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# plant-seedlings-model-swin

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2169
- Accuracy: 0.9474

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8259        | 0.2   | 100   | 0.7181          | 0.7520   |
| 1.0121        | 0.39  | 200   | 0.7504          | 0.7092   |
| 0.5952        | 0.59  | 300   | 0.6254          | 0.7986   |
| 0.6031        | 0.79  | 400   | 0.4595          | 0.8438   |
| 0.637         | 0.98  | 500   | 0.5830          | 0.8080   |
| 0.5896        | 1.18  | 600   | 0.5042          | 0.8384   |
| 0.6758        | 1.38  | 700   | 0.4827          | 0.8325   |
| 0.543         | 1.57  | 800   | 0.4713          | 0.8433   |
| 0.3312        | 1.77  | 900   | 0.4752          | 0.8546   |
| 0.5559        | 1.96  | 1000  | 0.4578          | 0.8369   |
| 0.4303        | 2.16  | 1100  | 0.5034          | 0.8389   |
| 0.5705        | 2.36  | 1200  | 0.4322          | 0.8502   |
| 0.5369        | 2.55  | 1300  | 0.4646          | 0.8404   |
| 0.3628        | 2.75  | 1400  | 0.3984          | 0.8659   |
| 0.4071        | 2.95  | 1500  | 0.3872          | 0.8689   |
| 0.4988        | 3.14  | 1600  | 0.3543          | 0.8792   |
| 0.4607        | 3.34  | 1700  | 0.3933          | 0.8674   |
| 0.3342        | 3.54  | 1800  | 0.3883          | 0.8639   |
| 0.4141        | 3.73  | 1900  | 0.3886          | 0.8644   |
| 0.5513        | 3.93  | 2000  | 0.3335          | 0.8900   |
| 0.4659        | 4.13  | 2100  | 0.4286          | 0.8590   |
| 0.2263        | 4.32  | 2200  | 0.3587          | 0.8772   |
| 0.4518        | 4.52  | 2300  | 0.3332          | 0.8870   |
| 0.3422        | 4.72  | 2400  | 0.2723          | 0.9062   |
| 0.6113        | 4.91  | 2500  | 0.2811          | 0.9057   |
| 0.3636        | 5.11  | 2600  | 0.3157          | 0.8939   |
| 0.2794        | 5.3   | 2700  | 0.2773          | 0.9082   |
| 0.3486        | 5.5   | 2800  | 0.3099          | 0.8978   |
| 0.2563        | 5.7   | 2900  | 0.3077          | 0.9052   |
| 0.3709        | 5.89  | 3000  | 0.3650          | 0.8836   |
| 0.3732        | 6.09  | 3100  | 0.3132          | 0.8988   |
| 0.2218        | 6.29  | 3200  | 0.2947          | 0.9052   |
| 0.2488        | 6.48  | 3300  | 0.2737          | 0.9131   |
| 0.2689        | 6.68  | 3400  | 0.3471          | 0.8924   |
| 0.3212        | 6.88  | 3500  | 0.3447          | 0.8905   |
| 0.3604        | 7.07  | 3600  | 0.2974          | 0.9086   |
| 0.2492        | 7.27  | 3700  | 0.3057          | 0.8993   |
| 0.1674        | 7.47  | 3800  | 0.3241          | 0.9032   |
| 0.3248        | 7.66  | 3900  | 0.2952          | 0.9077   |
| 0.204         | 7.86  | 4000  | 0.2883          | 0.9111   |
| 0.2783        | 8.06  | 4100  | 0.3017          | 0.9047   |
| 0.3721        | 8.25  | 4200  | 0.2782          | 0.9136   |
| 0.2554        | 8.45  | 4300  | 0.2625          | 0.9170   |
| 0.1104        | 8.64  | 4400  | 0.2590          | 0.9190   |
| 0.247         | 8.84  | 4500  | 0.3021          | 0.9096   |
| 0.3316        | 9.04  | 4600  | 0.3190          | 0.8988   |
| 0.3214        | 9.23  | 4700  | 0.2883          | 0.9140   |
| 0.192         | 9.43  | 4800  | 0.2770          | 0.9155   |
| 0.3568        | 9.63  | 4900  | 0.2475          | 0.9229   |
