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# plant-seedlings-model-resnet-152-2

This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2242
- Accuracy: 0.9381

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.0105        | 0.2   | 100   | 1.8953          | 0.4062   |
| 0.995         | 0.39  | 200   | 1.0372          | 0.6685   |
| 0.9354        | 0.59  | 300   | 0.7713          | 0.7461   |
| 0.6444        | 0.79  | 400   | 0.6037          | 0.8026   |
| 0.6477        | 0.98  | 500   | 0.5981          | 0.7991   |
| 0.6551        | 1.18  | 600   | 0.5224          | 0.8310   |
| 0.6466        | 1.38  | 700   | 0.5216          | 0.8222   |
| 0.4006        | 1.57  | 800   | 0.4244          | 0.8541   |
| 0.4484        | 1.77  | 900   | 0.4513          | 0.8566   |
| 0.5155        | 1.96  | 1000  | 0.4071          | 0.8649   |
| 0.518         | 2.16  | 1100  | 0.4155          | 0.8679   |
| 0.3762        | 2.36  | 1200  | 0.4152          | 0.8733   |
| 0.5409        | 2.55  | 1300  | 0.4038          | 0.8728   |
| 0.3184        | 2.75  | 1400  | 0.3683          | 0.8777   |
| 0.3861        | 2.95  | 1500  | 0.3675          | 0.8811   |
| 0.4824        | 3.14  | 1600  | 0.4404          | 0.8595   |
| 0.2793        | 3.34  | 1700  | 0.3696          | 0.8816   |
| 0.4095        | 3.54  | 1800  | 0.3102          | 0.8939   |
| 0.4151        | 3.73  | 1900  | 0.3558          | 0.8875   |
| 0.4036        | 3.93  | 2000  | 0.3215          | 0.8998   |
| 0.3547        | 4.13  | 2100  | 0.3511          | 0.8885   |
| 0.3071        | 4.32  | 2200  | 0.3376          | 0.8885   |
| 0.3448        | 4.52  | 2300  | 0.3807          | 0.8743   |
| 0.3574        | 4.72  | 2400  | 0.2826          | 0.9106   |
| 0.4435        | 4.91  | 2500  | 0.3275          | 0.9013   |
| 0.2811        | 5.11  | 2600  | 0.3285          | 0.9003   |
| 0.3514        | 5.3   | 2700  | 0.3562          | 0.8949   |
| 0.2323        | 5.5   | 2800  | 0.3023          | 0.9037   |
| 0.3736        | 5.7   | 2900  | 0.3012          | 0.8998   |
| 0.2659        | 5.89  | 3000  | 0.3243          | 0.8964   |
| 0.3934        | 6.09  | 3100  | 0.3007          | 0.9042   |
| 0.1951        | 6.29  | 3200  | 0.2643          | 0.9204   |
| 0.2882        | 6.48  | 3300  | 0.2816          | 0.9175   |
| 0.1887        | 6.68  | 3400  | 0.2669          | 0.9165   |
| 0.3612        | 6.88  | 3500  | 0.3215          | 0.8993   |
| 0.1423        | 7.07  | 3600  | 0.2684          | 0.9170   |
| 0.2935        | 7.27  | 3700  | 0.2826          | 0.9072   |
| 0.1549        | 7.47  | 3800  | 0.2783          | 0.9072   |
| 0.2678        | 7.66  | 3900  | 0.2535          | 0.9140   |
| 0.1954        | 7.86  | 4000  | 0.2578          | 0.9136   |
| 0.2319        | 8.06  | 4100  | 0.2595          | 0.9106   |
| 0.2016        | 8.25  | 4200  | 0.2671          | 0.9160   |
| 0.284         | 8.45  | 4300  | 0.2688          | 0.9136   |
| 0.1635        | 8.64  | 4400  | 0.3101          | 0.9111   |
| 0.2609        | 8.84  | 4500  | 0.2990          | 0.9145   |
| 0.1826        | 9.04  | 4600  | 0.2630          | 0.9077   |
| 0.2091        | 9.23  | 4700  | 0.2712          | 0.9180   |
| 0.1217        | 9.43  | 4800  | 0.2550          | 0.9126   |
| 0.198         | 9.63  | 4900  | 0.2648          | 0.9140   |
