yolos-tiny

ModelFree

object-detection model by undefined. 96,175 downloads.

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

vision transformer-based object detection with attention-weighted region proposals

Medium confidence

Detects objects in images using a Vision Transformer (ViT) backbone that processes images as sequences of patches, combined with learnable object queries that attend to relevant image regions. Unlike CNN-based detectors (YOLO, Faster R-CNN), YOLOS uses pure transformer self-attention to identify and localize objects, enabling it to capture long-range spatial dependencies and learn object relationships directly from patch embeddings without hand-crafted region proposal networks.

Solves for

Detect and localize multiple object classes in images with transformer-based attention mechanismsLeverage vision transformers for object detection tasks where capturing global context is importantRun lightweight object detection on resource-constrained devices using the tiny variant

Best for

Computer vision engineers building detection pipelines that prioritize architectural simplicity over CNN inductive biases

Researchers experimenting with transformer-based detection alternatives to traditional CNN detectors

Edge deployment scenarios requiring sub-100M parameter models with reasonable accuracy-latency tradeoffs

Requires

PyTorch 1.9+ or TensorFlow 2.6+

transformers library 4.10.0+

PIL/Pillow for image preprocessing

Limitations

Inference latency ~50-100ms per image on CPU (slower than optimized YOLO variants due to transformer overhead)

Requires fixed input resolution (typically 512x512); variable-size inputs need padding/resizing preprocessing

Smaller model capacity (tiny variant ~5.4M parameters) trades accuracy for speed compared to larger ViT backbones

What makes it unique

Applies pure transformer architecture (DETR-style with learnable object queries) to object detection instead of CNN backbones, enabling attention-based spatial reasoning without region proposal networks; tiny variant achieves 5.4M parameters through aggressive model compression while maintaining COCO detection capability

vs alternatives

Simpler architecture than Faster R-CNN (no RPN) and more parameter-efficient than standard ViT detectors, but slower inference than optimized YOLO v5/v8 on edge devices due to transformer computational overhead

coco-pretrained multi-class object detection with 80 object categories

Medium confidence

Detects 80 object classes from the COCO dataset (people, vehicles, animals, furniture, etc.) using weights pretrained on 118K training images. The model outputs bounding box coordinates and class probabilities for each detected object, with confidence thresholds typically set at 0.5 for filtering low-confidence predictions. Inference uses the pretrained checkpoint directly without requiring fine-tuning for standard COCO classes.

Solves for

Detect common objects (people, cars, dogs, chairs, etc.) in images without custom trainingUse pretrained weights as a foundation for transfer learning to custom object classesEvaluate detection performance on COCO benchmark tasks

Best for

Developers building object detection features for consumer applications (autonomous vehicles, surveillance, robotics)

Teams needing zero-shot detection of common objects without dataset collection or model training

Researchers comparing transformer-based detection against CNN baselines on COCO metrics

Requires

Pretrained model checkpoint (automatically downloaded from HuggingFace Hub, ~22MB for tiny variant)

transformers library with YOLOS model class

COCO class label mapping (typically 80 classes, provided in model config)

Limitations

Limited to 80 COCO classes; detecting objects outside this taxonomy requires fine-tuning

Performance degrades on domain-specific images (medical imaging, satellite imagery, specialized industrial equipment)

No built-in class weighting for imbalanced detection scenarios (e.g., rare object classes in COCO)

What makes it unique

Leverages COCO pretraining with transformer architecture, enabling detection of 80 common object classes without custom training while maintaining parameter efficiency through the tiny variant design

vs alternatives

Requires no dataset collection or fine-tuning for COCO classes (vs YOLOv5 which also supports COCO but with larger model sizes), though accuracy is typically 2-5% lower than larger transformer detectors due to model compression

batch inference with dynamic batching and mixed-precision acceleration

Medium confidence

Processes multiple images simultaneously using PyTorch's batching mechanism, with optional mixed-precision (FP16) inference to reduce memory footprint and accelerate computation on NVIDIA GPUs. The model accepts batched tensor inputs and returns batched outputs, enabling efficient throughput for processing image collections. Automatic mixed precision (AMP) reduces model size by ~50% in memory while maintaining accuracy through selective FP16 quantization.

