metadata
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Dogs-Breed-Image-Classification-V0
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7444120505344995
Dogs-Breed-Image-Classification-V0
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.8210
- Accuracy: 0.7444
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 13.4902 | 1.0 | 515 | 4.7822 | 0.0104 |
| 4.7159 | 2.0 | 1030 | 4.6822 | 0.0323 |
| 4.6143 | 3.0 | 1545 | 4.5940 | 0.0554 |
| 4.4855 | 4.0 | 2060 | 4.5027 | 0.0935 |
| 4.36 | 5.0 | 2575 | 4.3961 | 0.1239 |
| 4.2198 | 6.0 | 3090 | 4.3112 | 0.1528 |
| 4.0882 | 7.0 | 3605 | 4.1669 | 0.1747 |
| 3.9314 | 8.0 | 4120 | 4.0775 | 0.2021 |
| 3.7863 | 9.0 | 4635 | 3.9487 | 0.2310 |
| 3.6511 | 10.0 | 5150 | 3.9028 | 0.2466 |
| 3.5168 | 11.0 | 5665 | 3.8635 | 0.2626 |
| 3.3999 | 12.0 | 6180 | 3.7550 | 0.2767 |
| 3.3037 | 13.0 | 6695 | 3.6973 | 0.2884 |
| 3.1613 | 14.0 | 7210 | 3.6315 | 0.3037 |
| 3.0754 | 15.0 | 7725 | 3.4839 | 0.3188 |
| 2.9441 | 16.0 | 8240 | 3.4406 | 0.3302 |
| 2.8579 | 17.0 | 8755 | 3.3528 | 0.3406 |
| 2.7531 | 18.0 | 9270 | 3.3132 | 0.3472 |
| 2.6477 | 19.0 | 9785 | 3.2736 | 0.3567 |
| 2.5422 | 20.0 | 10300 | 3.1950 | 0.3756 |
| 2.4629 | 21.0 | 10815 | 3.1174 | 0.4004 |
| 2.3735 | 22.0 | 11330 | 2.9916 | 0.4225 |
| 2.2436 | 23.0 | 11845 | 2.9205 | 0.4509 |
| 2.1578 | 24.0 | 12360 | 2.9197 | 0.4689 |
| 2.0671 | 25.0 | 12875 | 2.8196 | 0.4866 |
| 1.9902 | 26.0 | 13390 | 2.7117 | 0.4961 |
| 1.8737 | 27.0 | 13905 | 2.7129 | 0.5078 |
| 1.7945 | 28.0 | 14420 | 2.6654 | 0.5143 |
| 1.7092 | 29.0 | 14935 | 2.6273 | 0.5301 |
| 1.6228 | 30.0 | 15450 | 2.5407 | 0.5454 |
| 1.5744 | 31.0 | 15965 | 2.5412 | 0.5559 |
| 1.4761 | 32.0 | 16480 | 2.4658 | 0.5658 |
| 1.4084 | 33.0 | 16995 | 2.4247 | 0.5673 |
| 1.2624 | 34.0 | 17510 | 2.3766 | 0.5758 |
| 1.2066 | 35.0 | 18025 | 2.2879 | 0.5843 |
| 1.124 | 36.0 | 18540 | 2.2039 | 0.5872 |
| 1.074 | 37.0 | 19055 | 2.2469 | 0.5965 |
| 0.9937 | 38.0 | 19570 | 2.1575 | 0.6011 |
| 0.9418 | 39.0 | 20085 | 2.0854 | 0.6122 |
| 0.8812 | 40.0 | 20600 | 1.9991 | 0.6254 |
| 0.819 | 41.0 | 21115 | 2.0161 | 0.6312 |
| 0.771 | 42.0 | 21630 | 1.9253 | 0.6375 |
| 0.7128 | 43.0 | 22145 | 1.9412 | 0.6390 |
| 0.6434 | 44.0 | 22660 | 1.8463 | 0.6509 |
| 0.6138 | 45.0 | 23175 | 1.8163 | 0.6650 |
| 0.5325 | 46.0 | 23690 | 1.7881 | 0.6710 |
| 0.498 | 47.0 | 24205 | 1.7526 | 0.6744 |