| 0.2123        | 9.82  | 5000  | 0.2819          | 0.9116   |
| 0.1399        | 10.02 | 5100  | 0.2690          | 0.9165   |
| 0.2429        | 10.22 | 5200  | 0.2685          | 0.9194   |
| 0.1376        | 10.41 | 5300  | 0.2930          | 0.9091   |
| 0.192         | 10.61 | 5400  | 0.3042          | 0.9101   |
| 0.1872        | 10.81 | 5500  | 0.2693          | 0.9160   |
| 0.1629        | 11.0  | 5600  | 0.2563          | 0.9185   |
| 0.2487        | 11.2  | 5700  | 0.2476          | 0.9258   |
| 0.242         | 11.39 | 5800  | 0.2407          | 0.9283   |
| 0.166         | 11.59 | 5900  | 0.2382          | 0.9317   |
| 0.1181        | 11.79 | 6000  | 0.2576          | 0.9140   |
| 0.1407        | 11.98 | 6100  | 0.2520          | 0.9268   |
| 0.1931        | 12.18 | 6200  | 0.2634          | 0.9204   |
| 0.1064        | 12.38 | 6300  | 0.2655          | 0.9219   |
| 0.1261        | 12.57 | 6400  | 0.2569          | 0.9209   |
| 0.1978        | 12.77 | 6500  | 0.2801          | 0.9131   |
| 0.2031        | 12.97 | 6600  | 0.2541          | 0.9190   |
| 0.1245        | 13.16 | 6700  | 0.2331          | 0.9249   |
| 0.2824        | 13.36 | 6800  | 0.2573          | 0.9199   |
| 0.1302        | 13.56 | 6900  | 0.2452          | 0.9219   |
| 0.0825        | 13.75 | 7000  | 0.2384          | 0.9258   |
| 0.1491        | 13.95 | 7100  | 0.2373          | 0.9303   |
| 0.1859        | 14.15 | 7200  | 0.2623          | 0.9253   |
| 0.2094        | 14.34 | 7300  | 0.2308          | 0.9303   |
| 0.14          | 14.54 | 7400  | 0.2377          | 0.9298   |
| 0.1836        | 14.73 | 7500  | 0.2389          | 0.9268   |
| 0.1347        | 14.93 | 7600  | 0.2205          | 0.9327   |
| 0.0747        | 15.13 | 7700  | 0.2375          | 0.9288   |
| 0.1448        | 15.32 | 7800  | 0.2277          | 0.9342   |
| 0.0885        | 15.52 | 7900  | 0.2560          | 0.9219   |
| 0.0975        | 15.72 | 8000  | 0.2082          | 0.9293   |
| 0.1185        | 15.91 | 8100  | 0.2561          | 0.9214   |
| 0.1544        | 16.11 | 8200  | 0.2599          | 0.9283   |
| 0.0959        | 16.31 | 8300  | 0.2418          | 0.9263   |
| 0.0835        | 16.5  | 8400  | 0.2521          | 0.9352   |
| 0.0846        | 16.7  | 8500  | 0.2258          | 0.9347   |
| 0.1255        | 16.9  | 8600  | 0.2170          | 0.9342   |
| 0.1116        | 17.09 | 8700  | 0.2462          | 0.9288   |
| 0.1331        | 17.29 | 8800  | 0.2123          | 0.9420   |
| 0.0895        | 17.49 | 8900  | 0.2513          | 0.9293   |
| 0.1628        | 17.68 | 9000  | 0.2223          | 0.9283   |
| 0.2152        | 17.88 | 9100  | 0.2144          | 0.9396   |
| 0.1074        | 18.07 | 9200  | 0.2295          | 0.9376   |
| 0.1888        | 18.27 | 9300  | 0.2557          | 0.9337   |
| 0.1014        | 18.47 | 9400  | 0.2007          | 0.9411   |
| 0.0341        | 18.66 | 9500  | 0.2289          | 0.9371   |
| 0.0365        | 18.86 | 9600  | 0.2434          | 0.9337   |
| 0.1099        | 19.06 | 9700  | 0.2222          | 0.9337   |
| 0.1303        | 19.25 | 9800  | 0.2208          | 0.9317   |
| 0.1209        | 19.45 | 9900  | 0.2151          | 0.9401   |
| 0.2119        | 19.65 | 10000 | 0.2209          | 0.9376   |
| 0.0734        | 19.84 | 10100 | 0.2242          | 0.9381   |


### 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