Solves for

Process image collections (100s-1000s of images) efficiently with reduced latency per imageDeploy detection on memory-constrained devices by enabling mixed-precision inferenceMaximize GPU utilization by batching multiple images per forward pass

Best for

Production systems processing image streams or batch jobs (security footage analysis, photo library scanning)

Edge devices with limited VRAM (mobile GPUs, Jetson Nano, embedded systems)

Cost-sensitive cloud deployments where reducing inference time directly reduces billing

Requires

PyTorch 1.9+ with CUDA support for GPU acceleration

NVIDIA GPU with compute capability 7.0+ for FP16 support (optional, falls back to FP32 on CPU)

torch.cuda.amp module for automatic mixed precision context manager

Limitations

Batch size is limited by available GPU/CPU memory; typical max batch size 8-32 depending on device

Mixed-precision inference may introduce numerical instability in rare edge cases (requires validation per use case)

Dynamic batching adds complexity to deployment; requires careful handling of variable-length outputs

What makes it unique

Integrates PyTorch's native batching with transformers library's mixed-precision support, enabling efficient multi-image inference without custom batching code; tiny model variant is optimized for batch processing on edge GPUs

vs alternatives

Simpler batching API than ONNX Runtime (no custom session management), but less optimized than TensorRT for production deployment at scale

model export to onnx and safetensors formats for cross-framework deployment

Medium confidence

Exports the YOLOS model to ONNX (Open Neural Network Exchange) format for inference on non-PyTorch runtimes (ONNX Runtime, TensorRT, CoreML), and to SafeTensors format for secure, efficient weight serialization. ONNX export converts the PyTorch computation graph to a framework-agnostic format with operator-level optimization, while SafeTensors provides a safer alternative to pickle-based weight storage with built-in integrity checking.

Solves for

Deploy the model in production environments using ONNX Runtime for faster inference than PyTorchRun detection on mobile/edge devices (iOS, Android, embedded Linux) via ONNX or CoreML conversionSafely load and verify model weights without pickle deserialization vulnerabilities

Best for

DevOps/MLOps engineers deploying models across heterogeneous inference stacks (cloud, edge, mobile)

Security-conscious teams avoiding pickle-based weight loading due to code execution risks

Mobile app developers integrating object detection without PyTorch runtime overhead

Requires

transformers library with ONNX export utilities

onnx and onnxruntime packages for validation and inference

safetensors library for SafeTensors format support

Limitations

ONNX export requires manual operator mapping for custom layers; standard YOLOS exports cleanly but custom modifications may fail

ONNX Runtime inference adds ~5-10% latency overhead vs native PyTorch on GPU due to graph interpretation

SafeTensors format is read-only after export; requires re-export to update weights

What makes it unique

Provides native ONNX export via transformers library (no custom conversion code needed) combined with SafeTensors weight serialization, enabling secure, framework-agnostic deployment without pickle deserialization

vs alternatives

Simpler export workflow than manual ONNX conversion (vs TensorFlow's tf2onnx), and safer than pickle-based PyTorch checkpoints, but requires additional optimization (quantization, graph simplification) for mobile deployment vs native TFLite models

fine-tuning on custom object detection datasets with transfer learning

Medium confidence

Enables transfer learning by unfreezing model layers and training on custom datasets with COCO-style annotations (bounding boxes + class labels). The pretrained COCO weights serve as initialization, reducing training time and data requirements compared to training from scratch. Fine-tuning uses standard PyTorch training loops with loss functions (Hungarian matching loss for DETR-style detectors) and gradient-based optimization.

Solves for

Adapt the model to detect custom object classes (e.g., specific product SKUs, industrial defects, medical conditions)Improve detection accuracy on domain-specific images (e.g., aerial imagery, medical scans) by fine-tuning on labeled dataBuild detection models with limited labeled data by leveraging COCO pretraining

Best for

Computer vision teams with 100-10K labeled images of custom objects

Researchers experimenting with transfer learning from COCO to specialized domains

Startups building domain-specific detection products without large annotation budgets

Requires

Custom dataset with COCO-format annotations (JSON with image metadata, bounding boxes, category IDs)

PyTorch 1.9+, transformers 4.10.0+, datasets library for data loading

GPU with 8GB+ VRAM for training (can use gradient accumulation for smaller GPUs)

Limitations

Requires COCO-format annotations (bounding boxes); no built-in support for polygon/segmentation masks

Fine-tuning on very small datasets (<100 images) risks overfitting; requires careful regularization and validation

