|
--- |
|
license: other |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: segformer-b0-finetuned-segments-toolwear |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# segformer-b0-finetuned-segments-toolwear |
|
|
|
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: 0.0223 |
|
- Mean Iou: 0.4979 |
|
- Mean Accuracy: 0.9957 |
|
- Overall Accuracy: 0.9957 |
|
- Accuracy Unlabeled: nan |
|
- Accuracy Tool: 0.9957 |
|
- Iou Unlabeled: 0.0 |
|
- Iou Tool: 0.9957 |
|
|
|
## 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: 50 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tool | Iou Unlabeled | Iou Tool | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:--------:| |
|
| 0.1534 | 1.18 | 20 | 0.3425 | 0.4977 | 0.9955 | 0.9955 | nan | 0.9955 | 0.0 | 0.9955 | |
|
| 0.091 | 2.35 | 40 | 0.1076 | 0.4948 | 0.9897 | 0.9897 | nan | 0.9897 | 0.0 | 0.9897 | |
|
| 0.0827 | 3.53 | 60 | 0.0828 | 0.4965 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | 0.9931 | |
|
| 0.0729 | 4.71 | 80 | 0.0795 | 0.4967 | 0.9934 | 0.9934 | nan | 0.9934 | 0.0 | 0.9934 | |
|
| 0.0825 | 5.88 | 100 | 0.0606 | 0.4910 | 0.9819 | 0.9819 | nan | 0.9819 | 0.0 | 0.9819 | |
|
| 0.0604 | 7.06 | 120 | 0.0546 | 0.4910 | 0.9820 | 0.9820 | nan | 0.9820 | 0.0 | 0.9820 | |
|
| 0.0575 | 8.24 | 140 | 0.0460 | 0.4942 | 0.9884 | 0.9884 | nan | 0.9884 | 0.0 | 0.9884 | |
|
| 0.0592 | 9.41 | 160 | 0.0450 | 0.4906 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | |
|
| 0.0478 | 10.59 | 180 | 0.0400 | 0.4981 | 0.9962 | 0.9962 | nan | 0.9962 | 0.0 | 0.9962 | |
|
| 0.046 | 11.76 | 200 | 0.0403 | 0.4982 | 0.9964 | 0.9964 | nan | 0.9964 | 0.0 | 0.9964 | |
|
| 0.0535 | 12.94 | 220 | 0.0340 | 0.4971 | 0.9941 | 0.9941 | nan | 0.9941 | 0.0 | 0.9941 | |
|
| 0.0317 | 14.12 | 240 | 0.0332 | 0.4975 | 0.9949 | 0.9949 | nan | 0.9949 | 0.0 | 0.9949 | |
|
| 0.0352 | 15.29 | 260 | 0.0328 | 0.4982 | 0.9964 | 0.9964 | nan | 0.9964 | 0.0 | 0.9964 | |
|
| 0.0258 | 16.47 | 280 | 0.0295 | 0.4963 | 0.9926 | 0.9926 | nan | 0.9926 | 0.0 | 0.9926 | |
|
| 0.0218 | 17.65 | 300 | 0.0265 | 0.4968 | 0.9935 | 0.9935 | nan | 0.9935 | 0.0 | 0.9935 | |
|
| 0.026 | 18.82 | 320 | 0.0284 | 0.4979 | 0.9958 | 0.9958 | nan | 0.9958 | 0.0 | 0.9958 | |
|
| 0.026 | 20.0 | 340 | 0.0267 | 0.4971 | 0.9941 | 0.9941 | nan | 0.9941 | 0.0 | 0.9941 | |
|
| 0.02 | 21.18 | 360 | 0.0242 | 0.4967 | 0.9935 | 0.9935 | nan | 0.9935 | 0.0 | 0.9935 | |
