|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: plant-seedlings-model-ConvNet-all-train |
|
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: 0.9171143514965464 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# plant-seedlings-model-ConvNet-all-train |
|
|
|
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2966 |
|
- Accuracy: 0.9171 |
|
|
|
## 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: 0.0002 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 16 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 1.2313 | 0.31 | 100 | 1.0832 | 0.6731 | |
|
| 0.7221 | 0.61 | 200 | 0.6529 | 0.7913 | |
|
| 0.5858 | 0.92 | 300 | 0.5267 | 0.8204 | |
|
| 0.4257 | 1.23 | 400 | 0.5765 | 0.8051 | |
|
| 0.6183 | 1.53 | 500 | 0.6322 | 0.7928 | |
|
| 0.4392 | 1.84 | 600 | 0.4168 | 0.8649 | |
|
| 0.3589 | 2.15 | 700 | 0.5549 | 0.8066 | |
|
| 0.4259 | 2.45 | 800 | 0.4678 | 0.8396 | |
|
| 0.3705 | 2.76 | 900 | 0.4542 | 0.8396 | |
|
| 0.4609 | 3.07 | 1000 | 0.4723 | 0.8411 | |
|
| 0.2082 | 3.37 | 1100 | 0.3631 | 0.8803 | |
|
| 0.4583 | 3.68 | 1200 | 0.3835 | 0.8688 | |
|
| 0.2218 | 3.99 | 1300 | 0.3913 | 0.8772 | |
|
| 0.3716 | 4.29 | 1400 | 0.3858 | 0.8818 | |
|
| 0.3675 | 4.6 | 1500 | 0.3849 | 0.8734 | |
|
| 0.2602 | 4.91 | 1600 | 0.4080 | 0.8734 | |
|
| 0.2091 | 5.21 | 1700 | 0.3767 | 0.8818 | |
|
| 0.2071 | 5.52 | 1800 | 0.3883 | 0.8795 | |
|
| 0.2426 | 5.83 | 1900 | 0.3557 | 0.8856 | |
|
| 0.2917 | 6.13 | 2000 | 0.3550 | 0.8872 | |
|
| 0.1417 | 6.44 | 2100 | 0.2918 | 0.9110 | |
|
| 0.237 | 6.75 | 2200 | 0.3785 | 0.8864 | |
|
| 0.1372 | 7.06 | 2300 | 0.3106 | 0.9025 | |
|
| 0.161 | 7.36 | 2400 | 0.3809 | 0.8841 | |
|
| 0.2354 | 7.67 | 2500 | 0.3739 | 0.8949 | |
|
| 0.2489 | 7.98 | 2600 | 0.3442 | 0.8941 | |
|
| 0.1962 | 8.28 | 2700 | 0.2875 | 0.9125 | |
|
| 0.3157 | 8.59 | 2800 | 0.2959 | 0.9163 | |
|
| 0.1204 | 8.9 | 2900 | 0.3017 | 0.9087 | |
|
| 0.1272 | 9.2 | 3000 | 0.3380 | 0.9071 | |
|
| 0.1768 | 9.51 | 3100 | 0.3611 | 0.9033 | |
|
| 0.2211 | 9.82 | 3200 | 0.2704 | 0.9210 | |
|
| 0.1213 | 10.12 | 3300 | 0.2813 | 0.9240 | |
|
| 0.0432 | 10.43 | 3400 | 0.2956 | 0.9179 | |
|
| 0.1152 | 10.74 | 3500 | 0.3256 | 0.9094 | |
|
| 0.178 | 11.04 | 3600 | 0.3470 | 0.9094 | |
|
| 0.1427 | 11.35 | 3700 | 0.3221 | 0.9079 | |
|
| 0.1046 | 11.66 | 3800 | 0.2559 | 0.9286 | |
|
| 0.1029 | 11.96 | 3900 | 0.2848 | 0.9202 | |
|
| 0.0459 | 12.27 | 4000 | 0.3051 | 0.9156 | |
|
| 0.1063 | 12.58 | 4100 | 0.2825 | 0.9225 | |
|
| 0.0974 | 12.88 | 4200 | 0.3168 | 0.9233 | |
|
| 0.0923 | 13.19 | 4300 | 0.3134 | 0.9194 | |
|
| 0.0736 | 13.5 | 4400 | 0.2480 | 0.9325 | |
|
| 0.0783 | 13.8 | 4500 | 0.2872 | 0.9202 | |
|
| 0.1444 | 14.11 | 4600 | 0.3011 | 0.9225 | |
|
| 0.1507 | 14.42 | 4700 | 0.2794 | 0.9271 | |
|
| 0.1318 | 14.72 | 4800 | 0.2625 | 0.9271 | |
|
| 0.0931 | 15.03 | 4900 | 0.2914 | 0.9279 | |
|
| 0.074 | 15.34 | 5000 | 0.2826 | 0.9248 | |
|
| 0.1306 | 15.64 | 5100 | 0.2836 | 0.9240 | |
|
| 0.0856 | 15.95 | 5200 | 0.2966 | 0.9171 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.1 |
|
- Pytorch 2.0.0+cu118 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.3 |
|
|