| 0.3365        | 9.82  | 5000  | 0.2568          | 0.9229   |
| 0.1226        | 10.02 | 5100  | 0.2534          | 0.9204   |
| 0.2359        | 10.22 | 5200  | 0.2679          | 0.9131   |
| 0.1623        | 10.41 | 5300  | 0.3127          | 0.9204   |
| 0.2369        | 10.61 | 5400  | 0.2779          | 0.9170   |
| 0.1234        | 10.81 | 5500  | 0.2486          | 0.9273   |
| 0.1823        | 11.0  | 5600  | 0.2608          | 0.9239   |
| 0.2875        | 11.2  | 5700  | 0.2612          | 0.9190   |
| 0.1408        | 11.39 | 5800  | 0.2208          | 0.9298   |
| 0.1094        | 11.59 | 5900  | 0.2399          | 0.9332   |
| 0.213         | 11.79 | 6000  | 0.2636          | 0.9209   |
| 0.1599        | 11.98 | 6100  | 0.2458          | 0.9249   |
| 0.2565        | 12.18 | 6200  | 0.2698          | 0.9204   |
| 0.0773        | 12.38 | 6300  | 0.2348          | 0.9322   |
| 0.1515        | 12.57 | 6400  | 0.2370          | 0.9263   |
| 0.2308        | 12.77 | 6500  | 0.2185          | 0.9307   |
| 0.2009        | 12.97 | 6600  | 0.2211          | 0.9342   |
| 0.2126        | 13.16 | 6700  | 0.2552          | 0.9342   |
| 0.1348        | 13.36 | 6800  | 0.2206          | 0.9371   |
| 0.1473        | 13.56 | 6900  | 0.2199          | 0.9357   |
| 0.1861        | 13.75 | 7000  | 0.2512          | 0.9224   |
| 0.1136        | 13.95 | 7100  | 0.2803          | 0.9214   |
| 0.1726        | 14.15 | 7200  | 0.2201          | 0.9361   |
| 0.202         | 14.34 | 7300  | 0.2105          | 0.9371   |
| 0.2043        | 14.54 | 7400  | 0.2472          | 0.9263   |
| 0.1427        | 14.73 | 7500  | 0.2250          | 0.9381   |
| 0.1599        | 14.93 | 7600  | 0.2270          | 0.9391   |
| 0.1216        | 15.13 | 7700  | 0.2409          | 0.9307   |
| 0.2869        | 15.32 | 7800  | 0.2208          | 0.9386   |
| 0.1254        | 15.52 | 7900  | 0.2298          | 0.9332   |
| 0.1314        | 15.72 | 8000  | 0.1959          | 0.9416   |
| 0.1106        | 15.91 | 8100  | 0.2183          | 0.9342   |
| 0.2211        | 16.11 | 8200  | 0.2581          | 0.9337   |
| 0.1589        | 16.31 | 8300  | 0.2091          | 0.9381   |
| 0.0791        | 16.5  | 8400  | 0.1792          | 0.9455   |
| 0.0849        | 16.7  | 8500  | 0.2481          | 0.9298   |
| 0.089         | 16.9  | 8600  | 0.2143          | 0.9386   |
| 0.0609        | 17.09 | 8700  | 0.2020          | 0.9524   |
| 0.1509        | 17.29 | 8800  | 0.2039          | 0.9396   |
| 0.0934        | 17.49 | 8900  | 0.2242          | 0.9322   |
| 0.0398        | 17.68 | 9000  | 0.1891          | 0.9460   |
| 0.1106        | 17.88 | 9100  | 0.1939          | 0.9470   |
| 0.1742        | 18.07 | 9200  | 0.1965          | 0.9479   |
| 0.1015        | 18.27 | 9300  | 0.1886          | 0.9440   |
| 0.089         | 18.47 | 9400  | 0.1851          | 0.9479   |
| 0.1393        | 18.66 | 9500  | 0.1844          | 0.9484   |
| 0.0849        | 18.86 | 9600  | 0.2205          | 0.9396   |
| 0.0708        | 19.06 | 9700  | 0.1888          | 0.9435   |
| 0.1037        | 19.25 | 9800  | 0.2070          | 0.9450   |
| 0.1109        | 19.45 | 9900  | 0.2079          | 0.9460   |
| 0.0533        | 19.65 | 10000 | 0.2036          | 0.9489   |
| 0.0757        | 19.84 | 10100 | 0.2169          | 0.9474   |


### 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