| 0.4565 | 48.0 | 24720 | 1.7155 | 0.6859 |
| 0.4109 | 49.0 | 25235 | 1.6874 | 0.6946 |
| 0.3681 | 50.0 | 25750 | 1.7386 | 0.6997 |
| 0.3306 | 51.0 | 26265 | 1.6578 | 0.7104 |
| 0.2913 | 52.0 | 26780 | 1.6641 | 0.7104 |
| 0.2598 | 53.0 | 27295 | 1.6823 | 0.7162 |
| 0.2311 | 54.0 | 27810 | 1.6835 | 0.7157 |
| 0.2115 | 55.0 | 28325 | 1.6581 | 0.7206 |
| 0.1843 | 56.0 | 28840 | 1.6286 | 0.7274 |
| 0.1668 | 57.0 | 29355 | 1.6358 | 0.7225 |
| 0.1483 | 58.0 | 29870 | 1.6422 | 0.7250 |
| 0.132 | 59.0 | 30385 | 1.6618 | 0.7284 |
| 0.1164 | 60.0 | 30900 | 1.6894 | 0.7262 |
| 0.1043 | 61.0 | 31415 | 1.6923 | 0.7276 |
| 0.0937 | 62.0 | 31930 | 1.6627 | 0.7323 |
| 0.0826 | 63.0 | 32445 | 1.6280 | 0.7342 |
| 0.0743 | 64.0 | 32960 | 1.6204 | 0.7366 |
| 0.0638 | 65.0 | 33475 | 1.6890 | 0.7383 |
| 0.0603 | 66.0 | 33990 | 1.6967 | 0.7335 |
| 0.0491 | 67.0 | 34505 | 1.6975 | 0.7306 |
| 0.0459 | 68.0 | 35020 | 1.7242 | 0.7337 |
| 0.0416 | 69.0 | 35535 | 1.7019 | 0.7374 |
| 0.0382 | 70.0 | 36050 | 1.7098 | 0.7381 |
| 0.0378 | 71.0 | 36565 | 1.7188 | 0.7383 |
| 0.0326 | 72.0 | 37080 | 1.8212 | 0.7376 |
| 0.0323 | 73.0 | 37595 | 1.7965 | 0.7393 |
| 0.0299 | 74.0 | 38110 | 1.7934 | 0.7301 |
| 0.0259 | 75.0 | 38625 | 1.7799 | 0.7335 |
| 0.0276 | 76.0 | 39140 | 1.8456 | 0.7301 |
| 0.0257 | 77.0 | 39655 | 1.8551 | 0.7391 |
| 0.0234 | 78.0 | 40170 | 1.7780 | 0.7391 |
| 0.0222 | 79.0 | 40685 | 1.8216 | 0.7362 |
| 0.0195 | 80.0 | 41200 | 1.8333 | 0.7352 |
| 0.0214 | 81.0 | 41715 | 1.8526 | 0.7430 |
| 0.0207 | 82.0 | 42230 | 1.8581 | 0.7364 |
| 0.0171 | 83.0 | 42745 | 1.8329 | 0.7393 |
| 0.0175 | 84.0 | 43260 | 1.8841 | 0.7396 |
| 0.0165 | 85.0 | 43775 | 1.8381 | 0.7345 |
| 0.0152 | 86.0 | 44290 | 1.8192 | 0.7379 |
| 0.0168 | 87.0 | 44805 | 1.8538 | 0.7388 |
| 0.0158 | 88.0 | 45320 | 1.8390 | 0.7371 |
| 0.0181 | 89.0 | 45835 | 1.8555 | 0.7374 |
| 0.0142 | 90.0 | 46350 | 1.7987 | 0.7352 |
| 0.0147 | 91.0 | 46865 | 1.8446 | 0.7427 |
| 0.0142 | 92.0 | 47380 | 1.8210 | 0.7444 |
| 0.0124 | 93.0 | 47895 | 1.8233 | 0.7405 |
| 0.0128 | 94.0 | 48410 | 1.8517 | 0.7393 |
| 0.0135 | 95.0 | 48925 | 1.8408 | 0.7413 |
| 0.0122 | 96.0 | 49440 | 1.8153 | 0.7396 |
| 0.0141 | 97.0 | 49955 | 1.8645 | 0.7432 |
| 0.0121 | 98.0 | 50470 | 1.8526 | 0.7430 |
| 0.0124 | 99.0 | 50985 | 1.8693 | 0.7388 |
| 0.0113 | 100.0 | 51500 | 1.8051 | 0.7427 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.15.1