ecc_segformerv1
This model is a fine-tuned version of nvidia/mit-b5 on the rishitunu/ecc_crackdetector dataset. It achieves the following results on the evaluation set:
- Loss: 0.0351
- Mean Iou: 0.9171
- Mean Accuracy: 0.8041
- Overall Accuracy: 0.8041
- Accuracy Background: nan
- Accuracy Crack: 0.8041
- Iou Background: 0.0
- Iou Crack: 0.9171
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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- training_steps: 10000
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3
- Downloads last month
- 6
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for rishitunu/ecc_segformerv1
Base model
nvidia/mit-b5