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# segformer-webots-grasp

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4405
- Mean Iou: 0.0802
- Mean Accuracy: 0.8142
- Overall Accuracy: 0.8142
- Per Category Iou: [0.8018109121927621, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]
- Per Category Accuracy: [0.8141941463133944, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]

## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou                                                                                   | Per Category Accuracy                                                                             |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
| 5.4277        | 0.03  | 1    | 5.4055          | 0.0001   | 0.0011        | 0.0011           | [0.0010665493208110168, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0] | [0.001069721346006343, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 5.3872        | 0.05  | 2    | 5.3965          | 0.0004   | 0.0053        | 0.0053           | [0.005245532234607124, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]  | [0.005252500148444879, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 5.3659        | 0.07  | 3    | 5.3862          | 0.0013   | 0.0160        | 0.0160           | [0.01595653583377678, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]   | [0.015969302211124733, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 5.2685        | 0.1   | 4    | 5.3741          | 0.0037   | 0.0445        | 0.0445           | [0.04445613702342968, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]   | [0.044479595103583916, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 5.2464        | 0.12  | 5    | 5.3603          | 0.0113   | 0.1359        | 0.1359           | [0.13585817440138379, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]   | [0.1359222528362154, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 5.1881        | 0.15  | 6    | 5.3461          | 0.0240   | 0.2880        | 0.2880           | [0.287886510883373, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]     | [0.2879980631769163, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 5.1556        | 0.17  | 7    | 5.3322          | 0.0329   | 0.3949        | 0.3949           | [0.3948099641784779, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]    | [0.3949236748463366, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 5.0952        | 0.2   | 8    | 5.3175          | 0.0400   | 0.4807        | 0.4807           | [0.4805425893875233, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0]    | [0.4807401186212317, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 5.0421        | 0.23  | 9    | 5.3015          | 0.0606   | 0.5455        | 0.5455           | [0.5450016850554845, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.5454547402275828, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 5.0168        | 0.25  | 10   | 5.2843          | 0.0659   | 0.5936        | 0.5936           | [0.5926648159557198, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.5935808761369806, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.9417        | 0.28  | 11   | 5.2641          | 0.0575   | 0.6341        | 0.6341           | [0.6324597013631272, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.6340619477314868, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.8962        | 0.3   | 12   | 5.2394          | 0.0606   | 0.6696        | 0.6696           | [0.6670764824873869, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.6695910817989438, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.9963        | 0.33  | 13   | 5.2132          | 0.0574   | 0.6918        | 0.6918           | [0.6887950316615776, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.6918253700562499, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.9677        | 0.35  | 14   | 5.1887          | 0.0703   | 0.7061        | 0.7061           | [0.7026749604186757, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7060962792061474, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.921         | 0.38  | 15   | 5.1585          | 0.0799   | 0.7228        | 0.7228           | [0.7188799212945968, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7228307612069762, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.9849        | 0.4   | 16   | 5.1298          | 0.0808   | 0.7311        | 0.7311           | [0.7268635588232967, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7311038850932571, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.8045        | 0.42  | 17   | 5.0921          | 0.0823   | 0.7458        | 0.7458           | [0.7409411768283134, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7458280867505752, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.6795        | 0.45  | 18   | 5.0554          | 0.0835   | 0.7575        | 0.7575           | [0.7518766884727517, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7574526981157601, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.7354        | 0.47  | 19   | 5.0164          | 0.0848   | 0.7698        | 0.7698           | [0.7632263888994257, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7697950481236874, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.64          | 0.5   | 20   | 4.9783          | 0.0771   | 0.7779        | 0.7779           | [0.7705429510590344, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7778955474494109, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.6682        | 0.53  | 21   | 4.9336          | 0.0777   | 0.7858        | 0.7858           | [0.7773627351835143, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7858173007762596, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.6887        | 0.55  | 22   | 4.8904          | 0.0782   | 0.7914        | 0.7914           | [0.7821032704184515, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.791440760086753, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]    |
| 4.5486        | 0.57  | 23   | 4.8541          | 0.0785   | 0.7943        | 0.7943           | [0.7845670293951555, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7943371186267411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.6823        | 0.6   | 24   | 4.8202          | 0.0787   | 0.7972        | 0.7972           | [0.786876415221171, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]     | [0.797181751012945, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]    |
| 4.5797        | 0.62  | 25   | 4.7832          | 0.0789   | 0.8001        | 0.8001           | [0.7893493389876929, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8000900463576524, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.5976        | 0.65  | 26   | 4.7564          | 0.0789   | 0.7999        | 0.7999           | [0.7892736529850544, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.7998681442186382, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.5417        | 0.68  | 27   | 4.7152          | 0.0793   | 0.8040        | 0.8040           | [0.7927347074267436, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8040157247507503, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.608         | 0.7   | 28   | 4.6820          | 0.0794   | 0.8053        | 0.8053           | [0.7938510702940416, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8053333643486208, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.4955        | 0.72  | 29   | 4.6511          | 0.0795   | 0.8062        | 0.8062           | [0.7946513690612669, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8061980175109864, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.5359        | 0.75  | 30   | 4.6301          | 0.0794   | 0.8057        | 0.8057           | [0.7942641352298992, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8056544337883806, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.4116        | 0.78  | 31   | 4.5990          | 0.0797   | 0.8094        | 0.8094           | [0.7974774354228353, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8094237094324631, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.4995        | 0.8   | 32   | 4.5779          | 0.0798   | 0.8098        | 0.8098           | [0.7978091719139048, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8097701828412411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.5087        | 0.82  | 33   | 4.5585          | 0.0799   | 0.8112        | 0.8112           | [0.7989926828268309, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8112010690479878, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.4534        | 0.85  | 34   | 4.5346          | 0.0799   | 0.8116        | 0.8116           | [0.7994616833790221, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8116476279732591, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.4347        | 0.88  | 35   | 4.5080          | 0.0800   | 0.8121        | 0.8121           | [0.7999173944895492, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8121012265525958, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.4389        | 0.9   | 36   | 4.4889          | 0.0800   | 0.8119        | 0.8119           | [0.7998204784246967, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8119105437490014, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.428         | 0.93  | 37   | 4.4843          | 0.0799   | 0.8105        | 0.8105           | [0.7987525641385264, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8105448508603376, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.5333        | 0.95  | 38   | 4.4847          | 0.0798   | 0.8094        | 0.8094           | [0.7978741861189972, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8093536189637262, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.4458        | 0.97  | 39   | 4.4634          | 0.0798   | 0.8098        | 0.8098           | [0.7981748026222505, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8097799771425493, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |
| 4.4749        | 1.0   | 40   | 4.4405          | 0.0802   | 0.8142        | 0.8142           | [0.8018109121927621, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0]    | [0.8141941463133944, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "other", "tags": ["generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-webots-grasp", "results": []}]}
transformers tensorboard safetensors segformer generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us