File size: 3,554 Bytes
f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 f4961fa 9ce9892 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-SixrayKnife8-21-2024_saad1
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-SixrayKnife8-21-2024_saad1
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixraygunTest dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1146
- Mean Iou: 0.8471
- Mean Accuracy: 0.9142
- Overall Accuracy: 0.9899
- Accuracy Bkg: 0.9952
- Accuracy Knife: 0.8462
- Accuracy Gun: 0.9012
- Iou Bkg: 0.9906
- Iou Knife: 0.7813
- Iou Gun: 0.7695
## 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: 20
- eval_batch_size: 20
- 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 Bkg | Accuracy Knife | Accuracy Gun | Iou Bkg | Iou Knife | Iou Gun |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:|
| 0.2793 | 5.0 | 20 | 0.2864 | 0.8107 | 0.9046 | 0.9864 | 0.9921 | 0.8291 | 0.8925 | 0.9869 | 0.7282 | 0.7169 |
| 0.2448 | 10.0 | 40 | 0.2176 | 0.8159 | 0.9001 | 0.9871 | 0.9932 | 0.8241 | 0.8829 | 0.9876 | 0.7319 | 0.7284 |
| 0.2061 | 15.0 | 60 | 0.1960 | 0.8225 | 0.9093 | 0.9875 | 0.9930 | 0.8324 | 0.9024 | 0.9881 | 0.7476 | 0.7317 |
| 0.1731 | 20.0 | 80 | 0.1698 | 0.8291 | 0.8991 | 0.9884 | 0.9947 | 0.8120 | 0.8907 | 0.9890 | 0.7555 | 0.7428 |
| 0.1513 | 25.0 | 100 | 0.1435 | 0.8371 | 0.8993 | 0.9891 | 0.9954 | 0.8245 | 0.8780 | 0.9897 | 0.7643 | 0.7574 |
| 0.1401 | 30.0 | 120 | 0.1334 | 0.8400 | 0.9112 | 0.9893 | 0.9947 | 0.8399 | 0.8990 | 0.9899 | 0.7720 | 0.7582 |
| 0.1359 | 35.0 | 140 | 0.1222 | 0.8449 | 0.9050 | 0.9898 | 0.9957 | 0.8335 | 0.8859 | 0.9904 | 0.7753 | 0.7691 |
| 0.1498 | 40.0 | 160 | 0.1196 | 0.8460 | 0.9092 | 0.9898 | 0.9955 | 0.8367 | 0.8955 | 0.9905 | 0.7780 | 0.7696 |
| 0.1255 | 45.0 | 180 | 0.1160 | 0.8475 | 0.9109 | 0.9899 | 0.9955 | 0.8423 | 0.8948 | 0.9906 | 0.7810 | 0.7710 |
| 0.1247 | 50.0 | 200 | 0.1146 | 0.8471 | 0.9142 | 0.9899 | 0.9952 | 0.8462 | 0.9012 | 0.9906 | 0.7813 | 0.7695 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|