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
Model Details
- Architecture: ViT-Base with patch size 32
- Training Data: EuroSAT 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: 0.455623596906662
- fine-tuned: 0.9906941652297974
Usage
load vision model
from transformers import CLIPVisionModel
vision_model = CLIPVisionModel.from_pretrained('tanganke/clip-vit-base-patch32_eurosat')
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
- 3,066
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_eurosat
Base model
openai/clip-vit-base-patch32