With the growth of the internet, the use of social media based on images has drastically increased like Twitter, Instagram, etc. In these social media, women have a very high contribution as of 75% women use social media multiple times compared to men which is only 65% of men uses social media multiple times a day. However, with this much contribution, it also increases systematic inequality and discrimination offline is replicated in online spaces in the form of MEMEs. A meme is essentially an image characterized by pictorial content with an overlaying text a posteriori introduced by humans, with the main goal of being funny and/or ironic. Although most of them are created with the intent of making funny jokes, in a short time people started to use them as a form of hate and prejudice against women, landing to sexist and aggressive messages in online environments that subsequently amplify the sexual stereotyping and gender inequality of the offline world. This leads to the need for automatic detection of Misogyny MEMEs. Specifically, I described the model submitted for the shared task on Multimedia Automatic Misogyny Identification (MAMI) and my team name is IIT DHANBAD CODECHAMPS.