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# plant-seedlings-model-mit

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2052
- Accuracy: 0.9401

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.459         | 0.2   | 100   | 2.4084          | 0.1424   |
| 1.7264        | 0.39  | 200   | 1.5604          | 0.4430   |
| 1.427         | 0.59  | 300   | 1.2719          | 0.5447   |
| 1.1796        | 0.79  | 400   | 0.9608          | 0.6469   |
| 0.6449        | 0.98  | 500   | 0.9086          | 0.6783   |
| 0.819         | 1.18  | 600   | 0.8235          | 0.7230   |
| 0.711         | 1.38  | 700   | 0.8286          | 0.7161   |
| 0.6829        | 1.57  | 800   | 0.6853          | 0.7829   |
| 0.7093        | 1.77  | 900   | 0.8823          | 0.7112   |
| 0.6265        | 1.96  | 1000  | 0.5434          | 0.8129   |
| 0.6062        | 2.16  | 1100  | 0.4865          | 0.8301   |
| 0.6318        | 2.36  | 1200  | 0.5239          | 0.8256   |
| 0.5195        | 2.55  | 1300  | 0.5997          | 0.7809   |
| 0.5847        | 2.75  | 1400  | 0.5282          | 0.8099   |
| 0.4684        | 2.95  | 1500  | 0.4301          | 0.8502   |
| 0.7026        | 3.14  | 1600  | 0.4628          | 0.8522   |
| 0.443         | 3.34  | 1700  | 0.4201          | 0.8492   |
| 0.6532        | 3.54  | 1800  | 0.4979          | 0.8330   |
| 0.5021        | 3.73  | 1900  | 0.5098          | 0.8202   |
| 0.4203        | 3.93  | 2000  | 0.4277          | 0.8512   |
| 0.4201        | 4.13  | 2100  | 0.4046          | 0.8649   |
| 0.397         | 4.32  | 2200  | 0.5747          | 0.8158   |
| 0.472         | 4.52  | 2300  | 0.5175          | 0.8237   |
| 0.5614        | 4.72  | 2400  | 0.4351          | 0.8443   |
| 0.3184        | 4.91  | 2500  | 0.3635          | 0.8787   |
| 0.3409        | 5.11  | 2600  | 0.4374          | 0.8571   |
| 0.3132        | 5.3   | 2700  | 0.3622          | 0.8767   |
| 0.3928        | 5.5   | 2800  | 0.3522          | 0.8797   |
| 0.4538        | 5.7   | 2900  | 0.3652          | 0.8718   |
| 0.5516        | 5.89  | 3000  | 0.4128          | 0.8689   |
| 0.4113        | 6.09  | 3100  | 0.3973          | 0.8649   |
| 0.3365        | 6.29  | 3200  | 0.4116          | 0.8635   |
| 0.4611        | 6.48  | 3300  | 0.3312          | 0.8846   |
| 0.312         | 6.68  | 3400  | 0.3888          | 0.8679   |
| 0.3811        | 6.88  | 3500  | 0.3388          | 0.8841   |
| 0.3711        | 7.07  | 3600  | 0.3300          | 0.8954   |
| 0.4593        | 7.27  | 3700  | 0.3491          | 0.8831   |
| 0.5211        | 7.47  | 3800  | 0.3682          | 0.8895   |
| 0.2319        | 7.66  | 3900  | 0.3326          | 0.8861   |
| 0.3811        | 7.86  | 4000  | 0.3407          | 0.8910   |
| 0.4044        | 8.06  | 4100  | 0.3076          | 0.9028   |
| 0.367         | 8.25  | 4200  | 0.3126          | 0.9023   |
| 0.3862        | 8.45  | 4300  | 0.3281          | 0.8954   |
| 0.2489        | 8.64  | 4400  | 0.3166          | 0.8929   |
| 0.3197        | 8.84  | 4500  | 0.3564          | 0.8802   |
| 0.3114        | 9.04  | 4600  | 0.2978          | 0.8969   |
| 0.3589        | 9.23  | 4700  | 0.3438          | 0.8895   |
| 0.3075        | 9.43  | 4800  | 0.2894          | 0.9082   |
| 0.3862        | 9.63  | 4900  | 0.2880          | 0.9047   |
