[Vision sdk] ObjectDetection Notebook: update url, remove redundant p…

…arams (#2538)

* update base_url to have forward slashes

* Remove redundant custom_pipeline_component_args from od notebook

* Revert "Remove redundant custom_pipeline_component_args from od notebook"

This reverts commit 8ba4de3.

* remove extra custom_pipeline_component_args variable

sdk/python/foundation-models/system/finetune/image-object-detection/mmdetection-fridgeobjects-object-detection.ipynb

@@ -450,7 +450,7 @@
    "outputs": [],
    "source": [
     "# Generate jsonl file from coco file\n",
-    "base_url = os.path.join(uri_folder_data_asset.path, \"images\", '')\n",
+    "base_url = uri_folder_data_asset.path + \"/images/\"\n",
     "\n",
     "!python coco2jsonl.py \\\n",
     "--input_coco_file_path \"./odFridgeObjects_coco.json\" \\\n",
@@ -633,62 +633,6 @@
     "print(f\"Finetuning model {use_model_name}\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# One can provide values to training args specified in pipeline component as a dictionary as shown below.\n",
-    "# In this case, user specified values will be respected.\n",
-    "\n",
-    "custom_pipeline_component_args = {\n",
-    "    # # Model import args\n",
-    "    \"model_family\": \"MmDetectionImage\",\n",
-    "    \"mlflow_model\": foundation_model,\n",
-    "    # \"model_name\": mmdetection_model_name, # specify the model_name instead of mlflow_model if you want to use a model from the mmdetection model zoo\n",
-    "\n",
-    "    # Finetune args\n",
-    "    \"task_name\": \"image-object-detection\",\n",
-    "    \"image_min_size\": -1,\n",
-    "    \"image_max_size\": -1,\n",
-    "    \"metric_for_best_model\": \"mean_average_precision\",\n",
-    "    \"apply_augmentations\": True,\n",
-    "    \"number_of_workers\": 8,\n",
-    "    \"apply_deepspeed\": False,\n",
-    "    \"deepspeed_config\": deepspeed_config_path,\n",
-    "    \"apply_ort\": False,\n",
-    "    \"number_of_epochs\": 15,\n",
-    "    \"max_steps\": -1,\n",
-    "    \"training_batch_size\": 4,\n",
-    "    \"validation_batch_size\": 4,\n",
-    "    \"auto_find_batch_size\": False,\n",
-    "    \"learning_rate\": 5e-5,\n",
-    "    \"learning_rate_scheduler\": \"warmup_linear\",\n",
-    "    \"warmup_steps\": 0,\n",
-    "    \"optimizer\": \"adamw_hf\",\n",
-    "    \"weight_decay\": 0.0,\n",
-    "    \"extra_optim_args\": \"\",\n",
-    "    \"gradient_accumulation_step\": 1,\n",
-    "    \"precision\": \"32\",\n",
-    "    \"iou_threshold\": 0.5,\n",
-    "    \"box_score_threshold\": 0.3,\n",
-    "    \"random_seed\": 42,\n",
-    "    \"evaluation_strategy\": \"epoch\",\n",
-    "    \"evaluation_steps\": 500,\n",
-    "    \"logging_strategy\": \"epoch\",\n",
-    "    \"logging_steps\": 500,\n",
-    "    \"save_strategy\": \"epoch\",\n",
-    "    \"save_steps\": 500,\n",
-    "    \"save_total_limit\": -1,\n",
-    "    \"early_stopping\": False,\n",
-    "    \"early_stopping_patience\": 1,\n",
-    "    \"max_grad_norm\": 1.0,\n",
-    "    \"resume_from_checkpoint\": False,\n",
-    "    \"save_as_mlflow_model\": True,\n",
-    "}"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",