Upload 8 files

.gitattributes

@@ -49,3 +49,4 @@ b.mp4 filter=lfs diff=lfs merge=lfs -text
 cut_a_2.mp4 filter=lfs diff=lfs merge=lfs -text
 cut_b_1.mp4 filter=lfs diff=lfs merge=lfs -text
 tresa.mp4 filter=lfs diff=lfs merge=lfs -text
+Img-1.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,44 @@
----
-title: S15 YOLOV9
-emoji: 🏢
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 4.31.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Inference of Vehicle detection using Yolov9
+
+- This application showcases the inference capabilities of a Yolo v9 trained on the vehicle dataset from kaggle. 
+  [Vehicle Dataset Repo Link](https://www.kaggle.com/datasets/nadinpethiyagoda/vehicle-dataset-for-yolo)
+- The model is trained on 6 classes:
+  - car
+  - threewheel
+  - bus
+  - truck
+  - motorbike
+  - van
+- The architecture is based on Yolo v9 papar https://arxiv.org/abs/2402.13616 and model is 
+  trained using  https://github.com/WongKinYiu/yolov9.git
+- detect.py file used for inference.
+- From gradio applicaiton call is made to detect.py using command line shell with unique folder name passed as argument
+- After processing, image/video is picked from same location. 
+
+Mentioned below is the link for Training Repository [Training Repo Link](https://github.com/Shivdutta/ERA2-Session15-Yolov9)
+
+- Post training process, the model is saved locally and then uploaded to Gradio Spaces.
+- Attached below is the link to [download model file](https://huggingface.co/spaces/Shivdutta/S15-YOLOV9/blob/main/yolov9/runs/train/exp/weights/best.pt)
+
+-  This app has two features :
+
+  - **Video Prediction:** 
+"   - This feature will allow detection of moving vehicles in the the video
+  - **Image Prediction:**
+    - This feature will allow detection of vehicle in the the image
+  
+## Usage:
+
+  - **Video Prediction:** 
+"   - Upload video file and detect vehicles present in the video.
+    - Inferencing is done using CPU therefore it takes more time.
+  - **Image Prediction:**
+    - Upload image file and detect vehicles present in the image.
+
+![Prediction Output](Img-1.png)
+
+![Prediction Output](Img-2.png)
+
+![Prediction Output](Img-4.png)
+Thank you
+

app.py

@@ -25,8 +25,8 @@ with gr.Blocks() as demo:
     with gr.Tab("Video"):
         gr.Markdown(
             """
-            Upload image file and detect vehicles present in the image.
-            Video files take some time for prediction.
+            Upload video file and detect vehicles present in the video.
+            Inferencing is done using CPU therefore it takes more time.
             """
         )
         with gr.Row():
@@ -36,11 +36,10 @@ with gr.Blocks() as demo:
         gr.Markdown("## Examples")
 
         with gr.Row(): 
-            gr.Examples([ 'cut_a_2.mp4',
-                            'cut_b_1.mp4',
-                            'tresa.mp4'
-                         ], 
-                        inputs=img_input, fn=inference)
+            gr.Examples([ 
+                'cut_a_2.mp4',
+                'cut_b_1.mp4','tresa.mp4'], 
+                inputs=img_input, fn=inference)
         
             image_button = gr.Button("Predict")
             image_button.click(inference, inputs=img_input, outputs=pred_outputs)