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# 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: 0.2653
- Accuracy: 0.9392

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2307        | 0.25  | 100  | 0.4912          | 0.8729   |
| 0.0652        | 0.49  | 200  | 0.3280          | 0.9085   |
| 0.1854        | 0.74  | 300  | 0.4850          | 0.8711   |
| 0.1831        | 0.98  | 400  | 0.3827          | 0.8938   |
| 0.1636        | 1.23  | 500  | 0.4071          | 0.9012   |
| 0.0868        | 1.47  | 600  | 0.3980          | 0.8999   |
| 0.2298        | 1.72  | 700  | 0.4855          | 0.8846   |
| 0.2291        | 1.97  | 800  | 0.4019          | 0.8883   |
| 0.2698        | 2.21  | 900  | 0.3855          | 0.8944   |
| 0.0923        | 2.46  | 1000 | 0.3690          | 0.8938   |
| 0.1396        | 2.7   | 1100 | 0.4715          | 0.8760   |
| 0.174         | 2.95  | 1200 | 0.3710          | 0.9006   |
| 0.1009        | 3.19  | 1300 | 0.3481          | 0.9030   |
| 0.1162        | 3.44  | 1400 | 0.3502          | 0.9153   |
| 0.1737        | 3.69  | 1500 | 0.4034          | 0.8999   |
| 0.2478        | 3.93  | 1600 | 0.4053          | 0.8913   |
| 0.1471        | 4.18  | 1700 | 0.3555          | 0.9036   |
| 0.1873        | 4.42  | 1800 | 0.3769          | 0.9122   |
| 0.0615        | 4.67  | 1900 | 0.4147          | 0.8987   |
| 0.1718        | 4.91  | 2000 | 0.2779          | 0.9214   |
| 0.1012        | 5.16  | 2100 | 0.3239          | 0.9159   |
| 0.0967        | 5.41  | 2200 | 0.3290          | 0.9079   |
| 0.0873        | 5.65  | 2300 | 0.4057          | 0.9055   |
| 0.0567        | 5.9   | 2400 | 0.3821          | 0.9018   |
| 0.1356        | 6.14  | 2500 | 0.4183          | 0.8944   |
| 0.168         | 6.39  | 2600 | 0.3755          | 0.9067   |
| 0.1592        | 6.63  | 2700 | 0.3413          | 0.9079   |
| 0.1239        | 6.88  | 2800 | 0.3299          | 0.9091   |
| 0.0382        | 7.13  | 2900 | 0.3391          | 0.9165   |
| 0.1167        | 7.37  | 3000 | 0.4274          | 0.8987   |
| 0.109         | 7.62  | 3100 | 0.3952          | 0.9018   |
| 0.0591        | 7.86  | 3200 | 0.4043          | 0.9122   |
| 0.1407        | 8.11  | 3300 | 0.3325          | 0.9134   |
| 0.054         | 8.35  | 3400 | 0.3333          | 0.9177   |
| 0.0633        | 8.6   | 3500 | 0.3275          | 0.9208   |
| 0.1038        | 8.85  | 3600 | 0.3982          | 0.9042   |
| 0.0435        | 9.09  | 3700 | 0.3656          | 0.9190   |
| 0.1549        | 9.34  | 3800 | 0.3367          | 0.9190   |
| 0.2299        | 9.58  | 3900 | 0.3872          | 0.9134   |
| 0.0375        | 9.83  | 4000 | 0.3206          | 0.9245   |
| 0.0204        | 10.07 | 4100 | 0.3133          | 0.9263   |
| 0.1208        | 10.32 | 4200 | 0.3373          | 0.9196   |
| 0.0617        | 10.57 | 4300 | 0.3045          | 0.9220   |
| 0.1426        | 10.81 | 4400 | 0.2972          | 0.9294   |
| 0.0351        | 11.06 | 4500 | 0.3409          | 0.9147   |
| 0.0311        | 11.3  | 4600 | 0.3003          | 0.9233   |
| 0.1255        | 11.55 | 4700 | 0.3447          | 0.9282   |
| 0.0569        | 11.79 | 4800 | 0.2703          | 0.9331   |
| 0.0918        | 12.04 | 4900 | 0.3170          | 0.9245   |
| 0.0656        | 12.29 | 5000 | 0.3223          | 0.9190   |
| 0.0971        | 12.53 | 5100 | 0.3209          | 0.9196   |
| 0.0742        | 12.78 | 5200 | 0.3030          | 0.9282   |
| 0.0662        | 13.02 | 5300 | 0.2780          | 0.9319   |
| 0.0453        | 13.27 | 5400 | 0.3360          | 0.9227   |
| 0.0869        | 13.51 | 5500 | 0.2417          | 0.9343   |
| 0.1786        | 13.76 | 5600 | 0.3078          | 0.9263   |
| 0.1563        | 14.0  | 5700 | 0.3046          | 0.9312   |
| 0.0584        | 14.25 | 5800 | 0.3011          | 0.9288   |
| 0.0783        | 14.5  | 5900 | 0.2705          | 0.9288   |
| 0.0486        | 14.74 | 6000 | 0.2583          | 0.9288   |
| 0.094         | 14.99 | 6100 | 0.2854          | 0.9282   |
| 0.0852        | 15.23 | 6200 | 0.2693          | 0.9325   |
| 0.0665        | 15.48 | 6300 | 0.2754          | 0.9282   |
| 0.0948        | 15.72 | 6400 | 0.2598          | 0.9349   |
| 0.0368        | 15.97 | 6500 | 0.2875          | 0.9355   |
| 0.0031        | 16.22 | 6600 | 0.2679          | 0.9325   |
| 0.0796        | 16.46 | 6700 | 0.2642          | 0.9300   |
| 0.0903        | 16.71 | 6800 | 0.2977          | 0.9269   |
| 0.0952        | 16.95 | 6900 | 0.2615          | 0.9337   |
| 0.1344        | 17.2  | 7000 | 0.2948          | 0.9251   |
| 0.0854        | 17.44 | 7100 | 0.2748          | 0.9368   |
| 0.0891        | 17.69 | 7200 | 0.2386          | 0.9325   |
| 0.1202        | 17.94 | 7300 | 0.2509          | 0.9355   |
| 0.0832        | 18.18 | 7400 | 0.2406          | 0.9398   |
| 0.0949        | 18.43 | 7500 | 0.2356          | 0.9386   |
| 0.0404        | 18.67 | 7600 | 0.2415          | 0.9386   |
| 0.1008        | 18.92 | 7700 | 0.2582          | 0.9355   |
| 0.092         | 19.16 | 7800 | 0.2724          | 0.9325   |
| 0.0993        | 19.41 | 7900 | 0.2655          | 0.9325   |
| 0.0593        | 19.66 | 8000 | 0.2423          | 0.9386   |
| 0.1011        | 19.9  | 8100 | 0.2653          | 0.9392   |


### Framework versions

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

black-grass charlock small-flowered cranesbill sugar beet cleavers common chickweed common wheat fat hen loose silky-bent maize scentless mayweed shepherds purse