8456 | 0.7301 |
151
+ | 0.0257 | 77.0 | 39655 | 1.8551 | 0.7391 |
152
+ | 0.0234 | 78.0 | 40170 | 1.7780 | 0.7391 |
153
+ | 0.0222 | 79.0 | 40685 | 1.8216 | 0.7362 |
154
+ | 0.0195 | 80.0 | 41200 | 1.8333 | 0.7352 |
155
+ | 0.0214 | 81.0 | 41715 | 1.8526 | 0.7430 |
156
+ | 0.0207 | 82.0 | 42230 | 1.8581 | 0.7364 |
157
+ | 0.0171 | 83.0 | 42745 | 1.8329 | 0.7393 |
158
+ | 0.0175 | 84.0 | 43260 | 1.8841 | 0.7396 |
159
+ | 0.0165 | 85.0 | 43775 | 1.8381 | 0.7345 |
160
+ | 0.0152 | 86.0 | 44290 | 1.8192 | 0.7379 |
161
+ | 0.0168 | 87.0 | 44805 | 1.8538 | 0.7388 |
162
+ | 0.0158 | 88.0 | 45320 | 1.8390 | 0.7371 |
163
+ | 0.0181 | 89.0 | 45835 | 1.8555 | 0.7374 |
164
+ | 0.0142 | 90.0 | 46350 | 1.7987 | 0.7352 |
165
+ | 0.0147 | 91.0 | 46865 | 1.8446 | 0.7427 |
166
+ | 0.0142 | 92.0 | 47380 | 1.8210 | 0.7444 |
167
+ | 0.0124 | 93.0 | 47895 | 1.8233 | 0.7405 |
168
+ | 0.0128 | 94.0 | 48410 | 1.8517 | 0.7393 |
169
+ | 0.0135 | 95.0 | 48925 | 1.8408 | 0.7413 |
170
+ | 0.0122 | 96.0 | 49440 | 1.8153 | 0.7396 |
171
+ | 0.0141 | 97.0 | 49955 | 1.8645 | 0.7432 |
172
+ | 0.0121 | 98.0 | 50470 | 1.8526 | 0.7430 |
173
+ | 0.0124 | 99.0 | 50985 | 1.8693 | 0.7388 |
174
+ | 0.0113 | 100.0 | 51500 | 1.8051 | 0.7427 |
175
+
176
+
177
+ ### Framework versions
178
+
179
+ - Transformers 4.37.2
180
+ - Pytorch 2.3.0
181
+ - Datasets 2.15.0
182
+ - Tokenizers 0.15.1