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---
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
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