File size: 2,632 Bytes
c681a23 ddd5d8f c681a23 ddd5d8f c681a23 ddd5d8f c681a23 |
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 |
---
license: apache-2.0
base_model: facebook/convnextv2-nano-22k-384
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-nano-15ep
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: 0.9081349206349206
---
<!-- 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. -->
# convnext-nano-15ep
This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on the vuongnhathien/30VNFoods dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4761
- Accuracy: 0.9081
## 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: cosine
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5814 | 1.0 | 275 | 0.5148 | 0.8477 |
| 0.2972 | 2.0 | 550 | 0.4967 | 0.8557 |
| 0.1871 | 3.0 | 825 | 0.4887 | 0.8716 |
| 0.1205 | 4.0 | 1100 | 0.5173 | 0.8688 |
| 0.0732 | 5.0 | 1375 | 0.4979 | 0.8815 |
| 0.0443 | 6.0 | 1650 | 0.5483 | 0.8815 |
| 0.0392 | 7.0 | 1925 | 0.5512 | 0.8835 |
| 0.018 | 8.0 | 2200 | 0.5102 | 0.8946 |
| 0.0043 | 9.0 | 2475 | 0.5423 | 0.8954 |
| 0.0087 | 10.0 | 2750 | 0.4903 | 0.9105 |
| 0.0035 | 11.0 | 3025 | 0.4855 | 0.9082 |
| 0.0022 | 12.0 | 3300 | 0.4874 | 0.9074 |
| 0.0019 | 13.0 | 3575 | 0.4858 | 0.9082 |
| 0.0018 | 14.0 | 3850 | 0.4857 | 0.9082 |
| 0.0018 | 15.0 | 4125 | 0.4859 | 0.9082 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|