---
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: 0.9429097605893186
---

<!-- 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: 0.2056
- Accuracy: 0.9429

## 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: 14
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4109        | 0.25  | 100  | 0.5246          | 0.8195   |
| 0.248         | 0.49  | 200  | 0.4594          | 0.8459   |
| 0.3389        | 0.74  | 300  | 0.4443          | 0.8551   |
| 0.4217        | 0.98  | 400  | 0.4500          | 0.8490   |
| 0.2815        | 1.23  | 500  | 0.3939          | 0.8588   |
| 0.3077        | 1.47  | 600  | 0.3813          | 0.8643   |
| 0.5098        | 1.72  | 700  | 0.4276          | 0.8576   |
| 0.3191        | 1.97  | 800  | 0.4218          | 0.8570   |
| 0.2761        | 2.21  | 900  | 0.3404          | 0.8883   |
| 0.2184        | 2.46  | 1000 | 0.3226          | 0.8889   |
| 0.3106        | 2.7   | 1100 | 0.3621          | 0.8729   |
| 0.3118        | 2.95  | 1200 | 0.3656          | 0.8797   |
| 0.2857        | 3.19  | 1300 | 0.3123          | 0.9012   |
| 0.2193        | 3.44  | 1400 | 0.2907          | 0.9048   |
| 0.2959        | 3.69  | 1500 | 0.3544          | 0.8840   |
| 0.3176        | 3.93  | 1600 | 0.3389          | 0.8877   |
| 0.2927        | 4.18  | 1700 | 0.3418          | 0.8864   |
| 0.2719        | 4.42  | 1800 | 0.3558          | 0.8821   |
| 0.2176        | 4.67  | 1900 | 0.3374          | 0.8981   |
| 0.1912        | 4.91  | 2000 | 0.3092          | 0.8999   |
| 0.2272        | 5.16  | 2100 | 0.2902          | 0.9128   |
| 0.175         | 5.41  | 2200 | 0.3002          | 0.9134   |
| 0.1513        | 5.65  | 2300 | 0.3356          | 0.8999   |
| 0.1439        | 5.9   | 2400 | 0.2954          | 0.9061   |
| 0.2341        | 6.14  | 2500 | 0.3343          | 0.8993   |
| 0.2178        | 6.39  | 2600 | 0.2891          | 0.9122   |
| 0.1731        | 6.63  | 2700 | 0.3235          | 0.9030   |
| 0.19          | 6.88  | 2800 | 0.2938          | 0.9042   |
| 0.1168        | 7.13  | 2900 | 0.2937          | 0.9110   |
| 0.1528        | 7.37  | 3000 | 0.2963          | 0.9104   |
| 0.1374        | 7.62  | 3100 | 0.2929          | 0.9085   |
| 0.2204        | 7.86  | 3200 | 0.3257          | 0.9048   |
| 0.1519        | 8.11  | 3300 | 0.2683          | 0.9171   |
| 0.0711        | 8.35  | 3400 | 0.2609          | 0.9251   |
| 0.1019        | 8.6   | 3500 | 0.2523          | 0.9251   |
| 0.1764        | 8.85  | 3600 | 0.2769          | 0.9202   |
| 0.0849        | 9.09  | 3700 | 0.2668          | 0.9214   |
| 0.2077        | 9.34  | 3800 | 0.2914          | 0.9165   |
| 0.2543        | 9.58  | 3900 | 0.2507          | 0.9251   |
| 0.0347        | 9.83  | 4000 | 0.2333          | 0.9269   |
| 0.0731        | 10.07 | 4100 | 0.2598          | 0.9269   |
| 0.238         | 10.32 | 4200 | 0.2675          | 0.9294   |
| 0.1114        | 10.57 | 4300 | 0.2317          | 0.9269   |
| 0.0836        | 10.81 | 4400 | 0.2344          | 0.9288   |
| 0.0598        | 11.06 | 4500 | 0.2499          | 0.9276   |
| 0.0488        | 11.3  | 4600 | 0.2361          | 0.9288   |
| 0.1437        | 11.55 | 4700 | 0.2551          | 0.9282   |
| 0.0773        | 11.79 | 4800 | 0.2276          | 0.9294   |
| 0.1013        | 12.04 | 4900 | 0.2537          | 0.9288   |
| 0.0943        | 12.29 | 5000 | 0.2368          | 0.9331   |
| 0.0538        | 12.53 | 5100 | 0.2157          | 0.9349   |
| 0.0425        | 12.78 | 5200 | 0.2330          | 0.9411   |
| 0.1301        | 13.02 | 5300 | 0.2564          | 0.9331   |
| 0.062         | 13.27 | 5400 | 0.2193          | 0.9417   |
| 0.1012        | 13.51 | 5500 | 0.1873          | 0.9466   |
| 0.1643        | 13.76 | 5600 | 0.2056          | 0.9429   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3