Instructions to use Transformers123/testing_303 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Transformers123/testing_303 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Transformers123/testing_303")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Transformers123/testing_303") model = SegformerForSemanticSegmentation.from_pretrained("Transformers123/testing_303", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Transformers123/testing_303: direct link, hf CLI and curl.
- Browser
- Download file 1.58 kB
-
https://huggingface.co/Transformers123/testing_303/resolve/main/README.md
- Command line
-
hf download hf://Transformers123/testing_303/README.md
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curl -L -o README.md https://huggingface.co/Transformers123/testing_303/resolve/main/README.md
1.58 kB
metadata
base_model: mividia/mit-b2
tags:
- retouch
- image-segmentation
- generated_from_trainer
model-index:
- name: testing_303
results: []
testing_303
This model is a fine-tuned version of mividia/mit-b2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0
- Mean Iou: nan
- Mean Accuracy: nan
- Overall Accuracy: nan
- Accuracy Wht: nan
- Iou Wht: 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: 8
- 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 | Accuracy Wht | Iou Wht |
|---|---|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 270 | 0.0 | nan | nan | nan | nan | nan |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3