segformer_b2_clothes
Maintainer: mattmdjaga
239
🌐
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model Overview
The segformer_b2_clothes model is a Segformer B2 model fine-tuned on the ATR dataset for clothes segmentation by maintainer mattmdjaga. It can also be used for human segmentation. The model was trained on the "mattmdjaga/human_parsing_dataset" dataset.
The Segformer architecture combines a vision transformer with a segmentation head, allowing the model to learn global and local features for effective image segmentation. This fine-tuned version focuses on accurately segmenting clothes and human parts in images.
Model Inputs and Outputs
Inputs
- Images of people or scenes containing people
- The model takes the image as input and returns segmentation logits
Outputs
- Segmentation masks identifying various parts of the human body and clothing
- The model outputs a tensor of logits, which can be post-processed to obtain the final segmentation map
Capabilities
The segformer_b2_clothes model is capable of accurately segmenting clothes and human body parts in images. It can identify 18 different classes, including hats, hair, sunglasses, upper-clothes, skirts, pants, dresses, shoes, face, legs, arms, bags, and scarves.
The model achieves high performance, with a mean IoU of 0.69 and mean accuracy of 0.80 on the test set. It particularly excels at segmenting background, pants, face, and legs.
What Can I Use it For?
This model can be useful for a variety of applications involving human segmentation and clothing analysis, such as:
- Fashion and retail applications, to automatically detect and extract clothing items from images
- Virtual try-on and augmented reality experiences, by accurately segmenting the human body and clothing
- Semantic understanding of scenes with people, for applications like video surveillance or human-computer interaction
- Data annotation and dataset creation, by automating the labeling of human body parts and clothing
The maintainer has also provided the training code, which can be fine-tuned further on custom datasets for specialized use cases.
Things to Try
One interesting aspect of this model is its ability to segment a wide range of clothing and body parts. Try experimenting with different types of images, such as full-body shots, close-ups, or images with multiple people, to see how the model performs.
You can also try incorporating the segmentation outputs into downstream applications, such as virtual clothing try-on or fashion recommendation systems. The detailed segmentation masks can provide valuable information about the person's appearance and clothing.
Additionally, the maintainer has mentioned plans to release a colab notebook and a blog post to make the model more user-friendly. Keep an eye out for these resources, as they may provide further insights and guidance on using the segformer_b2_clothes model effectively.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🖼️
segformer-b0-finetuned-ade-512-512
119
The segformer-b0-finetuned-ade-512-512 model is a version of the SegFormer model fine-tuned on the ADE20k dataset for semantic segmentation. SegFormer is a convolutional neural network architecture that uses a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve strong results on semantic segmentation benchmarks. This particular model was pre-trained on ImageNet-1k and then fine-tuned on the ADE20k dataset at a resolution of 512x512. The SegFormer architecture is similar to the Vision Transformer (ViT) in that it treats an image as a sequence of patches and uses a Transformer encoder to process them. However, SegFormer uses a more efficient hierarchical design and a lightweight decode head, making it simpler and faster than traditional semantic segmentation models. The segformer-b2-clothes model is another example of a SegFormer variant fine-tuned for a specific task, in this case clothes segmentation. Model inputs and outputs Inputs Images**: The model takes in images as its input, which are split into a sequence of fixed-size patches that are then linearly embedded and processed by the Transformer encoder. Outputs Segmentation maps**: The model outputs a segmentation map, where each pixel is assigned a class label corresponding to the semantic category it belongs to (e.g., person, car, building, etc.). The resolution of the output segmentation map is lower than the input image resolution, typically by a factor of 4. Capabilities The segformer-b0-finetuned-ade-512-512 model is capable of performing semantic segmentation, which is the task of assigning a semantic label to each pixel in an image. It can accurately identify and delineate the various objects, scenes, and regions present in an image. This makes it useful for applications like autonomous driving, scene understanding, and image editing. What can I use it for? This SegFormer model can be used for a variety of semantic segmentation tasks, such as: Autonomous Driving**: Identify and segment different objects on the road (cars, pedestrians, traffic signs, etc.) to enable self-driving capabilities. Scene Understanding**: Understand the composition of a scene by segmenting it into different semantic regions (sky, buildings, vegetation, etc.), which can be useful for applications like robotics and augmented reality. Image Editing**: Perform precise segmentation of objects in an image, allowing for selective editing, masking, and manipulation of specific elements. The model hub provides access to a range of SegFormer models fine-tuned on different datasets, so you can explore options that best suit your specific use case. Things to try One interesting aspect of the SegFormer architecture is its hierarchical Transformer encoder, which allows it to capture features at multiple scales. This enables the model to understand the context and relationships between different semantic elements in an image, leading to more accurate and detailed segmentation. To see this in action, you could try using the segformer-b0-finetuned-ade-512-512 model on a diverse set of images, ranging from indoor scenes to outdoor landscapes. Observe how the model is able to segment the various objects, textures, and regions in the images, and how the segmentation maps evolve as you move up the hierarchy of the Transformer encoder.
Updated Invalid Date
📶
fashion-clip
139
The fashion-clip model is a CLIP-based model developed by maintainer patrickjohncyh to produce general product representations for fashion concepts. Leveraging the pre-trained checkpoint (ViT-B/32) released by OpenAI, the model was trained on a large, high-quality novel fashion dataset to study whether domain-specific fine-tuning of CLIP-like models is sufficient to produce product representations that are zero-shot transferable to entirely new datasets and tasks. The model was further fine-tuned on the laion/CLIP-ViT-B-32-laion2B-s34B-b79K checkpoint, which the maintainer found worked better than the original OpenAI CLIP on fashion tasks. This updated "FashionCLIP 2.0" model achieves higher performance across several fashion-related benchmarks compared to the original OpenAI CLIP and the initial FashionCLIP model. Model inputs and outputs Inputs Images**: The fashion-clip model takes images as input to generate product representations. Text**: The model can also accept text prompts, which are used to guide the representation learning. Outputs Image Embeddings**: The primary output of the fashion-clip model is a vector representation (embedding) of the input image, which can be used for tasks like image retrieval, zero-shot classification, and downstream fine-tuning. Capabilities The fashion-clip model is capable of producing general product representations that can be used for a variety of fashion-related tasks in a zero-shot manner. The model's performance has been evaluated on several benchmarks, including Fashion-MNIST, KAGL, and DEEP, where it outperforms the original OpenAI CLIP model and achieves state-of-the-art results on the updated "FashionCLIP 2.0" version. What can I use it for? The fashion-clip model can be used for a variety of fashion-related applications, such as: Image Retrieval**: The model's image embeddings can be used to perform efficient image retrieval, allowing users to find similar products based on visual similarity. Zero-Shot Classification**: The model can be used to classify fashion items into different categories without the need for task-specific fine-tuning, making it a powerful tool for applications that require flexible and adaptable classification capabilities. Downstream Fine-tuning**: The model's pre-trained representations can be used as a strong starting point for fine-tuning on more specific fashion tasks, such as product recommendation, attribute prediction, or outfit generation. Things to try One interesting aspect of the fashion-clip model is its ability to generate representations that are "zero-shot transferable" to new datasets and tasks. Researchers and developers could explore how well these representations generalize to fashion-related tasks beyond the benchmarks used in the initial evaluation, such as fashion trend analysis, clothing compatibility prediction, or virtual try-on applications. Additionally, the model's performance improvements when fine-tuned on the laion/CLIP-ViT-B-32-laion2B-s34B-b79K checkpoint suggest that further exploration of large-scale, domain-specific pretraining data could lead to even more capable fashion-oriented models. Experimenting with different fine-tuning strategies and data sources could yield valuable insights into the limits and potential of this approach.
Updated Invalid Date
📈
maskformer-swin-large-ade
53
maskformer-swin-large-ade is a semantic segmentation model created by Facebook. It is based on the MaskFormer architecture, which addresses instance, semantic and panoptic segmentation using the same approach - predicting a set of masks and corresponding labels. This model was trained on the ADE20k dataset and uses a Swin Transformer backbone. Model inputs and outputs The model takes an image as input and outputs class logits for each query as well as segmentation masks for each query. The image processor can be used to post-process the outputs into a final semantic segmentation map. Inputs Image Outputs Class logits for each predicted query Segmentation masks for each predicted query Capabilities maskformer-swin-large-ade excels at dense pixel-level segmentation, able to accurately identify and delineate individual objects and regions within an image. It can be used for tasks like scene understanding, autonomous driving, and medical image analysis. What can I use it for? You can use this model for semantic segmentation of natural scenes, as it was trained on the diverse ADE20k dataset. The predicted segmentation maps can provide detailed, pixel-level understanding of an image, which could be valuable for applications like autonomous navigation, image editing, and visual analysis. Things to try Try experimenting with the model on a variety of natural images to see how it performs. You can also fine-tune the model on a more specialized dataset to adapt it to a particular domain or task. The documentation provides helpful examples and resources for working with the MaskFormer architecture.
Updated Invalid Date
🏋️
yolos-fashionpedia
71
The yolos-fashionpedia model is a fine-tuned object detection model for fashion. It was developed by Valentina Feve and is based on the YOLOS architecture. The model was trained on the Fashionpedia dataset, which contains over 50,000 annotated fashion product images across 80 different categories. Similar models include yolos-tiny, a smaller YOLOS model fine-tuned on COCO, and adetailer, a suite of YOLOv8 detection models for various visual tasks like face, hand, and clothing detection. Model Inputs and Outputs Inputs Image data: The yolos-fashionpedia model takes in image data as input, and is designed to detect and classify fashion products in those images. Outputs Object detection: The model outputs bounding boxes around detected fashion items, along with their predicted class labels from the 80 categories in the Fashionpedia dataset. These include items like shirts, pants, dresses, accessories, and fine-grained details like collars, sleeves, and patterns. Capabilities The yolos-fashionpedia model excels at accurately detecting and categorizing a wide range of fashion products within images. This can be particularly useful for applications like e-commerce, virtual try-on, and visual search, where precise product identification is crucial. What Can I Use It For? The yolos-fashionpedia model can be leveraged in a variety of fashion-related applications: E-commerce product tagging**: Automatically tag and categorize product images on e-commerce platforms to improve search, recommendation, and visual browsing experiences. Virtual try-on**: Integrate the model into virtual fitting room technologies to accurately detect garment types and sizes. Visual search**: Enable fashion-focused visual search engines by allowing users to query using images of products they're interested in. Fashion analytics**: Analyze fashion trends, inventory, and consumer preferences by processing large datasets of fashion images. Things to Try One interesting aspect of the yolos-fashionpedia model is its ability to detect fine-grained fashion details like collars, sleeves, and patterns. Developers could experiment with using this capability to enable more advanced fashion-related features, such as: Generating detailed product descriptions from images Recommending complementary fashion items based on detected garment attributes Analyzing runway shows or street style to identify emerging trends By leveraging the model's detailed understanding of fashion elements, researchers and practitioners can create novel applications that go beyond basic product detection.
Updated Invalid Date