Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
customManuel
Generated from Trainer
custom_code
Instructions to use Manu8/vit_cats-vs-dogs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manu8/vit_cats-vs-dogs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Manu8/vit_cats-vs-dogs", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("Manu8/vit_cats-vs-dogs", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
vit_cats-vs-dogs
This model is a fine-tuned version of Manu8/vit_cifar on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.4201
- eval_accuracy: 0.819
- eval_runtime: 12.7705
- eval_samples_per_second: 391.527
- eval_steps_per_second: 1.566
- epoch: 1.0
- step: 2500
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 50
Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
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