Training time: ~2-8 hours on single GPU for 10K images (vs 24+ hours for training from scratch)

What makes it unique

Leverages DETR-style Hungarian matching loss for fine-tuning (vs traditional anchor-based losses in YOLO), enabling direct optimization of object queries without hand-crafted anchor design; tiny model variant reduces training memory requirements

vs alternatives

Simpler fine-tuning API than YOLOv5 (no anchor configuration), but requires more careful hyperparameter tuning than CNN-based detectors due to transformer training dynamics

confidence-based detection filtering and non-maximum suppression (nms)

Medium confidence

Filters detected objects by confidence threshold (default 0.5) to remove low-confidence predictions, then applies non-maximum suppression (NMS) to eliminate duplicate detections of the same object. NMS iteratively removes lower-confidence boxes that overlap significantly (IoU > threshold, typically 0.5) with higher-confidence boxes, reducing false positives from multiple overlapping predictions.

Solves for

Remove low-confidence predictions to reduce false positives in detection outputEliminate duplicate detections when the model predicts multiple overlapping boxes for the same objectTune detection sensitivity by adjusting confidence and NMS thresholds per application

Best for

Production detection pipelines requiring clean, non-redundant outputs

Applications with strict false-positive budgets (security, medical imaging)

Developers tuning detection sensitivity for specific use cases

Requires

Detection outputs with bounding boxes and confidence scores

torchvision.ops.nms or custom NMS implementation

Configurable confidence and IoU thresholds

Limitations

NMS is a greedy algorithm; may fail to separate closely-spaced objects (e.g., crowded scenes with overlapping people)

Fixed IoU threshold (typically 0.5) is suboptimal for objects of varying sizes; soft-NMS or class-specific thresholds require custom implementation

Confidence threshold is global; no per-class threshold tuning without post-processing

What makes it unique

Applies standard NMS post-processing to transformer-based detections (same as CNN detectors), with no architecture-specific optimizations; confidence threshold is applied uniformly across all 80 COCO classes

vs alternatives

Standard NMS implementation (no advantage vs YOLO), but can be enhanced with soft-NMS or class-specific thresholds for improved performance on specific datasets

inference on cpu with quantization support for resource-constrained environments

Medium confidence

Runs object detection on CPU without GPU acceleration, with optional 8-bit integer quantization (INT8) to reduce model size by ~75% and accelerate inference on CPU-only devices. Quantization maps floating-point weights to 8-bit integers, reducing memory bandwidth and enabling faster computation on CPUs without specialized hardware. Inference uses standard PyTorch CPU kernels or quantized inference engines (ONNX Runtime with QNN backend).

Solves for

Deploy detection on CPU-only devices (older servers, embedded systems, Raspberry Pi)Reduce model size from 22MB to ~6MB for deployment on bandwidth-constrained networksRun inference without GPU dependency for cost-sensitive or offline scenarios

Best for

Edge device developers (IoT, embedded systems, Raspberry Pi, Jetson Nano with CPU fallback)

Cost-conscious deployments avoiding GPU infrastructure

Offline/on-device inference scenarios without cloud connectivity

Requires

PyTorch CPU build (default installation)

Optional: ONNX Runtime with CPU providers for faster inference

Optional: quantization libraries (torch.quantization, ONNX Runtime QNN backend)

Limitations

CPU inference is 10-50x slower than GPU (50-100ms per image on modern CPU vs 5-10ms on GPU)

INT8 quantization reduces accuracy by 1-3% on COCO metrics; requires validation per use case

Quantization requires calibration on representative data; no pre-quantized checkpoints provided

What makes it unique

Supports both FP32 CPU inference (standard PyTorch) and INT8 quantization via torch.quantization, enabling flexible accuracy-latency tradeoffs; tiny model variant is optimized for CPU memory footprint

vs alternatives

Simpler quantization workflow than TensorFlow Lite (no custom conversion), but slower CPU inference than ONNX Runtime with optimized CPU providers

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with yolos-tiny, ranked by overlap. Discovered automatically through the match graph.

