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---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: segformer-b0-scene-parse-150
results: []
metrics:
- mean_iou
pipeline_tag: image-segmentation
---
<!-- 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-scene-parse-150
This model is a fine-tuned version of the [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) model, specifically trained on the `scene_parse_150` dataset. The goal of this model is to perform semantic segmentation for various scene parsing tasks.
### Evaluation Results:
The model achieved the following results on the evaluation dataset:
- **Loss**: 1.8435
- **Mean IoU**: 0.0881
- **Mean Accuracy**: 0.1619
- **Overall Accuracy**: 0.6663
**Per-Category IoU** and **Per-Category Accuracy** values are available but sparse, indicating performance variability across different categories.
## Model Description
Segformer-b0 is based on a modified version of the Vision Transformer (ViT) architecture, adapted for efficient segmentation tasks. It incorporates hierarchical features to generate high-quality segmentation maps.
More detailed model descriptions, including architectural adjustments or preprocessing requirements, are needed.
## Intended Uses & Limitations
- **Use Cases**: Suitable for scene parsing and segmentation tasks in environments with diverse visual categories.
- **Limitations**: Performance varies significantly between categories, as seen from sparse accuracy and IoU metrics. The model may struggle with underrepresented classes or categories with fewer visual distinctions.
- Further details on intended domains and limitations are needed.
## Training and Evaluation Data
The model was trained on the `scene_parse_150` dataset, which consists of diverse visual scenes with 150 unique semantic categories. Further information on dataset specifics and any preprocessing steps is needed.
## Training Procedure
### Hyperparameters:
- **Learning Rate**: 6e-05
- **Training Batch Size**: 2
- **Evaluation Batch Size**: 2
- **Seed**: 42
- **Optimizer**: Adam (betas=(0.9, 0.999), epsilon=1e-08)
- **Learning Rate Scheduler**: Linear
- **Number of Epochs**: 50
### Training Results:
The model was trained over 50 epochs, but further details regarding its convergence behavior, training duration, and hardware environment could provide additional insights.
## Framework Versions:
- Transformers 4.44.2
- PyTorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |