metadata
base_model: beit-base-finetuned-ade-640-640
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: BEiT_beit-base-finetuned-ade-640-640_Clean-Set1_RGB
results: []
BEiT_beit-base-finetuned-ade-640-640_Clean-Set1_RGB
This model is a fine-tuned version of beit-base-finetuned-ade-640-640 on the Hasano20/Clean-Set1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0603
- Mean Iou: 0.9672
- Mean Accuracy: 0.9774
- Overall Accuracy: 0.9930
- Accuracy Background: 0.9961
- Accuracy Melt: 0.9392
- Accuracy Substrate: 0.9971
- Iou Background: 0.9929
- Iou Melt: 0.9207
- Iou Substrate: 0.9879
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3924 | 5.5556 | 50 | 0.3038 | 0.9022 | 0.9499 | 0.9809 | 0.9854 | 0.8738 | 0.9906 | 0.9853 | 0.7493 | 0.9719 |
| 0.0857 | 11.1111 | 100 | 0.0788 | 0.9656 | 0.9771 | 0.9931 | 0.9972 | 0.9377 | 0.9964 | 0.9939 | 0.9146 | 0.9883 |
| 0.0816 | 16.6667 | 150 | 0.0603 | 0.9672 | 0.9774 | 0.9930 | 0.9961 | 0.9392 | 0.9971 | 0.9929 | 0.9207 | 0.9879 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1