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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: plant-seedlings-model-ConvNet
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: 0.9522292993630573
---
<!-- 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
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: 0.2410
- Accuracy: 0.9522
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.494 | 0.8 | 100 | 0.4274 | 0.8828 |
| 0.246 | 1.6 | 200 | 0.2878 | 0.8930 |
| 0.1042 | 2.4 | 300 | 0.2227 | 0.9172 |
| 0.0174 | 3.2 | 400 | 0.2208 | 0.9299 |
| 0.0088 | 4.0 | 500 | 0.3197 | 0.9185 |
| 0.0078 | 4.8 | 600 | 0.2555 | 0.9357 |
| 0.0013 | 5.6 | 700 | 0.2599 | 0.9427 |
| 0.0068 | 6.4 | 800 | 0.3072 | 0.9312 |
| 0.0007 | 7.2 | 900 | 0.2217 | 0.9484 |
| 0.0004 | 8.0 | 1000 | 0.2551 | 0.9401 |
| 0.0003 | 8.8 | 1100 | 0.2321 | 0.9478 |
| 0.0002 | 9.6 | 1200 | 0.2329 | 0.9484 |
| 0.0002 | 10.4 | 1300 | 0.2322 | 0.9478 |
| 0.0002 | 11.2 | 1400 | 0.2342 | 0.9478 |
| 0.0002 | 12.0 | 1500 | 0.2348 | 0.9490 |
| 0.0001 | 12.8 | 1600 | 0.2358 | 0.9490 |
| 0.0001 | 13.6 | 1700 | 0.2368 | 0.9497 |
| 0.0001 | 14.4 | 1800 | 0.2377 | 0.9510 |
| 0.0001 | 15.2 | 1900 | 0.2384 | 0.9516 |
| 0.0001 | 16.0 | 2000 | 0.2391 | 0.9516 |
| 0.0001 | 16.8 | 2100 | 0.2397 | 0.9522 |
| 0.0001 | 17.6 | 2200 | 0.2401 | 0.9522 |
| 0.0001 | 18.4 | 2300 | 0.2406 | 0.9522 |
| 0.0001 | 19.2 | 2400 | 0.2409 | 0.9522 |
| 0.0001 | 20.0 | 2500 | 0.2410 | 0.9522 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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