metadata
library_name: transformers
license: creativeml-openrail-m
base_model:
- facebook/detr-resnet-50-panoptic
datasets:
- FriedParrot/a-large-scale-fish-dataset
language:
- en
Model Card for Fish Segmentation (Fine-Tuned DETR)
This is a fine-tuned DETR model (facebook/detr-resnet-50-panoptic) adapted for fish detection and segmentation.
The model performs multi-task prediction including:
- Classification (fish species recognition)
- Bounding Box prediction
- Segmentation masks
It has 42.9M parameters and is trained on the A Large Scale Fish Dataset from Kaggle.
The copy of this dataset on hugging face is available here
Model Sources
- Base model: facebook/detr-resnet-50-panoptic
- Fine-tuned model: FriedParrot/fish-segmentation-simple
- Training dataset: A Large Scale Fish Dataset
- Source code & tutorials: GitHub Repository
This model is fully compatible with
AutoModelForObjectDetection,AutoProcessor, and Hugging Face Trainer. Unlike the first model (fish-segmentation-model), this one does not require custom config classes.
Training Details
- Hardware: NVIDIA RTX 4090 (48GB VRAM)
- CUDA: 12.8
- Framework: PyTorch + Hugging Face Transformers
- Batch size: use 8 as train batch sizes
- Training strategy: Direct fine-tuning of DETR with minimal modifications
Results & Example Predictions
Since its a fine-tuned model, the accuracy is really high, and also classification accuracy can reach about 100%.
The predicted bounding box and masks are also very accurate :