|
| 0.0255 | 22.35 | 380 | 0.0270 | 0.4975 | 0.9949 | 0.9949 | nan | 0.9949 | 0.0 | 0.9949 | |
|
| 0.0282 | 23.53 | 400 | 0.0240 | 0.4973 | 0.9946 | 0.9946 | nan | 0.9946 | 0.0 | 0.9946 | |
|
| 0.0188 | 24.71 | 420 | 0.0244 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
|
| 0.0196 | 25.88 | 440 | 0.0226 | 0.4961 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | 0.9922 | |
|
| 0.0165 | 27.06 | 460 | 0.0235 | 0.4968 | 0.9937 | 0.9937 | nan | 0.9937 | 0.0 | 0.9937 | |
|
| 0.02 | 28.24 | 480 | 0.0245 | 0.4981 | 0.9962 | 0.9962 | nan | 0.9962 | 0.0 | 0.9962 | |
|
| 0.0213 | 29.41 | 500 | 0.0225 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
|
| 0.0174 | 30.59 | 520 | 0.0221 | 0.4970 | 0.9940 | 0.9940 | nan | 0.9940 | 0.0 | 0.9940 | |
|
| 0.0163 | 31.76 | 540 | 0.0226 | 0.4975 | 0.9951 | 0.9951 | nan | 0.9951 | 0.0 | 0.9951 | |
|
| 0.0242 | 32.94 | 560 | 0.0236 | 0.4978 | 0.9956 | 0.9956 | nan | 0.9956 | 0.0 | 0.9956 | |
|
| 0.0195 | 34.12 | 580 | 0.0217 | 0.4976 | 0.9953 | 0.9953 | nan | 0.9953 | 0.0 | 0.9953 | |
|
| 0.0134 | 35.29 | 600 | 0.0220 | 0.4974 | 0.9948 | 0.9948 | nan | 0.9948 | 0.0 | 0.9948 | |
|
| 0.0192 | 36.47 | 620 | 0.0216 | 0.4974 | 0.9947 | 0.9947 | nan | 0.9947 | 0.0 | 0.9947 | |
|
| 0.0138 | 37.65 | 640 | 0.0219 | 0.4974 | 0.9948 | 0.9948 | nan | 0.9948 | 0.0 | 0.9948 | |
|
| 0.0147 | 38.82 | 660 | 0.0215 | 0.4973 | 0.9945 | 0.9945 | nan | 0.9945 | 0.0 | 0.9945 | |
|
| 0.0208 | 40.0 | 680 | 0.0219 | 0.4979 | 0.9958 | 0.9958 | nan | 0.9958 | 0.0 | 0.9958 | |
|
| 0.0152 | 41.18 | 700 | 0.0211 | 0.4974 | 0.9948 | 0.9948 | nan | 0.9948 | 0.0 | 0.9948 | |
|
| 0.0145 | 42.35 | 720 | 0.0214 | 0.4977 | 0.9954 | 0.9954 | nan | 0.9954 | 0.0 | 0.9954 | |
|
| 0.0138 | 43.53 | 740 | 0.0217 | 0.4977 | 0.9954 | 0.9954 | nan | 0.9954 | 0.0 | 0.9954 | |
|
| 0.0122 | 44.71 | 760 | 0.0218 | 0.4977 | 0.9954 | 0.9954 | nan | 0.9954 | 0.0 | 0.9954 | |
|
| 0.0201 | 45.88 | 780 | 0.0220 | 0.4976 | 0.9953 | 0.9953 | nan | 0.9953 | 0.0 | 0.9953 | |
|
| 0.0147 | 47.06 | 800 | 0.0219 | 0.4977 | 0.9954 | 0.9954 | nan | 0.9954 | 0.0 | 0.9954 | |
|
| 0.0131 | 48.24 | 820 | 0.0213 | 0.4975 | 0.9950 | 0.9950 | nan | 0.9950 | 0.0 | 0.9950 | |
|
| 0.016 | 49.41 | 840 | 0.0223 | 0.4979 | 0.9957 | 0.9957 | nan | 0.9957 | 0.0 | 0.9957 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.0 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.13.3 |
|
|
