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---
license: apache-2.0
base_model: microsoft/resnet-101
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Dogs-Breed-Image-Classification-V1
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: 0.8757971454600668
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dogs-Breed-Image-Classification-V1
This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the [Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.).
It achieves the following results on the evaluation set:
- Loss: 0.4469
- Accuracy: 0.8758
## Model description
[Link to the fine-tuned model using resnet-50](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V0)
This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset)
Quotes from the website:
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.
citation:
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]
Secondary:
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]
## Intended uses & limitations
This model is fined tune solely for classifiying 120 species of dogs.
## Training and evaluation data
75% training data, 25% testing data.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 309 | 18.7685 | 0.0091 |
| 18.7211 | 2.0 | 618 | 18.5975 | 0.0091 |
| 18.7211 | 3.0 | 927 | 17.4087 | 0.0091 |
| 15.4274 | 4.0 | 1236 | 11.8712 | 0.0091 |
| 10.3252 | 5.0 | 1545 | 6.6642 | 0.0091 |
| 10.3252 | 6.0 | 1854 | 5.2754 | 0.0112 |
| 6.2268 | 7.0 | 2163 | 4.8454 | 0.0158 |
| 6.2268 | 8.0 | 2472 | 4.7658 | 0.0140 |
| 4.9682 | 9.0 | 2781 | 4.6860 | 0.0234 |
| 4.7245 | 10.0 | 3090 | 4.6165 | 0.0316 |
| 4.7245 | 11.0 | 3399 | 4.5349 | 0.0446 |
| 4.5441 | 12.0 | 3708 | 4.4555 | 0.0623 |
| 4.3912 | 13.0 | 4017 | 4.3437 | 0.0862 |
| 4.3912 | 14.0 | 4326 | 4.2182 | 0.1330 |
| 4.2211 | 15.0 | 4635 | 4.0752 | 0.2153 |
| 4.2211 | 16.0 | 4944 | 3.9803 | 0.2599 |
| 3.9762 | 17.0 | 5253 | 3.7347 | 0.3596 |
| 3.69 | 18.0 | 5562 | 3.5493 | 0.4194 |
| 3.69 | 19.0 | 5871 | 3.3404 | 0.4813 |
| 3.3803 | 20.0 | 6180 | 3.1122 | 0.5600 |
| 3.3803 | 21.0 | 6489 | 2.8656 | 0.6101 |
| 3.0345 | 22.0 | 6798 | 2.6544 | 0.6462 |
| 2.6793 | 23.0 | 7107 | 2.4178 | 0.6647 |
| 2.6793 | 24.0 | 7416 | 2.1967 | 0.7121 |
| 2.3251 | 25.0 | 7725 | 2.0091 | 0.7203 |
| 1.9975 | 26.0 | 8034 | 1.8189 | 0.7464 |
| 1.9975 | 27.0 | 8343 | 1.6537 | 0.7519 |
| 1.7009 | 28.0 | 8652 | 1.4413 | 0.7880 |
| 1.7009 | 29.0 | 8961 | 1.3137 | 0.7968 |
| 1.4494 | 30.0 | 9270 | 1.2150 | 0.7929 |
| 1.2389 | 31.0 | 9579 | 1.1238 | 0.8041 |
| 1.2389 | 32.0 | 9888 | 1.0215 | 0.8208 |
| 1.0646 | 33.0 | 10197 | 0.9637 | 0.8190 |
| 0.9319 | 34.0 | 10506 | 0.8891 | 0.8299 |
| 0.9319 | 35.0 | 10815 | 0.8520 | 0.8330 |
| 0.8297 | 36.0 | 11124 | 0.8212 | 0.8400 |
| 0.8297 | 37.0 | 11433 | 0.7579 | 0.8415 |
| 0.7293 | 38.0 | 11742 | 0.7254 | 0.8454 |
