File size: 2,278 Bytes
81bc71e a233ce7 81bc71e a233ce7 81bc71e a233ce7 81bc71e |
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 |
---
license: apache-2.0
base_model: microsoft/resnet-152
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Resnet152-30VN
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: vuongnhathien/30VNFoods
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8353174603174603
---
<!-- 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. -->
# Resnet152-30VN
This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the vuongnhathien/30VNFoods dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5769
- Accuracy: 0.8353
## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.4198 | 1.0 | 275 | 0.7348 | 0.8741 |
| 0.565 | 2.0 | 550 | 0.8119 | 0.6347 |
| 0.2846 | 3.0 | 825 | 0.8310 | 0.6003 |
| 0.1727 | 4.0 | 1100 | 0.8410 | 0.6041 |
| 0.0835 | 5.0 | 1375 | 0.8461 | 0.6464 |
| 0.0534 | 6.0 | 1650 | 0.8565 | 0.6776 |
| 0.0283 | 7.0 | 1925 | 0.7107 | 0.8501 |
| 0.0186 | 8.0 | 2200 | 0.7066 | 0.8620 |
| 0.0111 | 9.0 | 2475 | 0.6772 | 0.8648 |
| 0.0096 | 10.0 | 2750 | 0.6898 | 0.8628 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|