use model locally

app.py

@@ -14,10 +14,8 @@ load_dotenv()
 
 # Init similarity search AI model and processor
 torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-dino_v2_model = AutoModel.from_pretrained(
-    "facebook/dinov2-base", force_download=True
-).to(torch_device)
-dino_v2_image_processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
+dino_v2_model = AutoModel.from_pretrained("./dinov2-base").to(torch_device)
+dino_v2_image_processor = AutoImageProcessor.from_pretrained("./dinov2-base")
 
 # MongoDB
 MONGO_URI = os.environ.get("MONGO_URI")

dinov2-base/config.json

@@ -0,0 +1,48 @@
+{
+  "_name_or_path": "facebook/dinov2-base",
+  "apply_layernorm": true,
+  "architectures": [
+    "Dinov2Model"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "drop_path_rate": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "image_size": 518,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-06,
+  "layerscale_value": 1.0,
+  "mlp_ratio": 4,
+  "model_type": "dinov2",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "out_features": [
+    "stage12"
+  ],
+  "out_indices": [
+    12
+  ],
+  "patch_size": 14,
+  "qkv_bias": true,
+  "reshape_hidden_states": true,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4",
+    "stage5",
+    "stage6",
+    "stage7",
+    "stage8",
+    "stage9",
+    "stage10",
+    "stage11",
+    "stage12"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.0",
+  "use_swiglu_ffn": false
+}

dinov2-base/preprocessor_config.json

@@ -0,0 +1,27 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "BitImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 256
+  }
+}