FriedParrot/fish-segmentation-simple

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 :