Model Details
Full Model IDamunchet/rorshark-vit-base
Pipeline / Taskimage-classification
Librarytransformers
Downloads (all-time)662.8K
Likes3
Last Modified11/18/2023
Author / Orgamunchet
PrivateNo � public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("image-classification", model="amunchet/rorshark-vit-base")
# Run inference
result = pipe("Your input here")
print(result)����� Tags
transformerstensorboardsafetensorsvitimage-classificationvisiongenerated_from_trainerdataset:imagefolderbase_model:google/vit-base-patch16-224-in21kbase_model:finetune:google/vit-base-patch16-224-in21klicense:apache-2.0model-indexendpoints_compatibleregion:us
More Image Classification Models
See all →🚀 Use This Model
Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗�� Browse All Models🖼�� Task: Image Classification
This model is designed for the Image Classification task. Explore more models for this use case.
All Image Classification Models →📊 Popularity
⬇ Downloads662.8K
����� Community Likes3
🛠�� Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.