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