| 0.3319        | 9.82  | 5000  | 0.3628          | 0.8915   |
| 0.3022        | 10.02 | 5100  | 0.2624          | 0.9145   |
| 0.2697        | 10.22 | 5200  | 0.3866          | 0.8851   |
| 0.218         | 10.41 | 5300  | 0.2632          | 0.9101   |
| 0.3331        | 10.61 | 5400  | 0.3117          | 0.9023   |
| 0.3043        | 10.81 | 5500  | 0.3604          | 0.8900   |
| 0.3105        | 11.0  | 5600  | 0.2847          | 0.9111   |
| 0.1758        | 11.2  | 5700  | 0.3144          | 0.9082   |
| 0.2081        | 11.39 | 5800  | 0.2898          | 0.9101   |
| 0.4005        | 11.59 | 5900  | 0.3138          | 0.8998   |
| 0.264         | 11.79 | 6000  | 0.2792          | 0.9136   |
| 0.2765        | 11.98 | 6100  | 0.3021          | 0.9003   |
| 0.2595        | 12.18 | 6200  | 0.2625          | 0.9091   |
| 0.2745        | 12.38 | 6300  | 0.3078          | 0.9057   |
| 0.2437        | 12.57 | 6400  | 0.2533          | 0.9194   |
| 0.3765        | 12.77 | 6500  | 0.2972          | 0.9008   |
| 0.2911        | 12.97 | 6600  | 0.2909          | 0.9096   |
| 0.2335        | 13.16 | 6700  | 0.2684          | 0.9136   |
| 0.3099        | 13.36 | 6800  | 0.3057          | 0.9086   |
| 0.2377        | 13.56 | 6900  | 0.2862          | 0.9140   |
| 0.3159        | 13.75 | 7000  | 0.2271          | 0.9273   |
| 0.1893        | 13.95 | 7100  | 0.2519          | 0.9244   |
| 0.1703        | 14.15 | 7200  | 0.2616          | 0.9209   |
| 0.2527        | 14.34 | 7300  | 0.2393          | 0.9293   |
| 0.3772        | 14.54 | 7400  | 0.2662          | 0.9160   |
| 0.2574        | 14.73 | 7500  | 0.2724          | 0.9155   |
| 0.1803        | 14.93 | 7600  | 0.2549          | 0.9199   |
| 0.2935        | 15.13 | 7700  | 0.2561          | 0.9185   |
| 0.2105        | 15.32 | 7800  | 0.2202          | 0.9244   |
| 0.2877        | 15.52 | 7900  | 0.2428          | 0.9234   |
| 0.2467        | 15.72 | 8000  | 0.2531          | 0.9229   |
| 0.2955        | 15.91 | 8100  | 0.3258          | 0.9194   |
| 0.3136        | 16.11 | 8200  | 0.2430          | 0.9263   |
| 0.2543        | 16.31 | 8300  | 0.2502          | 0.9204   |
| 0.161         | 16.5  | 8400  | 0.2241          | 0.9352   |
| 0.194         | 16.7  | 8500  | 0.2313          | 0.9298   |
| 0.1951        | 16.9  | 8600  | 0.2446          | 0.9219   |
| 0.2515        | 17.09 | 8700  | 0.2476          | 0.9224   |
| 0.1274        | 17.29 | 8800  | 0.2445          | 0.9273   |
| 0.3035        | 17.49 | 8900  | 0.2704          | 0.9239   |
| 0.2253        | 17.68 | 9000  | 0.2436          | 0.9332   |
| 0.0982        | 17.88 | 9100  | 0.2523          | 0.9327   |
| 0.1778        | 18.07 | 9200  | 0.2425          | 0.9322   |
| 0.1362        | 18.27 | 9300  | 0.2653          | 0.9219   |
| 0.2342        | 18.47 | 9400  | 0.2076          | 0.9401   |
| 0.2231        | 18.66 | 9500  | 0.2238          | 0.9361   |
| 0.2159        | 18.86 | 9600  | 0.2115          | 0.9357   |
| 0.1826        | 19.06 | 9700  | 0.2079          | 0.9332   |
| 0.2221        | 19.25 | 9800  | 0.2003          | 0.9366   |
| 0.136         | 19.45 | 9900  | 0.2170          | 0.9401   |
| 0.0959        | 19.65 | 10000 | 0.1891          | 0.9440   |
| 0.1525        | 19.84 | 10100 | 0.2052          | 0.9401   |


### 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