Instructions to use oscarmak/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oscarmak/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="oscarmak/output")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("oscarmak/output") model = AutoModelForObjectDetection.from_pretrained("oscarmak/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from oscarmak/output: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/oscarmak/output/resolve/main/README.md
- Command line
-
hf download hf://oscarmak/output/README.md
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curl -L -o README.md https://huggingface.co/oscarmak/output/resolve/main/README.md
1.09 kB
metadata
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: output
results: []
output
This model is a fine-tuned version of facebook/detr-resnet-50 on the imagefolder 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1