Model36

rtdetr_v2_r18vd

object-detection model by undefined. 1,10,212 downloads.

coco-pretrained multi-class object classification and localizationreal-time object detection with deformable transformer attentionbatch inference with dynamic input resolution

3 shared capabilities

Model36

rtdetr_r50vd_coco_o365

object-detection model by undefined. 86,670 downloads.

multi-dataset transfer learning with coco and objects365 pre-trainingreal-time object detection with transformer-based architecturebatch inference with dynamic input shape handling

3 shared capabilities

Model40

rtdetr_r18vd_coco_o365

object-detection model by undefined. 5,21,638 downloads.

multi-dataset transfer learning with coco and objects365 pre-trainingreal-time object detection with transformer-based architecturebatch inference with dynamic input resolution

3 shared capabilities

Model36

rtdetr_r101vd_coco_o365

object-detection model by undefined. 1,02,666 downloads.

multi-domain object detection with coco+objects365 pretrainingreal-time object detection with transformer-based architecture

2 shared capabilities

Model34

rtdetr_r50vd

object-detection model by undefined. 36,914 downloads.

real-time object detection with deformable transformer architecturecoco-pretrained weight initialization with transfer learning support

2 shared capabilities

Model37

detr-resnet-101

object-detection model by undefined. 51,631 downloads.

end-to-end transformer-based object detection with resnet-101 backbonetransformer encoder-decoder object prediction

2 shared capabilities

Best For

✓Computer vision engineers building detection pipelines that prioritize architectural simplicity over CNN inductive biases
✓Researchers experimenting with transformer-based detection alternatives to traditional CNN detectors
✓Edge deployment scenarios requiring sub-100M parameter models with reasonable accuracy-latency tradeoffs
✓Developers building object detection features for consumer applications (autonomous vehicles, surveillance, robotics)
✓Teams needing zero-shot detection of common objects without dataset collection or model training
✓Researchers comparing transformer-based detection against CNN baselines on COCO metrics
✓Production systems processing image streams or batch jobs (security footage analysis, photo library scanning)
✓Edge devices with limited VRAM (mobile GPUs, Jetson Nano, embedded systems)

Known Limitations

⚠Inference latency ~50-100ms per image on CPU (slower than optimized YOLO variants due to transformer overhead)
⚠Requires fixed input resolution (typically 512x512); variable-size inputs need padding/resizing preprocessing
⚠Smaller model capacity (tiny variant ~5.4M parameters) trades accuracy for speed compared to larger ViT backbones
⚠No native support for real-time video processing; requires frame-by-frame inference without temporal optimization
⚠Limited to 80 COCO classes; detecting objects outside this taxonomy requires fine-tuning
⚠Performance degrades on domain-specific images (medical imaging, satellite imagery, specialized industrial equipment)

Requirements

PyTorch 1.9+ or TensorFlow 2.6+transformers library 4.10.0+PIL/Pillow for image preprocessingCUDA 11.0+ for GPU acceleration (optional but recommended)Minimum 2GB RAM for model loading and inferencePretrained model checkpoint (automatically downloaded from HuggingFace Hub, ~22MB for tiny variant)transformers library with YOLOS model classCOCO class label mapping (typically 80 classes, provided in model config)

Input / Output

Accepts: image (PIL Image, numpy array, torch tensor), image formats: JPEG, PNG, BMP, WebP, batch inputs: multiple images as tensor with shape [batch_size, 3, height, width], image (RGB, 3-channel), image resolution: 512x512 (standard COCO training resolution), batched tensor: shape [batch_size, 3, 512, 512], batch_size: 1-32 depending on available memory, PyTorch model checkpoint (.pt, .pth, or HuggingFace Hub identifier), COCO-format JSON annotation file with structure: {images: [...], annotations: [...], categories: [...]}, detections: list of dicts with 'box' (4 coordinates), 'score' (float 0-1), 'label' (int), image (same as GPU inference)

Produces: structured data: bounding boxes (x_min, y_min, x_max, y_max or center_x, center_y, width, height), class predictions: integer class IDs with confidence scores (0.0-1.0), detection format: list of dicts with keys 'box', 'score', 'label' per image, structured data: list of detections per image, per-detection: bounding box coordinates, class ID (0-79), confidence score (float 0.0-1.0), batched detections: list of detection lists, one per image in batch, per-image: variable number of detections (0-N objects per image), ONNX model file (.onnx, ~22MB for tiny variant), SafeTensors weight file (.safetensors, ~22MB), ONNX graph: serialized computation graph with operator definitions, fine-tuned model checkpoint (.pt or SafeTensors format), training logs: loss curves, validation metrics (mAP, precision, recall), filtered detections: subset of input detections after confidence and NMS filtering, per-detection: same format as input (box, score, label), detections (same format as GPU inference, with 1-3% accuracy variance from quantization)

