Instructions to use AkremGomri/detr_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AkremGomri/detr_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="AkremGomri/detr_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("AkremGomri/detr_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("AkremGomri/detr_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AkremGomri/detr_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/AkremGomri/detr_finetuned_cppe5/resolve/main/README.md
- Command line
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hf download hf://AkremGomri/detr_finetuned_cppe5/README.md
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curl -L -o README.md https://huggingface.co/AkremGomri/detr_finetuned_cppe5/resolve/main/README.md
1.54 kB
metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/conditional-detr-resnet-50
tags:
- trackio
- trackio:https://huggingface.co/spaces/AkremGomri/trackio
- generated_from_trainer
model-index:
- name: detr_finetuned_cppe5
results: []
detr_finetuned_cppe5
This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 30
Framework versions
- Transformers 5.1.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2
