File size: 3,221 Bytes
52b02cb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-base-food101-jpqd-ov
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9060990099009901
---
<!-- 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. -->
# swin-base-food101-jpqd-ov
It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the
JPQD image classification example.
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3396
- Accuracy: 0.9061
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.2162 | 0.42 | 500 | 2.1111 | 0.7967 |
| 0.729 | 0.84 | 1000 | 0.5474 | 0.8773 |
| 0.7536 | 1.27 | 1500 | 0.3844 | 0.8984 |
| 0.4822 | 1.69 | 2000 | 0.3340 | 0.9043 |
| 12.2559 | 2.11 | 2500 | 12.0128 | 0.9033 |
| 48.7302 | 2.54 | 3000 | 48.3874 | 0.8681 |
| 75.1831 | 2.96 | 3500 | 75.3200 | 0.7183 |
| 93.5572 | 3.38 | 4000 | 93.4142 | 0.5939 |
| 103.798 | 3.8 | 4500 | 103.4427 | 0.5634 |
| 108.0993 | 4.23 | 5000 | 108.6461 | 0.5490 |
| 110.1265 | 4.65 | 5500 | 109.3663 | 0.5636 |
| 1.5584 | 5.07 | 6000 | 0.9255 | 0.8374 |
| 1.0883 | 5.49 | 6500 | 0.5841 | 0.8758 |
| 0.7024 | 5.92 | 7000 | 0.5055 | 0.8854 |
| 0.9033 | 6.34 | 7500 | 0.4639 | 0.8901 |
| 0.6901 | 6.76 | 8000 | 0.4360 | 0.8947 |
| 0.6114 | 7.19 | 8500 | 0.4080 | 0.8978 |
| 0.5102 | 7.61 | 9000 | 0.3911 | 0.9009 |
| 0.7154 | 8.03 | 9500 | 0.3747 | 0.9027 |
| 0.5621 | 8.45 | 10000 | 0.3622 | 0.9021 |
| 0.5262 | 8.88 | 10500 | 0.3554 | 0.9041 |
| 0.5442 | 9.3 | 11000 | 0.3462 | 0.9053 |
| 0.5615 | 9.72 | 11500 | 0.3416 | 0.9061 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|