CLIP-ViT-B/32 on the eight image classication tasks
Collection
if you find these models helpful, consider citing [our paper](https://arxiv.org/abs/2406.03280)
•
9 items
•
Updated
Model Card: tanganke/clip-vit-base-patch32_mnist
Model Details
- Architecture: ViT-Base with patch size 32
- Training Data: MNIST dataset
Training Details
Adam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32). Only the vision encoder is fine-tuned.
Evaluation Results
- pre-trained: 0.4759327471256256
- fine-tuned: 0.9957262277603149
Usage
load vision model
from transformers import CLIPVisionModel
vision_model = CLIPVisionModel.from_pretrained('tanganke/clip-vit-base-patch32_mnist')
substitute the vision encoder of clip
from transformers import CLIPModel
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_model.vision_model.load_state_dict(vision_model.vision_model.state_dict())
- Downloads last month
- 1,166
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for tanganke/clip-vit-base-patch32_mnist
Base model
openai/clip-vit-base-patch32