Vision Transformer: ViT redefine how images are
analyzed and understood by leveraging the principles of
transformers. The primary strength of ViT lies in its ability to
process images at a granular pixelwise level while simultaneously maintaining awareness of the entire image spatial-global
context. Hence, this model excels in extracting a deeper
understanding of image features, highlighting its superiority in
handling complex image analysis tasks [33], [43].To process
2-D images from RGB camera and YOLOv8 segmentation,
an image represented as Q is transformed into ν sequences
of vectorized 2-D p patches in Rν×(p2c) with the convolution
layer of c channels. This restructuring yields a sequence well
suited for the transformer architecture. A learnable linear
transformation is applied to each patch vector to accommodate
the transformer requirement for uniform dimensionality across
its layers, converting it to a model-specific dimension dm.
These transformed vectors are recognized as patch embedding.
Beforehand, for the wrapped camera data, we have to change
the view to images in R(Z×B) ×c×w×h to feed to the network
for compatibility, where B denotes the batch size. Similarly,
the embeddings are produced through MHSA of the ViT and
fed into the blocks of MLP to produce the output ζ (Q) for
the input images Q while ζ (Qs) for the image segments
Qs of Q, respectively, as shown in Fig. 3. In Table III, we
outline the specifications of various ViT models, ranging from
the lightweight ViT-Tiny to the ViT-Huge. ViT-Tiny and
ViT-Small are distilled variants optimized for performance,
featuring reduced resource consumption and faster inference
speeds. The table compares ViT variants based on layers,
embedding dimension (dm), MLP size, attention heads, and
parameters, highlighting the scalability and efficiency of each
model. For our model, we employ the ViT-Small with a patch embedding size of 32 for the edge-device practicality while
preserving the performance of the learnable features.

above is the algo for beam prediction now i have a csv file which have the location to the images , I will provide you the format of csv file in that file you have beam index aslo i want you to write a code which predict the top 3 beans and find the accuracy of that top 3 beams and also find the DBA score use large Vit model 

index,unit1_rgb,unit1_pwr_60ghz,unit1_lidar,unit1_radar,unit1_loc,unit2_loc,unit1_beam,unit1_max_pwr,time_stamp,seq_index,unit2_spd_over_grnd_kmph,unit2_num_sats,unit2_altitude,unit2_geo_sep,unit2_mode_fix_type,unit2_pdop,unit2_hdop,unit2_vdop,unit2_interpolated_position
1,./unit1/camera_data/image_1.jpg,./unit1/mmWave_data/mmWave_power_1.txt,./unit1/lidar_data/lidar_data_1.ply,./unit1/radar_data/radar_data_1.npy,./unit1/GPS_data/gps_location.txt,./unit2/GPS_data/GPS_location_1.txt,3,0.174674109,01:22:30-405771,1,5.82,12.0,356.81,-27.749,3.0,1.09,0.58,0.92,0.0
2,./unit1/camera_data/image_2.jpg,./unit1/mmWave_data/mmWave_power_2.txt,./unit1/lidar_data/lidar_data_2.ply,./unit1/radar_data/radar_data_2.npy,./unit1/GPS_data/gps_location.txt,./unit2/GPS_data/GPS_location_2.txt,3,0.254460901,01:22:30-497819,1,,,,,,,,,1.0
3,./unit1/camera_data/image_3.jpg,./unit1/mmWave_data/mmWave_power_3.txt,./unit1/lidar_data/lidar_data_3.ply,./unit1/radar_data/radar_data_3.npy,./unit1/GPS_data/gps_location.txt,./unit2/GPS_data/GPS_location_3.txt,3,0.189701036,01:22:30-592039,1,5.862,12.0,356.814,-27.749,3.0,1.09,0.58,0.92,0.0
above is the csv file

rember to give a google colab code