diff --git a/README.md b/README.md index d93e9317..26a15366 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ Download a pre-trained BEV vehicle segmentation model from here: [https://drive. | 32.07 | 33.03 | ### Evaluate a model -Evaluate the IOU of a model on the nuScenes validation set: +Evaluate the IOU of a model on the nuScenes validation set. To evaluate on the "mini" split, pass `mini`. To evaluate on the "trainval" split, pass `trainval`. ``` python main.py eval_model_iou mini/trainval --modelf=MODEL_LOCATION --dataroot=NUSCENES_ROOT diff --git a/src/explore.py b/src/explore.py index dbc1e5fa..51dec064 100644 --- a/src/explore.py +++ b/src/explore.py @@ -111,7 +111,7 @@ def lidar_check(version, ax = plt.subplot(gs[:, 4:5]) plt.imshow(binimgs[si].squeeze(0).T, origin='lower', cmap='Greys', vmin=0, vmax=1) - imname = f'lcheck{epoch:03}_{si:05}_{batchi:02}.jpg' + imname = f'lcheck{epoch:03}_{batchi:05}_{si:02}.jpg' print('saving', imname) plt.savefig(imname) @@ -342,7 +342,7 @@ def viz_model_preds(version, ax = plt.subplot(gs[0, :]) ax.get_xaxis().set_ticks([]) ax.get_yaxis().set_ticks([]) - plt.setp(ax.spines.values(), color='#76b900', linewidth=2) + plt.setp(ax.spines.values(), color='b', linewidth=2) plt.legend(handles=[ mpatches.Patch(color=(0.0, 0.0, 1.0, 1.0), label='Output Vehicle Segmentation'), mpatches.Patch(color='#76b900', label='Ego Vehicle'),