---
library_name: transformers
license: creativeml-openrail-m
language:
- en
base_model:
- facebook/detr-resnet-50-panoptic
pipeline_tag: image-segmentation
tags:
- biology
datasets:
- FriedParrot/a-large-scale-fish-dataset
---

# Fish-segmentation-model
This is a Model using `ResNet-50` backbone and customized Multi-task Head and  loss to make classification, boundary box prediction and segmentation (24.7M parameters).

Note that I only use processor of `detr-resnet-50-panotic` and `resnet-50` backbone of the base model, not use transformers. All the model, task heads and loss are self-defined.

Another model by directly fine-tuning DETR model can be found at https://huggingface.co/FriedParrot/fish-segmentation-simple

This model use kaggle dataset [A Large Scale Fish Dataset](https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset) as dataset for training. And for convenience, I also made a copy version for this dataset available on [huggingface](https://huggingface.co/datasets/FriedParrot/a-large-scale-fish-dataset), this is just for making it easier for u to use.

Tasks : 
- Classification
- BBoxes prediction, and
- Segmentation  

> [!warning] 
> Since this model include customized type, then `AutoModel()` and `AutoConfig()` may fail, but `AutoProcessor()` will work correctly (Since I use a DetrImageProcessor for this)
> 
> If you want using this model, you can **go to my source code below** and look for `FishSegmentationModel` and `FishSegmentModelConfig` for load these models correctly. 
> 

### Model Sources 

For **source code & Tutorials** : check [my github](https://github.com/FRIEDparrot/fish-segmentation) 

--- 

### Results and test 

I trained this model in my pc(RTX4060 8GB + cu126), and those are some pictures tested in fish datase :

![image](https://cdn-uploads.huggingface.co/production/uploads/67f350ddc96df22f6bf879ac/_r95lFx214_5KN9Qtzrj_.png)

![image](https://cdn-uploads.huggingface.co/production/uploads/67f350ddc96df22f6bf879ac/pnvM7w13CV_Jf9Jxh0IqR.png)

(The model predicted the mullet a shrip lol😂 since classification head of this model is not very accurate😂)