fix subplots plotting

yolov5.py

@@ -211,7 +211,8 @@ def dff_nmf(image, target_lyr, n_components):
         scores = scores * objectness  # Adjust scores by objectness
         boxes = output1[..., :4]  # First 4 values are x1, y1, x2, y2
         boxes = boxes[confidence_mask]  # Filter boxes by confidence mask
-        fig, ax = plt.subplots(1, figsize=(10, 10))
+        fig, ax = plt.subplots(1, figsize=(8, 8))
+        ax.axis("off")
         ax.imshow(torch.tensor(batch_explanations[0][indx]).cpu().numpy(), cmap="RdYlGn")  # Display image
         top_score_idx = scores.argmax(dim=0)  # Get the index of the max score
         top_score = scores[top_score_idx].item()
@@ -228,7 +229,7 @@ def dff_nmf(image, target_lyr, n_components):
         predicted_label = labels[top_class_id]  # Map ID to label
         ax.text(x1, y1, f"{predicted_label}: {top_score:.2f}",
             color='r', fontsize=12, verticalalignment='top')
-       
+        plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
 
         fig.canvas.draw()  # Draw the canvas to make sure the image is rendered
         image_array = np.array(fig.canvas.renderer.buffer_rgba())  # Convert to numpy array