Upload DINO pre-trained ViT-Small model

README.md

@@ -1,43 +1,43 @@
-
-# DINO ViT-Small Custom Dataset
-
-This model is a Vision Transformer (ViT) Small model trained using DINO (self-DIstillation with NO labels) on a custom dataset.
-
-## Model Details
-
-- **Architecture**: ViT-Small (patch size 16)
-- **Pre-training Method**: DINO
-- **Training Epochs**: 2
-- **Output Dimension**: 384
-- **Dataset Size**: ~3000 images
-- **Base Model**: WinKawaks/vit-small-patch16-224
-
-## Training Configuration
-
-- Batch Size: 32
-- Learning Rate: 0.0003
-- Teacher Temperature: 0.07
-- Local Crops: 4
-- Weight Decay: 0.04 → 0.4
-- Optimizer: adamw
-
-## Training Results
-
-- Final Loss: 5.9383
-- Training Time: 0:03:55
-
-## Usage
-
-```python
-from transformers import ViTModel
-import torch
-
-# Load the model
-model = ViTModel.from_pretrained("odinson/dino-vit-small-custom")
-
-# Use for feature extraction
-model.eval()
-with torch.no_grad():
-    features = model(images).last_hidden_state
-Training Curves
-See the training plots in the repository for loss, learning rate, and weight decay curves.
+
+# DINO ViT-Small Custom Dataset
+
+This model is a Vision Transformer (ViT) Small model trained using DINO (self-DIstillation with NO labels) on a custom dataset.
+
+## Model Details
+
+- **Architecture**: ViT-Small (patch size 16)
+- **Pre-training Method**: DINO
+- **Training Epochs**: 10
+- **Output Dimension**: 384
+- **Dataset Size**: ~3000 images
+- **Base Model**: WinKawaks/vit-small-patch16-224
+
+## Training Configuration
+
+- Batch Size: 32
+- Learning Rate: 0.0003
+- Teacher Temperature: 0.07
+- Local Crops: 4
+- Weight Decay: 0.04 → 0.4
+- Optimizer: adamw
+
+## Training Results
+
+- Final Loss: 5.8926
+- Training Time: 0:06:19
+
+## Usage
+
+```python
+from transformers import ViTModel
+import torch
+
+# Load the model
+model = ViTModel.from_pretrained("odinson/dino-vit-small-custom")
+
+# Use for feature extraction
+model.eval()
+with torch.no_grad():
+    features = model(images).last_hidden_state
+Training Curves
+See the training plots in the repository for loss, learning rate, and weight decay curves.

model.safetensors

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