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SpydazWeb_VisonEncoderDecoder_Project

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SpydazWeb_VisonEncoderDecoder_Project / README.md
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metadata
library_name: transformers
license: apache-2.0
language:
  - en

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Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Model Sources [optional]

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Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model 

from transformers import AutoProcessor, VisionEncoderDecoderModel
import requests
from PIL import Image
import torch

processor = AutoProcessor.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
model = VisionEncoderDecoderModel.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")

# load image from the IAM dataset
url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

# training
model.config.decoder_start_token_id = processor.tokenizer.eos_token_id
model.config.pad_token_id = processor.tokenizer.pad_token_id
model.config.vocab_size = model.config.decoder.vocab_size

pixel_values = processor(image, return_tensors="pt").pixel_values
text = "hello world"
labels = processor.tokenizer(text, return_tensors="pt").input_ids
outputs = model(pixel_values=pixel_values, labels=labels)
loss = outputs.loss

# inference (generation)
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

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Training Details 

from transformers import ViTImageProcessor, AutoTokenizer, VisionEncoderDecoderModel
from datasets import load_dataset

image_processor = ViTImageProcessor.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Cyber_Q_Vision")
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
    "LeroyDyer/Mixtral_AI_Cyber_Q_Vision", "LeroyDyer/Mixtral_AI_Cyber_Q_Vision"
)

model.config.decoder_start_token_id = tokenizer.cls_token_id
model.config.pad_token_id = tokenizer.pad_token_id

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
pixel_values = image_processor(image, return_tensors="pt").pixel_values

labels = tokenizer(
    "an image of two cats chilling on a couch",
    return_tensors="pt",
).input_ids

# the forward function automatically creates the correct decoder_input_ids
loss = model(pixel_values=pixel_values, labels=labels).loss

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective 


from transformers import MistralConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel

# Initializing a ViT & Mistral style configuration
config_encoder = ViTConfig()
config_decoder = MistralConfig()

config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

# Initializing a ViTBert model (with random weights) from a ViT & Mistral style configurations
model = VisionEncoderDecoderModel(config=config)

# Accessing the model configuration
config_encoder = model.config.encoder
config_decoder = model.config.decoder
# set decoder config to causal lm
config_decoder.is_decoder = True
config_decoder.add_cross_attention = True

# Saving the model, including its configuration
model.save_pretrained("my-model")

# loading model and config from pretrained folder
encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model")
model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)

Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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