UnfragileRank

Adoption56%(40% weight)

Quality16%(20% weight)

Ecosystem50%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

7 capabilities

Visit yolos-tiny→

Model Details

huggingface

Provider

transformers

Architecture

96,175

Downloads

Tasks

object-detection

About

hustvl/yolos-tiny — a object-detection model on HuggingFace with 96,175 downloads

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Capabilities7 decomposed

vision transformer-based object detection with attention-weighted region proposals

Medium confidence

Solves for

Best for

Computer vision engineers building detection pipelines that prioritize architectural simplicity over CNN inductive biases

Researchers experimenting with transformer-based detection alternatives to traditional CNN detectors

Edge deployment scenarios requiring sub-100M parameter models with reasonable accuracy-latency tradeoffs

Requires

PyTorch 1.9+ or TensorFlow 2.6+

transformers library 4.10.0+

PIL/Pillow for image preprocessing

Limitations

Inference latency ~50-100ms per image on CPU (slower than optimized YOLO variants due to transformer overhead)

Requires fixed input resolution (typically 512x512); variable-size inputs need padding/resizing preprocessing

Smaller model capacity (tiny variant ~5.4M parameters) trades accuracy for speed compared to larger ViT backbones

What makes it unique

vs alternatives

coco-pretrained multi-class object detection with 80 object categories

Medium confidence

Solves for

Best for

Developers building object detection features for consumer applications (autonomous vehicles, surveillance, robotics)

Teams needing zero-shot detection of common objects without dataset collection or model training

Researchers comparing transformer-based detection against CNN baselines on COCO metrics

Requires

Pretrained model checkpoint (automatically downloaded from HuggingFace Hub, ~22MB for tiny variant)

transformers library with YOLOS model class

COCO class label mapping (typically 80 classes, provided in model config)

Limitations

Limited to 80 COCO classes; detecting objects outside this taxonomy requires fine-tuning

Performance degrades on domain-specific images (medical imaging, satellite imagery, specialized industrial equipment)

No built-in class weighting for imbalanced detection scenarios (e.g., rare object classes in COCO)

What makes it unique

Leverages COCO pretraining with transformer architecture, enabling detection of 80 common object classes without custom training while maintaining parameter efficiency through the tiny variant design

vs alternatives

batch inference with dynamic batching and mixed-precision acceleration

Medium confidence

Solves for

Best for

Production systems processing image streams or batch jobs (security footage analysis, photo library scanning)

Edge devices with limited VRAM (mobile GPUs, Jetson Nano, embedded systems)

Cost-sensitive cloud deployments where reducing inference time directly reduces billing

Requires

PyTorch 1.9+ with CUDA support for GPU acceleration

NVIDIA GPU with compute capability 7.0+ for FP16 support (optional, falls back to FP32 on CPU)

torch.cuda.amp module for automatic mixed precision context manager

Limitations

Batch size is limited by available GPU/CPU memory; typical max batch size 8-32 depending on device

Mixed-precision inference may introduce numerical instability in rare edge cases (requires validation per use case)

Dynamic batching adds complexity to deployment; requires careful handling of variable-length outputs

What makes it unique

vs alternatives

Simpler batching API than ONNX Runtime (no custom session management), but less optimized than TensorRT for production deployment at scale

model export to onnx and safetensors formats for cross-framework deployment

Medium confidence

Solves for

Best for

DevOps/MLOps engineers deploying models across heterogeneous inference stacks (cloud, edge, mobile)

Security-conscious teams avoiding pickle-based weight loading due to code execution risks

Mobile app developers integrating object detection without PyTorch runtime overhead

Requires

transformers library with ONNX export utilities

onnx and onnxruntime packages for validation and inference

safetensors library for SafeTensors format support

Limitations

ONNX export requires manual operator mapping for custom layers; standard YOLOS exports cleanly but custom modifications may fail

ONNX Runtime inference adds ~5-10% latency overhead vs native PyTorch on GPU due to graph interpretation

SafeTensors format is read-only after export; requires re-export to update weights

What makes it unique

vs alternatives

fine-tuning on custom object detection datasets with transfer learning

Medium confidence

Solves for

Best for

Computer vision teams with 100-10K labeled images of custom objects

Researchers experimenting with transfer learning from COCO to specialized domains

