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  1. README.md +44 -0
  2. config.json +23 -0
  3. model.safetensors +3 -0
README.md ADDED
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+ ---
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+ base_model:
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+ - openai/clip-vit-base-patch32
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+ datasets:
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+ - tanganke/eurosat
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+ metrics:
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+ - accuracy
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+ ---
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+
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+ # Model Card
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+
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+ ## Model Details
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+
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+ - Architecture: ViT-Base with patch size 32
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+ - Training Data: EuroSAT dataset
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+
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+ ## Training Details
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+
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+ Adam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32).
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+ Only the vision encoder is fine-tuned.
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+
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+ ## Evaluation Results
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+
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+ - pre-trained: 0.455623596906662
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+ - fine-tuned: 0.9906941652297974
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+
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+ ## Usage
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+
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+ load vision model
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+
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+ ```python
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+ from transformers import CLIPVisionModel
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+
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+ vision_model = CLIPVisionModel.from_pretrained('tanganke/clip-vit-base-patch32_eurosat')
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+ ```
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+
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+ substitute the vision encoder of clip
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+
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+ ```python
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+ from transformers import CLIPModel
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+
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+ clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
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+ clip_model.vision_model.load_state_dict(vision_model.vision_model.state_dict())
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "results/clip-vit-base-patch32_eurosat/",
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+ "architectures": [
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+ "CLIPVisionModel"
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+ ],
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+ "attention_dropout": 0.0,
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+ "dropout": 0.0,
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+ "hidden_act": "quick_gelu",
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+ "hidden_size": 768,
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+ "image_size": 224,
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+ "initializer_factor": 1.0,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-05,
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+ "model_type": "clip_vision_model",
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+ "num_attention_heads": 12,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 32,
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+ "projection_dim": 512,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.39.1"
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+ }
model.safetensors ADDED
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+ size 349847824