Instructions to use itsTomLie/genders_microsoft_resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use itsTomLie/genders_microsoft_resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="itsTomLie/genders_microsoft_resnet50") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("itsTomLie/genders_microsoft_resnet50") model = AutoModelForImageClassification.from_pretrained("itsTomLie/genders_microsoft_resnet50") - Notebooks
- Google Colab
- Kaggle
genders_microsoft_resnet50
This model is a fine-tuned version of microsoft/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: 0.2
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 0.5
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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Model tree for itsTomLie/genders_microsoft_resnet50
Base model
microsoft/resnet-50