| 0.6657 | 39.0 | 12051 | 0.7019 | 0.8457 |
| 0.6657 | 40.0 | 12360 | 0.6669 | 0.8527 |
| 0.6047 | 41.0 | 12669 | 0.6510 | 0.8530 |
| 0.6047 | 42.0 | 12978 | 0.6264 | 0.8545 |
| 0.557 | 43.0 | 13287 | 0.6275 | 0.8506 |
| 0.5126 | 44.0 | 13596 | 0.5947 | 0.8536 |
| 0.5126 | 45.0 | 13905 | 0.5860 | 0.8573 |
| 0.475 | 46.0 | 14214 | 0.5745 | 0.8545 |
| 0.4406 | 47.0 | 14523 | 0.5579 | 0.8600 |
| 0.4406 | 48.0 | 14832 | 0.5386 | 0.8621 |
| 0.4086 | 49.0 | 15141 | 0.5346 | 0.8624 |
| 0.4086 | 50.0 | 15450 | 0.5200 | 0.8612 |
| 0.3882 | 51.0 | 15759 | 0.5233 | 0.8612 |
| 0.3646 | 52.0 | 16068 | 0.5148 | 0.8640 |
| 0.3646 | 53.0 | 16377 | 0.5078 | 0.8679 |
| 0.3386 | 54.0 | 16686 | 0.5067 | 0.8646 |
| 0.3386 | 55.0 | 16995 | 0.4976 | 0.8673 |
| 0.3208 | 56.0 | 17304 | 0.4934 | 0.8682 |
| 0.3039 | 57.0 | 17613 | 0.4849 | 0.8688 |
| 0.3039 | 58.0 | 17922 | 0.4930 | 0.8691 |
| 0.2915 | 59.0 | 18231 | 0.4867 | 0.8655 |
| 0.2784 | 60.0 | 18540 | 0.4832 | 0.8679 |
| 0.2784 | 61.0 | 18849 | 0.4785 | 0.8670 |
| 0.2597 | 62.0 | 19158 | 0.4753 | 0.8685 |
| 0.2597 | 63.0 | 19467 | 0.4701 | 0.8712 |
| 0.2488 | 64.0 | 19776 | 0.4766 | 0.8697 |
| 0.2426 | 65.0 | 20085 | 0.4726 | 0.8700 |
| 0.2426 | 66.0 | 20394 | 0.4670 | 0.8694 |
| 0.2261 | 67.0 | 20703 | 0.4624 | 0.8722 |
| 0.2252 | 68.0 | 21012 | 0.4631 | 0.8718 |
| 0.2252 | 69.0 | 21321 | 0.4702 | 0.8670 |
| 0.2116 | 70.0 | 21630 | 0.4629 | 0.8715 |
| 0.2116 | 71.0 | 21939 | 0.4650 | 0.8685 |
| 0.2032 | 72.0 | 22248 | 0.4670 | 0.8673 |
| 0.2035 | 73.0 | 22557 | 0.4565 | 0.8670 |
| 0.2035 | 74.0 | 22866 | 0.4550 | 0.8697 |
| 0.19 | 75.0 | 23175 | 0.4544 | 0.8706 |
| 0.19 | 76.0 | 23484 | 0.4483 | 0.8670 |
| 0.1833 | 77.0 | 23793 | 0.4650 | 0.8694 |
| 0.184 | 78.0 | 24102 | 0.4604 | 0.8709 |
| 0.184 | 79.0 | 24411 | 0.4484 | 0.8697 |
| 0.1728 | 80.0 | 24720 | 0.4469 | 0.8758 |
| 0.1688 | 81.0 | 25029 | 0.4536 | 0.8676 |
| 0.1688 | 82.0 | 25338 | 0.4450 | 0.8709 |
| 0.1674 | 83.0 | 25647 | 0.4530 | 0.8691 |
| 0.1674 | 84.0 | 25956 | 0.4532 | 0.8725 |
| 0.1632 | 85.0 | 26265 | 0.4495 | 0.8718 |
| 0.1605 | 86.0 | 26574 | 0.4440 | 0.8673 |
| 0.1605 | 87.0 | 26883 | 0.4504 | 0.8731 |
| 0.1586 | 88.0 | 27192 | 0.4551 | 0.8667 |
| 0.1558 | 89.0 | 27501 | 0.4498 | 0.8670 |
| 0.1558 | 90.0 | 27810 | 0.4516 | 0.8718 |
| 0.1587 | 91.0 | 28119 | 0.4450 | 0.8725 |
| 0.1587 | 92.0 | 28428 | 0.4435 | 0.8706 |
| 0.1505 | 93.0 | 28737 | 0.4459 | 0.8722 |
| 0.1492 | 94.0 | 29046 | 0.4578 | 0.8673 |
| 0.1492 | 95.0 | 29355 | 0.4499 | 0.8725 |
| 0.1459 | 96.0 | 29664 | 0.4494 | 0.8703 |
| 0.1459 | 97.0 | 29973 | 0.4533 | 0.8697 |
| 0.1481 | 98.0 | 30282 | 0.4524 | 0.8652 |
| 0.1477 | 99.0 | 30591 | 0.4496 | 0.8715 |
| 0.1477 | 100.0 | 30900 | 0.4523 | 0.8661 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.15.1
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