Startups building domain-specific detection products without large annotation budgets

Requires

Custom dataset with COCO-format annotations (JSON with image metadata, bounding boxes, category IDs)

PyTorch 1.9+, transformers 4.10.0+, datasets library for data loading

GPU with 8GB+ VRAM for training (can use gradient accumulation for smaller GPUs)

Limitations

Requires COCO-format annotations (bounding boxes); no built-in support for polygon/segmentation masks

Fine-tuning on very small datasets (<100 images) risks overfitting; requires careful regularization and validation

Training time: ~2-8 hours on single GPU for 10K images (vs 24+ hours for training from scratch)

What makes it unique

vs alternatives

Simpler fine-tuning API than YOLOv5 (no anchor configuration), but requires more careful hyperparameter tuning than CNN-based detectors due to transformer training dynamics

confidence-based detection filtering and non-maximum suppression (nms)

Medium confidence

Solves for

Best for

Production detection pipelines requiring clean, non-redundant outputs

Applications with strict false-positive budgets (security, medical imaging)

Developers tuning detection sensitivity for specific use cases

Requires

Detection outputs with bounding boxes and confidence scores

torchvision.ops.nms or custom NMS implementation

Configurable confidence and IoU thresholds

Limitations

NMS is a greedy algorithm; may fail to separate closely-spaced objects (e.g., crowded scenes with overlapping people)

Fixed IoU threshold (typically 0.5) is suboptimal for objects of varying sizes; soft-NMS or class-specific thresholds require custom implementation

Confidence threshold is global; no per-class threshold tuning without post-processing

What makes it unique

vs alternatives

Standard NMS implementation (no advantage vs YOLO), but can be enhanced with soft-NMS or class-specific thresholds for improved performance on specific datasets

inference on cpu with quantization support for resource-constrained environments

Medium confidence

Solves for

Best for

Edge device developers (IoT, embedded systems, Raspberry Pi, Jetson Nano with CPU fallback)

Cost-conscious deployments avoiding GPU infrastructure

Offline/on-device inference scenarios without cloud connectivity

Requires

PyTorch CPU build (default installation)

Optional: ONNX Runtime with CPU providers for faster inference

Optional: quantization libraries (torch.quantization, ONNX Runtime QNN backend)

Limitations

CPU inference is 10-50x slower than GPU (50-100ms per image on modern CPU vs 5-10ms on GPU)

INT8 quantization reduces accuracy by 1-3% on COCO metrics; requires validation per use case

Quantization requires calibration on representative data; no pre-quantized checkpoints provided

What makes it unique

vs alternatives

Simpler quantization workflow than TensorFlow Lite (no custom conversion), but slower CPU inference than ONNX Runtime with optimized CPU providers

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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yolos-tiny

Capabilities7 decomposed

vision transformer-based object detection with attention-weighted region proposals

coco-pretrained multi-class object detection with 80 object categories

batch inference with dynamic batching and mixed-precision acceleration

model export to onnx and safetensors formats for cross-framework deployment

fine-tuning on custom object detection datasets with transfer learning

confidence-based detection filtering and non-maximum suppression (nms)

inference on cpu with quantization support for resource-constrained environments

Related Artifactssharing capabilities

rtdetr_v2_r18vd

rtdetr_r50vd_coco_o365

rtdetr_r18vd_coco_o365

rtdetr_r101vd_coco_o365

rtdetr_r50vd

detr-resnet-101

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to yolos-tiny

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Data Sources

yolos-tiny

Capabilities7 decomposed

vision transformer-based object detection with attention-weighted region proposals

coco-pretrained multi-class object detection with 80 object categories

batch inference with dynamic batching and mixed-precision acceleration

model export to onnx and safetensors formats for cross-framework deployment

fine-tuning on custom object detection datasets with transfer learning

confidence-based detection filtering and non-maximum suppression (nms)

inference on cpu with quantization support for resource-constrained environments

Related Artifactssharing capabilities

rtdetr_v2_r18vd

rtdetr_r50vd_coco_o365

rtdetr_r18vd_coco_o365

rtdetr_r101vd_coco_o365

rtdetr_r50vd

detr-resnet-101

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to yolos-tiny

Are you the builder of yolos-tiny?

Get the weekly brief

Data Sources